a study on intelligent tires based on ...tokyo institute of technology a study on intelligent tires...

TOKYO INSTITUTE OF TECHNOLOGY

A STUDY ON INTELLIGENT TIRES BASED ON

MEASUREMENT OF TIRE DEFORMATION

（タイヤ変形計測に基づいたインテリジェントタイヤに関する研究）

by

Ryosuke Matsuzaki

A thesis submitted to Tokyo Institute of Technology for the degree of Doctor of Engineering

Department of Mechanical Sciences and Engineering Tokyo Institute of Technology

2-12-1 O-okayama, Meguro, Tokyo 152-8552, Japan

February 2007

Abstract

Abstract From a traffic safety point-of-view, there is urgent need for intelligent tires as a warning system of road conditions, for optimized braking control on poor road surfaces and as a tire fault detection system. Intelligent tires, equipped with sensors for monitoring applied strain, are effective in improving reliability and control systems, such as anti-lock braking systems (ABSs). However, conventional strain gages, with their high stiffness and lead wires, are unsuitable for strain measurement in tires. This thesis examines the feasibility of a direct in-service strain measurement system for automobile tires. Three main technologies are investigated: sensing, wireless transmission and strain utilization. Two types of strain sensors, using changes in capacitance, are examined. The first is a patch-type sensor using a relatively soft material so that the sensor does not interfere with tire deformation and debonding is difficult. The second is a self-sensing method utilizing the tire structure itself as the sensor. This has the advantage that an attached sensor is not needed and debonding problems are eliminated. Measured capacitance changes are transmitted to a receiver, wirelessly, without batteries. The battery-less technology comprises a tuning circuit, external transmitter and external receiver. Since the tuning circuit acts as a frequency filter, the tuning frequency of the sensor can be wirelessly measured, without batteries to the sensor circuit. The proposed technologies are applied to radial automobile tires and compression tests performed. Experimental results demonstrate that the method is effective for passive, wireless strain monitoring. The thesis also demonstrates a utilization model of strain data for optimized braking control and road condition warning system. Relationships between strain sensor outputs and tire mechanical parameters, including braking torque, effective radius and contact patch length, are calculated using finite element analysis. Optimized braking control and road condition warning systems have been suggested based on strain data.

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Acknowledgements

Acknowledgements I would like to thank my supervisors, Professor Hideo Kobayashi and Professor Akira Todoroki, for invaluable guidance, technical knowledge and support during the study. I would also like to acknowledge Professor Endo, Professor Hagiwara, Professor Kishimoto, Professor Inoue, Professor Shimamura and Professor Iwasaki for their invaluable advice. Sincere thanks are also extended to my colleagues in the Todoroki Laboratory. This work was funded by Bridgestone Corporation, The Japan Science Society, Technology Licensing Organization in Tokyo Institute of Technology, Venture Business Laboratory in Tokyo Institute of Technology, and Japan Society for the Promotion of Science, and supported by Alps Electronic Co., Ltd.

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Table of contents

Table of contents

Chapter 1 Introduction.............................................................................................. 19 1.1 Motivation.................................................................................................. 19 1.2 Background................................................................................................ 21 1.3 Objectives .................................................................................................. 23 1.4 Outline of thesis ......................................................................................... 24

Chapter 2 Patch-type flexible sensor........................................................................ 27 2.1 Background................................................................................................ 27 2.2 Sensor outline ............................................................................................ 28 2.3 Wireless monitoring system....................................................................... 30 2.4 Finite element analysis............................................................................... 31 2.5 Experimental procedures ........................................................................... 33

2.5.1 Sensor fabrication ............................................................................. 33 2.5.2 Capacitance change measurements................................................... 34 2.5.3 Wireless strain measurements ........................................................... 34 2.5.4 Temperature effect measurements .................................................... 35

2.6 Results and discussion ............................................................................... 35 2.6.1 Capacitance changes ......................................................................... 35 2.6.2 Wireless strain measurements ........................................................... 36 2.6.3 Self-temperature compensation ........................................................ 37

2.7 Summary.................................................................................................... 37

Chapter 3 Self-sensing method for measuring tire deformation ........................... 54 3.1 Background................................................................................................ 54 3.2 Electronic circuit model............................................................................. 55 3.3 Track/bus tire specimen ............................................................................. 56

3.3.1 Experimental procedures .................................................................. 56 3.3.2 Results and discussion ...................................................................... 57

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Table of contents

3.4 Radial tire specimen................................................................................... 58 3.4.1 Experimental procedures .................................................................. 58 3.4.2 Capacitance changes ......................................................................... 59 3.4.3 Initial distance between electrodes ................................................... 60 3.4.4 Number of electrodes........................................................................ 61 3.4.5 Interdigital electrodes........................................................................ 61

3.5 Actual radial tire ........................................................................................ 62 3.5.1 Experimental procedures .................................................................. 62 3.5.2 Results and discussion ...................................................................... 62

3.6 Summary.................................................................................................... 63

Chapter 4 Active wireless monitoring using CR oscillator .................................... 82 4.1 Background................................................................................................ 82 4.2 Wireless monitoring system....................................................................... 82 4.3 Experimental procedures ........................................................................... 84

4.3.1 Specimens ......................................................................................... 84 4.3.2 Self-temperature compensation ........................................................ 84 4.3.3 Static and cyclic loading tests ........................................................... 85 4.3.4 Feasibility study for a commercially available tire........................... 85

4.4 Results and discussion ............................................................................... 86 4.4.1 Self-temperature compensation ........................................................ 86 4.4.2 Static and cyclic loading tests ........................................................... 87 4.4.3 Feasibility study for a commercially available tire........................... 87

4.5 Summary.................................................................................................... 88

Chapter 5 Passive wireless monitoring using electromagnetic induction ........... 100 5.1 Background.............................................................................................. 100 5.2 Wireless monitoring system..................................................................... 101 5.3 Experimental procedures ......................................................................... 103 5.4 Results and discussion ............................................................................. 103 5.5 Summary.................................................................................................. 105

Chapter 6 Passive wireless monitoring using tuning circuit .................................111 6.1 Background.............................................................................................. 111 6.2 Wireless monitoring system..................................................................... 112 6.3 Experimental procedures ......................................................................... 113

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Table of contents

6.3.1 Specimens ....................................................................................... 113 6.3.2 Static tension tests........................................................................... 114 6.3.3 Cyclic loading tests ......................................................................... 114

6.4 Results and discussion ............................................................................. 116 6.4.1 Static tension tests........................................................................... 116 6.4.2 Cyclic loading tests ......................................................................... 117

6.5 Application to an actual tire..................................................................... 117 6.5.1 Strain measurements using multiple spectral features .................... 117 6.5.2 Tuning frequency and peak power spectrum .................................. 118 6.5.3 Quality factor .................................................................................. 120 6.5.4 Response surface method................................................................ 122 6.5.5 Experimental procedures ................................................................ 123 6.5.6 Results and discussion .................................................................... 125

6.6 Summary.................................................................................................. 126

Chapter 7 Obtaining tire configurations and applied forces using deformation data ................................................................................................................. 139

7.1 Background.............................................................................................. 139 7.2 Obtaining tire configurations and applied forces..................................... 141

7.2.1 Finite element analysis.................................................................... 141 7.2.2 Contact patch length and effective radius ....................................... 143 7.2.3 Wheel loads..................................................................................... 144 7.2.4 Driving and braking torques ........................................................... 145

7.3 Summary.................................................................................................. 146

Chapter 8 Conclusions............................................................................................. 155

Appendix A Circuit diagrams .................................................................................... 161

References ................................................................................................................. 165

List of publications...................................................................................................... 174

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List of figures

List of figures

Figure 1.1: Tire failure/tread separation incidents occurring in the year 2000 due to faulty tire design and lower inflation pressure than recommended by the manufacturer. After [71]......................................................... 26

Figure 1.2: Tire pressure monitoring systems with valve-attached pressure sensors. (a) Bridgestone tire pressure sensor. (b) Alps Electric TMPS central unit with a Schrader sensor. After [19]. ..................... 26

Figure 2.1: Flexible interdigital capacitive sensor of length ls and width ws: (a) lateral comb (n = 5); (b) transverse comb (n = 5)............................. 39

Figure 2.2: Layout of interdigital capacitive sensor. ............................................. 40 Figure 2.3: Wireless transmitting circuit for flexible capacitive sensor using

amplitude modulation. ...................................................................... 40 Figure 2.4: Capacitance changes of LC sensor due to tensile strain calculated

using FEM. The theoretical solution is obtained using Eq. (2.1). .... 41 Figure 2.5: Electric field distribution of interdigital capacitive sensor.................. 42 Figure 2.6: Ratios of capacitances CFEM/Ctheory obtained using FEM and theory

based on Eq. (2.1) as a function of overlap length (2a−w+2b)......... 42 Figure 2.7: Capacitance changes of TC sensor due to tensile strain calculated

using FEM. The theoretical solution is obtained using Eq. (2.6). .... 43 Figure 2.8: Initial capacitance and FM of TC and LC sensors with varying finger

length a from 9 to 16 mm. ................................................................ 43 Figure 2.9: Schematic showing negative offset alignment of TC sensor electrodes

for the purpose of obtaining high C0 or FM...................................... 44 Figure 2.10: Initial capacitance and figure of merit of offset TC sensor as a

function of offset value. .................................................................... 44 Figure 2.11: Fabrication process for the TC flexible capacitive sensor: (Step 1)

flexible substrates; (Step 2) FeCl3 etch; (Step 3) polyimide cutting, (Step 4) bonding of the two electrodes using ultra-flexible epoxy. .. 45

Figure 2.12: Photographs of fabricated TC sensor (TC-3). This sensor measures

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List of figures

55 mm in length and 26 mm in width and has 14 fingers for each electrode............................................................................................ 46

Figure 2.13: Experimental setup for tire compression test. TC-3 sensor is attached to the inner surface of an automobile tire (175/70 R14) in order to measure strain in the circumferential direction................................. 47

Figure 2.14: Frequency responses of phase angle of impedance and capacitance of TC–3 sensor. ................................................................................. 48

Figure 2.15: Capacitance change ratio of LC-1 sensor attached to GFRP plate due to applied tensile strain: loading ( — ) and unloading (- - - ). .......... 49

Figure 2.16: Capacitance change ratio of TC-1 sensor attached to GFRP plate due to applied tensile strain: loading ( — ) and unloading (- - - ). .......... 49

Figure 2.17: Capacitance change ratio of TC-2 flexible sensor (n = 7) attached to the inner surface of a radial tire due to compressive tests: loading ( — ) and unloading (- - - ). .............................................................. 50

Figure 2.18: Capacitance change ratio of TC-3 flexible sensor (n = 14) attached to the inner surface of the radial tire due to compressive tests: loading ( — ) and unloading (- - - ). ................................................. 50

Figure 2.19: Waveform of output signal at strain of ( — ) 0 με and (- - - ) 2160 με using flexible TC-3 sensor as Cx of the wireless transmitting circuit shown in Fig. 2.3 with (a) wired measurement and (b) wireless measurement. .................................................................................... 51

Figure 2.20: Wave amplitude change ratio measured using TC-3 sensor and wireless measurement system due to compressive tests of the radial tire. .................................................................................................... 52

Figure 2.21: Electric capacitance change of TC-3 sensor due to temperature increase, taking 30 ºC as the initial capacitance. .............................. 53

Figure 2.22: Wave amplitude change of the output signal without ( and ∆) and with () temperature compensation using two TC-3 sensors as Cx and Cref. ............................................................................................. 53

Figure 3.1: Inner structure of a typical steel-wire-reinforced radial tire. .............. 65 Figure 3.2: Electric resistor-condenser parallel model of a steel wire belt in a

radial tire. .......................................................................................... 66 Figure 3.3: Photographs of rectangular steel-wire belt specimen cut from a

truck/bus tire. .................................................................................... 67 Figure 3.4: Configuration of the rectangular specimen cut from a truck/bus tire. 67 Figure 3.5: Experimental set-up for capacitance change measurements of the

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List of figures

rectangular tire-belt specimens due to tensile loading using the tensile testing machine and the LCR meter. ..................................... 68

Figure 3.6: Measured capacitance change of the steel-wire belt specimen cut from a truck/bus tire due to applied tensile loading and unloading when initial distance between electrodes d0 = 70 mm. ..................... 69

Figure 3.7: Measured capacitance change of the steel-wire belt specimen cut from a truck/bus tire for various distances: d0= 20 mm; 46 mm; 70 mm. ................................................................................................... 69

Figure 3.8: Photographs of steel-wire belt specimen cut from a radial tire for a passenger car. The lead wires for measurements of capacitance are connected to the steel wires inside the tire belt. ............................... 70

Figure 3.9: Configuration of rectangular specimen of steel-wire belt including polyester carcass fiber cut from a radial tire for a passenger car. ..... 70

Figure 3.10: Two types of electrodes alignments of the radial tire specimen: (a) counter electrode (d0=3, Nd=8); (b) upper interdigital type (Nd=10) 71

Figure 3.11: Measured capacitance change in upper layer (d0=1, Nd=2) of the rectangular specimen cut from a radial tire due to tensile loading and unloading.................................................................................... 72

Figure 3.12: Schema of spacing decrease between adjacent steel wires of tire belt cut from a radial tire with the increase of tensile load...................... 72

Figure 3.13: Measured frequency response of the capacitance and phase angle of the impedance of upper layer type specimen cut from a radial tire. . 73

Figure 3.14: Measured relationship between initial capacitance C0 and spacing d0 of the radial tire specimen for three different types: upper layer; lower layer; upper & lower layers. ................................................... 74

Figure 3.15: Measured relationship between capacitance change ΔC and spacing d0 of the radial tire specimen for three different types: upper layer; lower layer; upper & lower layers. ................................................... 74

Figure 3.16: Measured relationship between initial capacitance C0 and number of wires Nd of the radial tire specimen for three different types: upper layer; lower layer; upper & lower layers. The initial distance between electrodes d0 is 1. ................................................................ 75

Figure 3.17: Measured relationship between capacitance change ΔC and number of wires Nd of the radial tire specimen for three different types: upper layer; lower layer; upper & lower layers. The initial distance between electrodes d0 is 1. ................................................................ 75

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List of figures

Figure 3.18: Measured capacitance change of the radial tire specimen with interdigital electrodes: Nd=10. .......................................................... 76

Figure 3.19: Measured relationship between initial capacitance C0 and of number of wires Nd for two different types of interdigital electrodes. The initial distance between electrodes d0 is 1. ....................................... 77

Figure 3.20: Measured relationship between capacitance change ΔC and of number of wires Nd for two different types of interdigital electrodes. The initial distance between electrodes d0 is 1. ................................ 77

Figure 3.21: Alignment of interdigital electrodes connected to the steel wires in the tire belt of actual radial car tire: (a) schematic illustration; (b) photograph. The part of the shoulder of the tire is cut off for the interdigital-electrode connections..................................................... 78

Figure 3.22: Frequency response of the phase angle of the impedance and the capacitance of an actual radial tire.................................................... 79

Figure 3.23: Measured longitudinal strain and transverse strain vs. displacement of the actual radial tire during compressive loading tests, loading ( — ) and unloading ( - - - ). ............................................................. 79

Figure 3.24: Experimental setup for tire compression tests using a tire itself (175/70 R14) as a sensor................................................................... 80

Figure 3.25: Measured relationship between electric properties and applied compressive loading and unloading of the actual radial tire using interdigital electrodes: (a) electric resistance; (b) capacitance. ........ 81

Figure 4.1: Circuit diagram of the CR oscillator for the wireless strain-measurement of the tire. The tire capacitance is embedded in the circuit as capacitor C................................................................... 89

Figure 4.2: Schematic illustration of strain-monitoring system using CR oscillating circuit............................................................................... 89

Figure 4.3: Photograph of the attached sensor on the inner surface of the radial tire using epoxy adhesive.................................................................. 91

Figure 4.4: Photograph of the dynamic tire-testing machine in the Bridgestone Corporation. ...................................................................................... 91

Figure 4.5: Temperature characteristics of the oscillation frequency of CR oscillating circuit............................................................................... 92

Figure 4.6: Temperature characteristics of NTC thermistor resistance; ( — ) theoretical line is based on Eq. (4.2)................................................. 92

Figure 4.7: Circuit diagram of the self-temperature compensated CR oscillator

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List of figures

with the NTC thermistor Rth, and tire capacitance C. ....................... 93 Figure 4.8: Frequency change ratio fT/f40 of the CR oscillating circuit

with/without self-temperature compensation: (∆) experimental results without NTC thermistor; () theoretical solution with NTC thermistor using Eq. (4.3); () experimental results with NTC thermistor for the purpose of the temperature compensation. .......... 94

Figure 4.9: Oscillating frequency change ratio Δf/f of the CR oscillator due to the static loading-unloading tests............................................................ 95

Figure 4.10: Frequency change ratio Δf/f of the CR oscillator and strain during dynamic loading tests: (a) cyclic frequency of 1 Hz; (b) cyclic frequency of 10 Hz. .......................................................................... 96

Figure 4.11: Measured relationships between oscillating frequency of CR oscillator and strain during static compression test of an actual radial tire. The patch-type truck/bus tire specimen is attached inner surface of the radial tire (175/60R14). .......................................................... 97

Figure 4.12: Frequency change ratio Δf/f and applied strain measured using a strain gage during dynamic rotation test of the actual radial tire (175/70 R14). .................................................................................... 98

Figure 4.13: Deformation of the inner surface of a radial tire near the ground contact area. ...................................................................................... 99

Figure 5.1: Circuit diagram of wireless passive sensor and external antenna using electromagnetic induction change. The capacitance of the tire is embedded in the circuit as Cx.......................................................... 106

Figure 5.2: Equivalent circuit model of wireless passive sensor using electromagnetic induction............................................................... 106

Figure 5.3: Frequency shift of phase dip point caused by the capacitance changes due to the changes in the applied strain to the tire.......................... 107

Figure 5.4: Experimental set-up for tensile test with wireless passive sensor using electromagnetic induction............................................................... 108

Figure 5.5: Measured phase angle shift with capacitance changes in the sensor capacitance using a ceramic condenser: Cx = 0 pF; 2000 pF; 4000 pF..................................................................................................... 109

Figure 5.6: Measured phase angle shift with distance di change between passive sensor and the external antenna: di = 0 mm; 1 mm; 2 mm; 3 mm.. 109

Figure 5.7: Measured impedance Za and phase angle φ of the antenna impedance of wireless passive sensor using the radial tire specimen as sensor

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List of figures

capacitor Cx. .................................................................................... 110 Figure 5.8: Measured resonance frequency change of wireless passive sensor and

strain measured using attached strain gage due to tensile loading tests: first loading () and unloading (); second loading () and unloading (∆)................................................................................... 110

Figure 6.1: Photograph of the tuning circuit consisting of a resistor, an inductor, and capacitor as the sensing target.................................................. 128

Figure 6.2: Schematic illustration of the wireless passive strain-measurement system using tuning circuit. ............................................................ 128

Figure 6.3: Software for real-time spectrum monitoring. The software calculates tuning frequency of the strain sensor using short-time maximum entropy method in real time. ........................................................... 129

Figure 6.4: Measured power spectrum of white noise, the tuning sensor output and received RF at a remote receiver.............................................. 130

Figure 6.5: Tuning frequency change and applied tensile strain to the radial tire specimen using an inductor of 10 mH. ........................................... 130

Figure 6.6: Tuning frequency change of the tuning sensor using inductor of 10 mH and applied tensile strain change under dynamic loading tests: (a) stroke frequency of 1 Hz, (b) stroke frequency of 10 Hz.......... 131

Figure 6.7: Measured relationships between tuning frequency of the tuning sensor using an inductor of 10 mH and applied tensile strain of the rectangular radial tire specimen at various cyclic frequencies: 0.5 Hz; 1 Hz; 5 Hz; 10 Hz. ................................................................... 132

Figure 6.8: Calculated Δft / ft0, ΔPp / Pp0 and ΔQ/Q0 from Eqs. (6.9), (6.11) and (6.16)............................................................................................... 133

Figure 6.9: Schematic illustration of the changes in the power spectrum figuration due to tensile loading. .................................................... 134

Figure 6.10: Measured power spectrum of wirelessly received signal at external receiver. The spectrum curve ( — ) obtained using a digital oscilloscope is approximated using inverse of Lorentzian function ( - - - ).............................................................................................. 134

Figure 6.11: Measured relationship between the change in the tuning frequencies and the strain due to tensile loading: first loading () and unloading (); second loading () and unloading (∆). .................................... 135

Figure 6.12: Measured relationship between the change in the peak power spectrum and strain due to tensile loading and unloading. ............. 135

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List of figures

Figure 6.13: Measured relationship between the change in the quality factor of resonance and strain due to tensile loading and unloading............. 136

Figure 6.14: Estimated strain using three spectral features and response surface methodology and measured strain of tire obtained using a strain gage. The circle symbols show used data for regression of the response surface and the triangle symbols show new experimental data.................................................................................................. 137

Figure 7.1: Wheel slip versus friction coefficient ratio for various road condition: dry, wet, snowy and icy. After [55]. ................................................ 147

Figure 7.2: Finite element model of passenger automobile tire. The blue element uses the material constant of tread in Table 7.1; the purple element is sidewall; the red element is bead. ................................................... 148

Figure 7.3: Contact area between tire and road surface (half and cut mode). The strain sensor is attached to the middle right on the inner surface of the tire. ............................................................................................ 148

Figure 7.4: Tire deformation at wheel load Fw and braking torque Tb. ............... 149 Figure 7.5: Tire deformation at wheel loads of 0, 100, 250, 500 and 1000 N. .... 150 Figure 7.6: Strain and rotation angle at wheel loads of 100, 250, 500 and 1000 N.150 Figure 7.7: Time derivative of strain at wheel loads of 100 N () and 1000 N ().

Two peaks of the waveform are edges of the contact patch. .......... 151 Figure 7.8: Relationship between contact patch lengths obtained using FEM and

estimated from strain data. The solid line indicates the ideal estimation........................................................................................ 151

Figure 7.9: Effective radius versus contact patch length obtained using strain data.................................................................................................. 152

Figure 7.10: Relationship between wheel load Fw and maximum compressive strain................................................................................................ 153

Figure 7.11: Strain distribution when braking torque is applied at 0, 144 and 342 Nm. Wheel load Fw is set to 500 N................................................. 154

Figure 7.12: Compressive strain ratio rfb versus applied braking torque Tb. ....... 154 Figure 8.1: Present and future stages of the proposed tire strain sensor models. 158 Figure 8.2: Schematic illustration of optimized braking control and road

condition warning systems.............................................................. 160 Figure A.1: Circuit diagram of the receiver described in Section 4.2. ................161 Figure A.2: Photograph of the receiver circuit described in Section 4.2. ............162 FigureA.3: Circuit diagram of PIC frequency counter described in Section 4.2.163

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List of figures

FigureA.4: Photograph of PIC frequency counter circuit described in Section 4.2. ........................................................................................................164

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List of tables

List of tables

Table 2.1: IDC sensor configurations used for FEM. ............................................ 41 Table 2.2: Configurations of fabricated flexible IDC sensors. .............................. 46 Table 4.1: Configuration of a radial tire for passenger car. ................................... 90 Table 5.1: Measured coil parameters in the strain sensor and an external antenna.

........................................................................................................ 108 Table 6.1: Specification of the real-time spectrum monitoring software............. 129 Table 6.2: Comparison of Radj

2 using different parameter. .................................. 138 Table 7.1: Material properties of the Mooney–Rivlin model used in finite element

analysis............................................................................................ 149 Table 8.1: Comparative chart showing the two proposed types of strain sensors for

tires. Symbols: Excellent; good; fair; poor................... 158 Table 8.2: Comparative chart showing the four proposed types of wireless data

transmitters. Symbols: Excellent; good; fair; poor....... 159

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Nomenclature

Nomenclature

a, b, c dimensions of flexible sensor b unbiased estimator C capacitance Cdum dummy capacitance Cp parasitic capacitance Cref reference capacitance C10, C01, D1 Mooney–Revlin material constants d distance Ex electric field at distance x FN normal force Ft transverse force Fw wheel load FM figure of merit of sensor f frequency fT frequency at temperature T ft tuning frequency fr resonance frequency g gap distance I electric current i integer J1, J2, J3 invariants of Green–Lagrangian strain tensors j 1− , integer k Boltzmann constant (1.38065… 10–23 [J/K])) k0, k1, k2 coefficients of quadratic polynomial function L coil inductance l length M mutual induction

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Nomenclature

N integer Nd number of electrodes n integer ov offset value P power spectrum Pp peak power spectrum Q quality factor, activation energy q electric charges per unit length R electric resistance Radj

2 adjusted multiple determination Rth electric resistance of thermistor r radius re effective rolling radius S area S(·) power density spectrum function SSE error sum of squares Syy total sum of squares s(·) signal function T temperature Tb braking torque t time, thickness V voltage, vehicle speed Vin input voltage Vout output voltage Vp peak voltage Vs tire slip speed W strain-energy density function w width w(·) window function x distance measurement xi, X variables y, Y responses

Y admittance Z impedance

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Nomenclature

α, β temperature coefficients β coefficient vector Δ variation ε strain εd dielectric constant κ bulk modulus μ frequency at mean power spectrum, friction coefficient ω angular frequency τ shear modulus φ phase angle of impedance ρ electric resistivity ϑ slip ratio θ rotation angle ν Poisson’s ratio Γ full width at half maximum of spectrum subscript a antenna subscript s sensor subscript L lateral comb subscript T transverse comb

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Chapter 1 Introduction


1.1 Motivation

Intelligent tires equipped with sensors for monitoring air pressure, applied strain, temperature, acceleration, etc. are effective in improving the reliability of tires and tire control systems, such as anti-lock braking systems (ABSs). The stimulus for increased research into intelligent tires is usually attributed to the Bridgestone/Firestone recalls in 2000. The Bridgestone/Firestone tire recalls are summarized, based on the US National Highway Traffic Safety Administration (NHTSA) report [1]. The purpose is to develop a clear understanding that faulty tires can cause casualties in an increasingly motorized society, with consequent strong demands for improved tire safety. From 1996, Bridgestone/Firestone began to receive a large number of claims relating to ATX, ATX II, and Wilderness AT tire brands. Most claims involved allegations of tread separation (Fig. 1.1). In May 2000, the NHTSA opened a defect investigation into the ~47 million of those tire brands manufactured by Bridgestone/Firestone. The majority of these tires were original equipment on Ford Explorers. In August 2000, Bridgestone/Firestone announced the voluntary recall of ~6.5 million tires in response to complaints they may have been linked to fatal road traffic accidents. Tires recalled were the Firestone ATX and ATX II brand in the P235/75R15 size and all Firestone Wilderness AT tires in the P235/75R15 size, manufactured at the Decature, Illinois, plant. Tire failure involved tread separation where the tread peeled off, often followed by tire disintegration. This caused the vehicle to roll over, which is estimated to have resulted in >140 deaths and more than 500 serious injuries. After a 4-month investigation, Bridgestone/Firestone announced that one of the

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principal factors in tire failure was low inflation pressure. Ford recommended a pressure of 26 psi for their sports utility vehicles (SUVs), less than the 30-psi inflation pressure recommended by Bridgestone/Firestone. Both of these recommended inflation pressures are less that the “maximum inflation pressure” marked on the sidewall of the tire. Although slightly lower pressure creates greater traction, it also generates more heat in the tire, which contributes to decreased belt-adhesion levels. As a result of the recall of Bridgestone/Firestone tires in 2000, US Transportation Recall Enhancement, Accountability, and Documentation (TREAD) legislation has mandated that every new automobile be equipped with a Tire Pressure Monitoring System (TPMSs) [2-7]. The TPMS employs pressure or other sensor types plus a reliable method of transferring the data from inside a pneumatic tire to alert drivers when tires are under-inflated [8-17]. This legislation has given impetus to the development of advanced tire technologies. In Europe, the European Transport Safety Council’s (ETSC) general reports on EU fatalities illustrate that each year 42 000 EU citizens are killed and over 3.5 million injured in traffic accidents. According to a report from the German Traffic Safety Committee, more than half of accidents involving personal injury are caused by slippery surfaces due to rain, ice or snow. Every year, 40 people are killed in Germany and over 2 000 injured due to defective tires alone [18]. A survey by the APPOLO project revealed that disseminating information about adverse road conditions to in-vehicle applications, drivers and other road users has considerable potential in preventing or reducing the impact of accidents. The decrease in the number of fatalities, provided that the entire automobile fleet is equipped with intelligent tire systems, could, according to conservative estimates, be at least 10%. This means that every year over 4 000 lives could be save in EU countries [19]. The various reports clearly show that adverse road condition and tire defects play a major role in road traffic accidents. As a consequence, there is urgent need, from a traffic safety point-of-view, for intelligent tires with a warning system of road conditions for optimizing control on poor surfaces, a tire defect detection system and a TPMS.

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1.2 Background

A simple TPMS method, based on indirect measurements, uses wheel-speed sensors and the electronic control unit (ECU) of anti-lock braking systems (ABSs) [8]. Persson et al. [20] proposed an indirect tire pressure monitoring system, where no pressure sensors are needed. The system is based on vibration and wheel radius analysis. Kojima et al. [21] developed a TPMS using the signal from wheel speed sensors. Focusing on the relationship between tire pressure and tire torsional stiffness, the system estimated stiffness by disturbance observations. Although indirect systems utilize existing sensors and are easy to install, the degree of accuracy is not reliable. In particular, changes in road conditions affect indirectly calculated pressure. TPMSs using direct measurements have been developed by SmarTire System Inc. of Canada with clamp-on rim sensors and by Schrader Electronics Ltd. of the UK via valve-attached sensors. The clamp-on rim sensors are fixed on the well-bed of the rim with a stainless steel clamp and this fixing method can be applied as an after-sales service. Bridgestone Corp. and Alps Electric Co., Ltd. have developed valve-attached sensors (see Fig. 1.2), where the fixing method involves attaching the sensor casing on the bottom end of the tire valve. Regarding tire pressure sensors, Arshak et al. [22] developed oxide, thick-film capacitors with an oxide dielectric layer formed from niobium pentoxide, titanium dioxide and cerium dioxide. Kolle et al. [23] developed a low-power sensor for tire pressure monitoring using low-power oscillators, but a battery is required for direct TMPS, which limits the operation time of the sensor. To guarantee an effective lifetime of 5–10 years, the battery needs to have a capacity of several hundred mAh, which increases the weight and size of the TPMS sensor. The effect of temperature on battery-life is also problematic. To resolve this problem, Snyder [24] proposed a battery-less TPMS where piezoelectric reeds are included in the tire sensor units and generate electricity. Passive sensor have been also developed [25, 26], i.e. battery-less TPMS sensors using the transponder principle, where the sensor electronics get the necessary energy from a radio signal. However, the energy from the radio signal is insufficient to operate a pressure-measuring sensor and still have enough left to reply. The problem of insufficient energy also shortens the radio range; therefore, further improvements in battery-less technologies are necessary in the area of increased electronic robustness and

21


ease of vehicle integration. Advanced tire sensor systems are currently under development. Known as “intelligent tires,” they are equipped with sensors to monitor strain [27, 28], temperature [29, 30], acceleration [31], etc., in addition to air pressure of tires to improve automobile safety [32-39]. In Europe, an EU project, called APOLLO (2002–2005), has been set-up for the purpose of developing intelligent tires [19, 40-43]. Tire strain monitoring enables one to know the amount of friction between the tires and road surface, which can then be used for the optimization of automobile tire control systems, such as ABS. Intelligent tires also offer beneficial effects for other advanced active safety systems, including traction control systems (TCSs), vehicle stability assist (VSA), early detection of tire separation [34] and tire-burst prevention [44]. As regards indirect methods, the friction between tires and the road surface is estimated based on the difference in wheel velocities of driven and non-driven wheels [45, 46], tire forces [47] or the frequency characteristics of wheel speed vibration [48]. It has also been shown that ABS efficiency is improved by determining four-axis directional force [49, 50], strains in the wheel–tire module [51, 52] or conventional measurements, such as vehicle velocity, angular speed and normal force with a fuzzy controller [53-55]. Bevly et al. [56] estimated three key vehicle states – wheel slip, body sideslip angle and tire sideslip angle – using global positioning system (GPS) velocity data in conjunction with other sensors. Tire–road friction, wheel slip, effective radius [57] and other tire parameters can be also estimated in real-time using a GPS [58-61]. As opposed to indirect measurements, the direct sensor allows a precise measurement of tire deformation or strain. Direct strain measurements provide a number of potential benefits: (i) improved performance of vehicle control systems; (ii) supplying driver information on tire and driving conditions; (iii) providing data for various user groups, such as infrastructure maintenance operators, tire suppliers and other service providers [42]. However, a specific model of strain utilization for improved tire safety has not been presented as yet; hence, a model is urgently needed. For direct strain monitoring, a strain gage, based on polyimide film, is the best known and most widely used method. The sensor, however, has a very high degree of stiffness and low elongation compared to tire rubber. This large difference in stiffness may disturb the deformation and stress of tires, and may also cause the debonding of sensors

22


and rubber over a long period of usage. Sensors with low elongation are also easily damaged by abrupt large deformations. Previously proposed sensors for the strain measurement of tires have had similar problems regarding high stiffness and low elongation. For examples, surface acoustic wave (SAW) sensors use metallic structures, i.e. interdigital transducers or IDTs, arranged on the surface of a piezoelectric substrate [32, 62-66]. Palmer et al. [33] used optical fiber sensors to monitor tire strain and Breuer et al. [37, 67] proposed a tread deformation sensor using GaAs chips, which are glued to thick film ceramic carriers. Tjiu et al. [68] used MEMS sensors, including a pressure sensor, accelerometer and temperature sensor for a tire condition monitoring system. All these sensors, however, are made of high stiffness materials. Until now, flexible sensors have been based on thin technology and, although their flexural rigidity is low, allowing them to be bent, their tensile stiffness is high and elongation low [69, 70]. These sensors are only suitable for very short service periods and, therefore, more reliable sensors with sufficiently low stiffness and high elongation need to be developed.

1.3 Objectives

The aim of the thesis is to investigate the feasibility of a direct, in-service strain measurement device for automobile tires. Based on background research and literature reviews, the key factors for intelligent tire sensors, focused on in this thesis, are as follows: 1. Development of a direct tire deformation or strain measurement system with

sufficiently low stiffness and high elongation. 2. Development of a wireless communication system between tires and vehicle to

operate without a battery. 3. Demonstrate the utilization model of strain data for improved safety and comfort as

well as providing additional services for other user groups.

23


1.4 Outline of thesis

This thesis presents a wireless strain monitoring system for tires using capacitance changes. Two types of sensor technologies are presented. The first method is a patch-type sensor using relatively soft material, which does not interfere with tire deformation and debonding is difficult. Second is a self-sensing method, utilizing the tire structure itself as a sensor. The advantage of this technology is that an attached sensor is unnecessary and debonding problems are eliminated. The applicability of the proposed sensing methods is investigated using radial automobile tires. The thesis is divided into eight chapters; the first being this introductory chapter. Chapter 2 presents the patch-type sensor using ultra-flexible resin. The sensor is constructed from flexible polyimide substrates and ultra-flexible epoxy resin, making the sensor unit, as a whole, low in stiffness and high in elongation. The sensor is attached to an automobile tire and compression tests performed. The strain applied to the radial tire is wirelessly measured using amplitude modulation. The effects of temperature changes are also measured. Chapter 3 presents a self-sensing method for measuring tire strain using the tire itself and studies the electric properties of the tire, in particular, capacitance changes. In this method, tire deformation produces a capacitance change in the tire, which is steel wire reinforced. The rectangular specimens are made from truck/bus tires and radial tires for a normal passenger car. Capacitance changes are measured by applying strain to the specimen. Capacitance and electric resistance changes of an actual automobile radial tire are also measured via compression tests. Chapter 4 proposes and experimentally investigates an active wireless strain measurement system using the capacitance changes in tires and a small capacitance–resistance (CR) oscillating circuit. This wireless communication system is based on frequency modulation, where changes in capacitance alter the oscillating frequency of the CR circuit. Measurement of the oscillating circuit frequency indicates the tire strain, wirelessly. A rectangular section cut from a truck/bus tire is adopted as a specimen for this study. Finally, the feasibility of the proposed system is examined using a commercially available automobile radial tire via static compression and dynamic rotation tests.

24


Chapter 5 proposes and investigates a passive wireless strain measurement system using electromagnetic induction. The system consists of an external antenna and a strain sensor with an inductance–capacitance (LC) resonant circuit. Since the wireless communication uses electromagnetic coupling between two inductors of the antenna and sensor, it does not need a battery in the sensor. Deformation in the tire varies the sensor’s resonant frequency; this frequency variation is measured as a change in the phase angle of the external antenna using electromagnetic induction. Tensile tests are performed and the antenna phase angles measured during the tests. Chapter 6 proposes a passive wireless sensor using tuning frequency changes. The method comprises a tire strain sensor (tuning circuit), an external transmitter and an external receiver. The sensor acts as an LC frequency filter and does not require a battery. The transmitted radio wave from the external transmitter is tuned at the sensor; the tuned radio wave is again picked up at the external receiver. Capacitance changes in the tire alter the tuning frequency of the sensor. These changes in the tuned radio wave facilitate wireless measurement of the applied strain without any power supply to the sensor circuit. The passive wireless method is applied to a radial tire and compression tests conducted. To precisely measure the strain, multiple power spectrum features, such as peak power, sharpness of resonance and tuning frequency, of the sensor output are used with statistical analysis. Chapter 7 introduces the utilization model of strain data for an optimized braking control and road condition warning system. The relationship between strain changes on the inner surface of pneumatic tires, simulating sensor output, and mechanical tire parameters, such as contact patch length, effective radius, wheel load and braking torque, is examined. Calculations are carried out using finite element analysis with a simulation of the strain sensor signal when a tire rotates. Chapter 8 summarizes the thesis. The two types of strain sensing and four types of wireless communication systems are compared. A schema for improved driving safety and comfort, utilizing the strain data, is also shown.

25


Figure 1.1: Tire failure/tread separation incidents occurring in the year 2000 due to faulty tire design and lower inflation pressure than recommended by the manufacturer. After [71].

(a) (b)

Figure 1.2: Tire pressure monitoring systems with valve-attached pressure sensors. (a) Bridgestone tire pressure sensor. (b) Alps Electric TMPS central unit with a Schrader sensor. After [19].

26

Chapter 2 Patch-type flexible sensor


2.1 Background

This Chapter proposes a patch-type sensor which is flexible enough so that it can be attached to tires. Since the sensor is of a patch-type, it is possible to apply it to existing tires. The sensor is composed of two flexible substrates and ultra-flexible epoxy resin. The two substrates are adhered using the ultra-flexible resin in the same plane. Although the substrates have high stiffness and low elongation due to the inclusion of a copper layer and polyimide carrier, the resin has quite low stiffness and high elongation. When the sensor is strained, the ultra-flexible resin is subjected to almost all of the strain, and this allows the sensor to have overall low stiffness and high elongation as a structure. This feature makes the proposed sensor feasible as a strain sensor for tires. It utilizes capacitance changes due to applied strain, and the wireless measurement uses amplitude modulation to transmit the capacitance changes to the external receiver. The advantages of the proposed patch-type flexible strain sensor using capacitance change are summed up as follows: Contrary to acceleration monitoring, strain changes in the tire are essentially

independent of the tire rotation speed. It is easy to implement the obtained strain data to the existing vehicle control system.

The capacitance changes are easy to transmit wirelessly to the vehicle by converting them to frequency changes.

It is easy to manufacture and applicable to existing automobile tires by only attaching inner surface.

The sensor is attached to the inner surface of a tire, and the applicability of the wireless measurement of tire strain is examined. The effects of temperature changes on the sensor system are also measured, and the feasibility of its self-temperature

27


compensation system is experimentally investigated.

2.2 Sensor outline

The proposed capacitive flexible sensor is composed of two flexible substrates based on polyimide films and ultra-flexible epoxy resin, which adheres the two substrates in the same plane, as shown in Fig. 2.1. Since the elastic stiffness of the resin is quite low and the elongation is high compared with the tire rubber and the substrates, the ultra-flexible resin is subjected to almost all of the deformation caused by sensor deformation. Thus, it is possible to make a low stiffness and high elongation sensor that is applicable to the strain measurement of tires. The sensor utilizes the capacitance changes due to deformation. As for the capacitive sensor structure, interdigital capacitors (IDCs) [72-75] are employed here to obtain large initial capacitance, and a lateral comb (LC, Fig. 2.1 (a)) and a transverse comb (TC, Fig. 2.1 (b)) are examined as a comparison of electrode alignments. As for the LC sensor, the measured strain is in the direction parallel to the fingers, and it utilizes the changes in the facing area of the lateral comb. As for the TC sensor, the measured strain is in the direction perpendicular to the fingers, and it utilizes the changes in the gaps between electrodes. For the LC sensor, the capacitance between electrodes is expressed as

( ) PdL 2122 nC

gbcn

gnxbwatC +⎟⎟

⎠

⎞⎜⎜⎝

⎛−−

−+−+−= ε , (2.1)

where εd is the dielectric constant of flexible epoxy resin, t is the thickness of the copper film, w is the width of the interdigital electrodes, c is the gaps between the fingers of an electrode, g is the gaps between electrodes, b is the width of a finger, and n is the number of comb finger pairs, as shown in Fig. 2.2. Each comb electrode is connected to a fixed potential and has a number of fingers of length a, each of which has parasitic capacitance Cp. The edges of the sensor are attached to the sensing material. When strain ε is applied to the sensor, the electrode is displaced at x=εls where ls is the sensor length, which

28


decreases the facing area of the comb. Moreover, capacitance decreases in the case of the LC sensor.

From Eq. (2.1), the initial capacitance C0 and the figure of merit of the sensor (FM) is obtained as follows:

Cx

CFM ∂

∂= , (2.2)

( ) PdL0 2122 nC

gbcn

gnbwatC +⎟⎟

⎠

⎞⎜⎜⎝

⎛−−

−++−= ε , (2.3)

⎟⎟⎠

⎞⎜⎜⎝

⎛−−

−+−=

∂∂

gbcn

gnt

xC

21

dL ε , (2.4)

bwaFM

221

L +−= . (2.5)

The figure of merit FM defined in Eq. (2.2) indicates how much the capacitance of the sensor changes due to applied strain. In general, high C0 and high FM are preferable for sensors, as a larger initial capacitance value makes the sensor less sensitive to parasitic capacitance, while a higher FM has the advantage of a higher resolution of measuring strain. From Eq. (2.4), it can be seen that the capacitance of the LC sensor decreases proportionally to the applied strain, while the FM increases as the overlap length of electrodes (2a−w+2b) decreases from Eq. (2.5). In case of the TC sensor, the capacitance CT and initial capacitance CT0 are given as

( ) PdT 2122 nC

xgbcn

xgnbwatC +⎟⎟

⎠

⎞⎜⎜⎝

⎛+−−

−+

−+−= ε , (2.6)

( ) PdT0 2122 nC

gbcn

gnbwatC +⎟⎟

⎠

⎞⎜⎜⎝

⎛−−

−++−= ε . (2.7)

Considering the one-pair interdigital electrodes model and the fact that the comb finger is initially located at the right middle of the two fingers (g = c/2−b), the FM of the TC sensor is obtained as

2T0T

gxC

xC

≈∂

∂, (2.8)

29


2T gxFM = . (2.9)

From Eqs. (2.8) and (2.9), the FM of the TC sensor increases as the initial gaps between electrodes g decreases.

2.3 Wireless monitoring system

Since tires usually rotate, wireless sensing is indispensable. To transmit the capacitance change of the sensor due to the deformation of the tire, the transmitting circuit is used as shown in Fig. 2.3. The transmitter is composed of the sensor capacitance Cx, reference capacitance Cref, a buffer, and a transmitting antenna. The gain of the buffer is set to one and employed in order to avoid coupling the reference capacitance Cref and the capacitance connected with the outside of the circuit. The transmitter modulates the amplitude of the input sine signal Vin corresponding to Cx, and the output voltage Vout is expressed as

inref

out VCC

CV

x

x

+= . (2.10)

Since the Cref has a constant capacitance value, the output voltage increases as the capacitance of the sensor Cx increases. The output signal, having an alternative voltage of Vout, is transmitted to the external receiver. The received wave is analyzed using a digital oscilloscope, and the amplitude of the sinusoidal wave, Vp, is measured and recorded. In practical application, the changes in environmental temperature affect the strain measurement of tires. Moreover, the friction between tires and the road surface can raise the temperature of tires up to about 60 ºC [76, 77]. The rising temperature of tires also elevates the temperature of sensors attached to the tire surfaces. This temperature change causes changes in the capacitances of the sensors and reference capacitors, and the output voltage of transmitters changes as

( )( ) ( ) in

refout 11

1V

CCC

Vx

x

βαα

++++

= , (2.11)

where α and β are the temperature coefficients of strain sensor Cx and reference

30


capacitor Cref, respectively. Generally, the ceramic condenser used as reference capacitance has a low temperature coefficient compared with sensor capacitance. Therefore, if α is a positive value, then the output voltage increases as the temperature rises. When this output voltage change due to the changing temperature is larger than that due to the changing strain, then the effect of the environmental temperature is not negligible, and the sensor therefore needs temperature-compensation. To compensate for this temperature effect, a dummy capacitive sensor Cdum, which is of the same structure and has almost the same temperature coefficient as the capacitance sensor Cx, is used in place of reference capacitance Cref. Here, the output voltage is rewritten as

( )( ) ( )

.

111

indum

indum

out

VCC

C

VCC

CV

x

x

x

x

+=

++++

=αα

α

(2.12)

From Eq. (2.12) the α is erased, and the change in temperature does not affect the output voltage. When strain is applied to the sensor, only Cx changes, causing an increase in the output voltage, and this enables one to know the applied strain of the tire.

2.4 Finite element analysis

For an actual capacitive sensor, an electric field is also created outside of the area facing the electrodes. Since Eqs. (2.1) and (2.6) assume that the electric field exists only between the facing areas, the actual capacitance created in the sensor is larger than the capacitances obtained by Eqs. (2.1) and (2.6). Since the effect of the additional capacitance on the sensor efficiency is not known, capacitance computation is performed to know the sensor performance before the experiments are conducted. The capacitance computation is performed using the finite element method (FEM) application program ANSYS ver.10. A 2-D, 8-node, and charge-based electric element (Element no. 121) is used, the number of elements is 5000, and there are 17000 nodes. The configurations used as interdigital electrodes are shown in Table 2.1. The dielectric constant of the epoxy resin is 5.0. The capacitance is obtained using the CMATRIX command in ANSYS, which performs electrostatic filed solutions and calculates the

31


capacitance between two electrodes. Figure 2.4 shows the capacitance change ratios of the LC sensor due to tensile strain obtained using FEM analysis (solid circles) and Eq. (2.1) (open circles). Both of the results show that the electric capacitance decreases due to increasing tensile strain. This is because the facing area of the electrodes decreases due to the tensile strain. The result from the FEM analysis, however, is about one fourth of that of Eq. (2.1). Figure 2.5 shows the electric field distribution of the LC sensor, and it reveals that the electric field also exists outside of the facing area in the actual interdigital structure. Figure 2.6 shows the additional capacitance with the changing overlap length of electrodes (2a－w+2b－x), or the facing area. It indicates that the effect of the additional capacitance is not negligible, especially at the short overlap length area. Therefore, the actual capacitance in the sensor due to tensile strain does not become larger than that obtained by Eq. (2.1). In the case of the TC sensor, the electric capacitance increases due to tensile strain as shown in Fig. 2.7. This is because the tensile strain changes the distance between electrodes. Since the overlap length of electrodes does not change due to the tensile strain, the results obtained using the FEM analysis agree with Eq. (2.6). Figure 2.8 shows the FM and initial capacitance due to changes in finger lengths a of the LC and the TC sensors. The figure of merit FM is calculated using 0.625 % tensile strain. As for the LC sensor, though it is problematic that the increasing finger length decreases the FM, the initial capacitance increases. In the case of the TC sensor, increases in finger length increase both the initial capacitance and the FM. Moreover, the aligning interdigital electrodes are offset, which means that the fingers of one electrode are initially not located in the middle of the counter fingers of the other electrodes, as is shown in Fig. 2.9. By employing this offset alignment of electrodes, high FM and C0 values could be obtained. Figure 2.10 shows the FM and C0 of the TC sensors as a function of the offset value ov defined as

bcgbcov

222

−−−

= . (2.13)

An offset value ov = 0 indicates the condition in which the upper finger is located to the right middle of the two lower comb fingers, while an ov of –1 or +1 indicates that

32


the upper finger contacts either the lower left finger or the right finger, respectively, as shown in Fig. 2.9. As can be seen from Fig. 2.10, a high FM and C0 are obtained near ov = ±1. This is because the electrode alignment near ov = ±1 indicates that the two fingers of the counter electrodes are located close to the short gaps between electrodes, and this allows for large capacitance and sensitivity to the changes in the gaps between electrodes. In general, the negative ov leads to a negative electric capacitance change and a positive ov leads to a positive capacitance change.

2.5 Experimental procedures

2.5.1 Sensor fabrication

The fabrication process of flexible capacitive strain sensor has been made as follows: Step. 1 The interdigital electrodes of the LC and TC sensors are made from flexible

substrates (Sunhayato Corp.) comprised of a two-layered structure: 35 μm thickness copper on a 50 μm thickness polyimide carrier (Fig. 2.11).

Step. 2 The copper layer of the flexible substrate is etched using FeCl3 as the

interdigital electrode. Step. 3 Then, the unnecessary polyimide part which is not to be mounted by the

copper layer is cut off. Owing to the etching of the copper layer as interdigital electrodes before the cutting of the polyimide film, the precise pattern of the electrodes can be made.

Step. 4 The two interdigital electrodes are attached using a two-component heat

curing type ultra-flexible epoxy resin (Japan Pelnox ME-113/XH-1859-2). The elongation of the resin is 150 %, and the elastic stiffness is 0.8 MPa.

To prevent from twisting the sensor due to the unsymmetrical structure of the TC sensor, as shown in Fig. 2.1 (b), the TC sensor is fabricated to be symmetric to the longitudinal direction as shown in Fig. 2.11. Further, the TC sensor’s offset value ov is

33


−0.75, which means the electric capacitance decreases due to tensile strain. The LC and TC sensors used in the experiments are named LC-1, TC-1, TC-2, and TC-3, and the dimensions are shown in Table 2.2. Photographs of the fabricated TC sensor are shown in Fig. 2.12.

2.5.2 Capacitance change measurements

Since the deformation of tires is a non-uniform strain condition due to the complexity of the tire structure, GFRP plates are used for the first verification tests of the sensor. The LC or TC sensor is attached to the GFRP plate, and tensile tests are performed using the AG-I (Shimadzu Corp.) tensile testing machine at a stroke speed of 0.2 mm/min. The capacitance is measured using a LCR meter (Hioki E.E. Corp.) at a measuring frequency of 100 kHz, and a conventional strain gage is attached to the GFRP for the reference of strain measurements. Next, the TC sensor is attached to the inner surface of a 14-inch automobile radial tire (175/70 R14) using cyanoacrylate adhesive. The sensor part is located at the bottom, and compressive tests are performed at a stroke speed of 2.0 mm/min, as shown in Fig. 2.13.

2.5.3 Wireless strain measurements

The flexible TC sensor is attached to the inner surface of the tire, and the sensor is connected to the wireless transmitter as capacitance Cx in Fig. 2.3. The input voltage Vin is excited using a general function generator (WAVE FACTORY WF1943, NF Corp.) at a frequency of 100 kHz and amplitude of 10 V sinusoidal wave. The tire is compressed using a tensile testing machine, and the output signal is received wirelessly. The received wave is analyzed using a digital oscilloscope (Waverunner LT224, LeCroy Corp.), and the amplitude Vp is obtained by averaging 1000 cycle waves.

34


2.5.4 Temperature effect measurements

The effects of the environmental temperature changes on the amplitude of the output signal are examined using the electrical furnace KOSMOS (Isuzuseisakusho Co., Ltd.). First, the electrical capacitances of the TC sensors are measured with the elevation of the environmental temperature from 30 ºC to 60 ºC. Then, the strain sensor is connected to the wireless transmitter as Cx, a ceramic condenser is used as Cref, and the amplitude changes due to temperature elevation are measured using the digital oscilloscope. As a temperature compensation test, the dummy sensor, which has the same structure as the TC sensor, is connected as Cref and the output signal amplitudes are measured.

2.6 Results and discussion

2.6.1 Capacitance changes

Figure 2.14 shows the frequency responses of the phase angle of impedance and electric capacitance of TC-3. The abscissa is the measuring frequency of the LCR meter, and the ordinate is the phase angle and capacitance. In the range of 50 kHz to 5 MHz, the phase angle is approximately −90 degrees, which means that the flexible sensor performs as a capacitor. The capacitance of the sensor is about 20 pF at the frequency of 100 kHz. Figure 2.15 shows the electric capacitance change when the LC-1 sensor is attached to the GFRP plate and tensile strain is applied. The abscissa is the strain measured by using a strain gage. The capacitance decreases with increasing tensile strain because the area facing the electrodes also decreases. The initial capacitance is 3.4 pF, which is not enough for a capacitive sensor since the parasitic capacitance could be a few pF. Figure 2.16 shows the case of the TC-1 sensor. The electric capacitance monotonously decreases due to tensile strain. The initial capacitance is as high as 9.3 pF, and the change ratio is also larger than that of the LC sensor. The linear relationship between

35


the capacitance and tensile strain is obtained in the range of 15000 μ. Comparing the results shown in Fig. 2.15 and Fig. 2.16, it can be understood that the TC sensor is more preferable than the LC sensor with respect to strain measurement. Figures 2.17 and 2.18 show the results of compression tests using the TC-2 and TC-3 sensors attached to the inner surface of a radial tire. The abscissa is the strain measured by using a strain gage attached to the inner surface. The inner surface of the tire is subjected to tensile strain due to the compressive loading of the tire. This has the effect of decreasing the capacitance of the TC sensors. The number of comb fingers of the TC-3 sensor is twice as that of the TC-2 sensor, and the offset of the TC-3 sensor is the same as that of the TC-2 sensor. Therefore, the measured initial capacitances of the TC-3 sensor are twice as those of the TC-2 sensor, and the capacitance change is approximately the same as that of the TC-2 sensor, results that agree with Eqs. (2.7) and (2.9). Moreover, hysteresis due to loading and unloading is not observed. Theses results therefore confirm that the TC sensor is applicable to tire strain measurements.

2.6.2 Wireless strain measurements

Figure 2.19 (a) shows the measured output signals of the transmitter with a wired connection at strains of 0 μ (solid line) and 2160 μ (dashed line) using the TC-3 sensor. The voltage of the output signal decreases with the increase in the tensile strain. This change is caused by the decrease in the capacitance due to tensile strain, as indicated in Eq. (2.10). Similarly, Fig. 2.19 (b) shows the wirelessly measured output signal, and the decrease in amplitude voltage can be seen as well. Figure 2.20 shows the relationship between the tensile strain and the change ratio of the output signal amplitude measured wirelessly. The amplitude change ratio decreases with the increase in tensile strain, and the paths of the amplitude changes between loading and unloading are approximately the same. These results therefore clarify that the sensor is feasible for the wireless measurement of tire strain.

36


2.6.3 Self-temperature compensation

Figure 2.21 shows the results of the electrical capacitance changes of two TC-3 sensors due to temperature increases. The electrical capacitance at a temperature of 30 ºC is taken as the initial value C0. The abscissa is the temperature in the furnace measured by a thermo couple, and the ordinate is the ratio of the electrical capacitance change ΔC/C. The electrical capacitance of the TC sensor increases as the environmental temperature rises. This may be caused by an increase in the dielectric constant of the flexible epoxy resin between electrodes. As shown in Fig. 2.21, the temperature coefficient of the sensor is obtained as 2100 ppm/ºC. This indicates that a temperature change of 10 ºC affects the change in the capacitance due to a strain change of 1000 μ. However, the strain of tires can change up to 3000 μ during actual normal use, and thus it can be concluded that the temperature effect is not negligible and that the sensor requires temperature compensation. Figure 2.22 shows the amplitude change of the TC sensor. The abscissa is the measured temperature, and the ordinate is the ratio of amplitude voltage. The amplitude voltage, measured using a 20 pF ceramic condenser as Cref (open squares and triangles), increases with the rise in temperature. This is because the capacitance increases along with the rise in temperature, as shown in Fig. 2.21. In order to compensate the changes in the output amplitude due to the environmental temperature change, the dummy TC-3 sensor Cdum is used as reference capacitor Cref. The configuration and temperature coefficient of Cdum are approximately the same as those of the TC-3 sensor used as Cx. The amplitude change of the compensated transmitter is shown as solid circles in Fig. 2.22. The amplitude change is within ± 0.5 % and does not affect the strain measurement, owing to the temperature compensating system proposed here. On the basis of these results, we conclude that the proposed sensor system is feasible even in an environment with temperature changes.

2.7 Summary

In this Chapter we proposed a patch-type flexible sensor for tire strain measurement.

37


The sensor is composed of flexible substrates and ultra-flexible resin, and the stiffness of the sensor is low while its elongation is high, allowing it to be applied to the strain measurement of automobile tires. The sensor was applied to an automobile radial tire, and the feasibility was experimentally investigated. The effect of the temperature change on the strain measurement was also examined, and the following results were obtained: 1. The capacitance of the proposed flexible sensor decreases monotonously due to

tensile strain when the offset is –0.75 of TC sensor, and there is no hysteresis due to loading or unloading.

2. Using amplitude modulation corresponding to the sensor capacitance, the proposed

sensor is feasible for the wireless strain measurement of tires. 3. Although the capacitance of the sensor increases due to temperature increases and

is not suitable for strain measurement, self-temperature compensation is possible using a dummy sensor.

38


w

s

s

l

Attachment area

Flexible resinCopper electrode

Strain direction x

(a)

w

l s

s

(b)

Figure 2.1: Flexible interdigital capacitive sensor of length ls and width ws: (a) lateral comb (n = 5); (b) transverse comb (n = 5).

39


g

b

w

l

a

c

Figure 2.2: Layout of interdigital capacitive sensor.

V in

V out

Buffer

x

ref

C

C

Figure 2.3: Wireless transmitting circuit for flexible capacitive sensor using amplitude modulation.

40


Table 2.1: IDC sensor configurations used for FEM.

Sensor type w c b g a n lS wS

(mm) (mm) (mm) (mm) (mm) (mm) (mm) LC 20 3 1 0.5 9.25 2 40 20 TC 20 3 1 0.5 16 2 40 20

0 0.5 1 1.5-1.5

-1.0

-0.5

0

Strain, %

ΔC/C

FEM Theory

Figure 2.4: Capacitance changes of LC sensor due to tensile strain calculated using FEM. The theoretical solution is obtained using Eq. (2.1).

41


021763

4352665288

87051108814

130577152340

174103195865

Electric field distribution (V/m)

Interdigital electrodes

Figure 2.5: Electric field distribution of interdigital capacitive sensor.

0 2 4 6 8 10

1

2

3

Overlap length (mm)

CFE

M/C

theo

ry

Figure 2.6: Ratios of capacitances CFEM/Ctheory obtained using FEM and theory based on Eq. (2.1) as a function of overlap length (2a−w+2b).

42


0 0.5 1 1.50

1

2

3

Strain, %

ΔC/C

FEM Theory

Figure 2.7: Capacitance changes of TC sensor due to tensile strain calculated using FEM. The theoretical solution is obtained using Eq. (2.6).

0 0.1 0.20

10

20

30

40

50

60

Initial Capacitance, C0 pF

FM

As finger length a increases,FM and C0 of TC increase.

FM and C0 of LC

Figure 2.8: Initial capacitance and FM of TC and LC sensors with varying finger length a from 9 to 16 mm.

43


ov = 0

ov = negative

Comb fingers

deformation direction

high C0

Figure 2.9: Schematic showing negative offset alignment of TC sensor electrodes for the purpose of obtaining high C0 or FM.

-1 -0.5 0 0.50

200

400

600

800

0

0.2

0.4

0.6

Offset value, ov

FM

Initi

al c

apac

itanc

e, C

0 pF

C0

FM

Figure 2.10: Initial capacitance and figure of merit of offset TC sensor as a function of offset value.

44


Flexible epoxy

Copper film (35μm)Polyimide carrier (50μm)

(pelnox, ME-113/XH-1859-2)

Step 2 FeCl3 etch

Step 3 Cutting polyimide

Step 4 Bonding two substrates

Step 1 Flexible substrates

Flexible substrate

Figure 2.11: Fabrication process for the TC flexible capacitive sensor: (Step 1) flexible substrates; (Step 2) FeCl3 etch; (Step 3) polyimide cutting, (Step 4) bonding of the two electrodes using ultra-flexible epoxy.

45


Table 2.2: Configurations of fabricated flexible IDC sensors.

Sensor no. w c b g a n lS wS

(mm) (mm) (mm) (mm) (mm) (mm) (mm)LC-1 39 2.5 1 0.5 20 9 45 20 TC-1 13 6 1 3.5 7 3 85 26 TC-2 13 6 1 3.5 7 7 55 26 TC-3 13 3 1 0.87 7 14 55 26

Figure 2.12: Photographs of fabricated TC sensor (TC-3). This sensor measures 55 mm in length and 26 mm in width and has 14 fingers for each electrode.

46


Flexible strain sensor

Electrode wire

Inner tire surface

Circumferential direction

Compressive load is applied at 2.0mm/min

Automobile tire

Sensor

Figure 2.13: Experimental setup for tire compression test. TC-3 sensor is attached to the inner surface of an automobile tire (175/70 R14) in order to measure strain in the circumferential direction.

47


104 105 106 107-90-80-70-60-50-40-30-20-10

0

0

10

20

30

Frequency (Hz)

Phas

e an

gle

(deg

)

Phase angle Capacitance

Cap

acita

nce

(pF)

Figure 2.14: Frequency responses of phase angle of impedance and capacitance of TC–3 sensor.

48


0 1000 2000 3000 4000 5000

-0.4

-0.2

0

0.2

Strain, με

ΔC/C

, %

Loading

Unloading

C0=3.4 pF

Figure 2.15: Capacitance change ratio of LC-1 sensor attached to GFRP plate due to applied tensile strain: loading ( — ) and unloading (- - - ).

0 5000 10000 15000

-6

-4

-2

0

2

Strain, με

ΔC/C

, %

Loading Unloading

C0=9.3 pF

Figure 2.16: Capacitance change ratio of TC-1 sensor attached to GFRP plate due to applied tensile strain: loading ( — ) and unloading (- - - ).

49


0 500 1000 1500 2000-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

Strain, με

ΔC/C

Loading

Unloading

C0=9.44 pF

Figure 2.17: Capacitance change ratio of TC-2 flexible sensor (n = 7) attached to the inner surface of a radial tire due to compressive tests: loading ( — ) and unloading (- - - ).

0 500 1000 1500 2000-0.05

-0.04

-0.03

-0.02

-0.01

0

Strain, με

ΔC

/C

Loading

Unloading

C0=22.1 pF

Figure 2.18: Capacitance change ratio of TC-3 flexible sensor (n = 14) attached to the inner surface of the radial tire due to compressive tests: loading ( — ) and unloading (- - - ).

50


0 0.2 0.4 0.6 0.8 1[×10-5]

-1

0

1

Time (sec)

Am

plitu

de (V

)

2160 με

0 με

(a)

0 0.2 0.4 0.6 0.8 1[×10-5]

-0.4

-0.2

0

0.2

0.4

Time (sec)

Am

plitu

de (V

)

0 με

2160 με

(b)

Figure 2.19: Waveform of output signal at strain of ( — ) 0 με and (- - - ) 2160 με using flexible TC-3 sensor as Cx of the wireless transmitting circuit shown in Fig. 2.3 with (a) wired measurement and (b) wireless measurement.

51


0 500 1000 1500 2000 2500-10

-8

-6

-4

-2

0

Strain, με

ΔVp/V

p (%

)

Loading Unloading

Figure 2.20: Wave amplitude change ratio measured using TC-3 sensor and wireless measurement system due to compressive tests of the radial tire.

52


30 40 50 600

1

2

3

4

5

6

Temperature (°C)

ΔC

/C (%

)

TC-3 no.1 TC-3 no.2

Figure 2.21: Electric capacitance change of TC-3 sensor due to temperature increase, taking 30 ºC as the initial capacitance.

30 40 50 600

1

2

3

4

5

Temperature (°C)

ΔV p

/Vp (

%)

TC-3 no.1

TC-3 no.2

Temperature compensation using TC-3 no.2 as Cref

Figure 2.22: Wave amplitude change of the output signal without ( and ∆) and with () temperature compensation using two TC-3 sensors as Cx and Cref.

53

Chapter 3 Self-sensing method for measuring tire deformation

Chapter 3 Self-sensing method for

measuring tire deformation

3.1 Background

Tires comprise rubber with carbon black, steel wire and organic fiber. Steel wire belts are inserted under the tread rubber during fabrication. The steel wire itself is an electrically conductive material and the rubber is a dielectric material. This tire structure resembles an electrical condenser, comprising a couple of electrodes and an inserted dielectric material. Tire deformation causes a change of spacing between the steel wires, which change implies a change in capacitance of the tire part. Measurement of that tire capacitance change indicates the tire strain or deformation without embedding or attaching additional sensors. Here, we highlight the advantages of the strain self-sensing method using capacitance of tire as follows: More direct strain measurement is possible using tire belt itself as a sensor than the

sensor attached on the inner surface. The measurement system could be small, light and capable of withstanding harsh

conditions: high accelerations, shocks, as well as low and high temperatures. The method does not cause debonding of the sensor even during a long period of

service since there is no stiffness difference between the sensor and tire rubber. The method does not disturb the tire’s stress and deformation field.

54


3.2 Electronic circuit model

The passive wireless strain measurement system employs a tire itself as a sensor. Figure 3.1 shows a typical radial tire’s inner structure. Carcass fibers are perpendicular to the beads wire on radial tires as shown in Fig. 3.1. The carcass’s function is to maintain the shape of the tire. Usually the carcass fiber is an organic fiber such as polyester. On the carcass fiber layer, steel wire layers are mounted similarly to cross-ply laminates of composite materials. These fibers are coated with rubber, and the tread rubber layer is mounted on the steel wire layers, as shown in Fig. 3.1. Tread deformation is transferred to the steel wire layers. This transferral implies that measurement of the steel wire layer strain indicates the tire tread deformation. In the steel-wire layer, the steel wire is a straight electrically conductive material; the rubber is a dielectric and electrically resistive material. Figure 3.2 shows a couple of adjacent steel wires. In this figure, the steel wires are placed face-to-face; the dielectric rubber is inserted between the two steel wires. Electric voltage is charged between the steel wires. This structure thereby represents a parallel circuit of an electric resistor and a condenser. The adjacent steel wires are electrodes of these electric resistor and the condenser. Let us consider the adjacent steel wires are given electric charges per unit length, q, -q, respectively. From Gauss’ law, the electric field on the line through centers of the adjacent steel wires, Ex, at distance x from a steel wire as shown in Fig. 3.2 is given as

( )xdq

xqEx −

+=dd 22 πεπε

, (3.1)

where εd is the dielectric constant of the tire rubber and d is the spacing between the adjacent steel wires. Equation (3.1) gives the difference in potential between adjacent steel wires, V, as

⎟⎠⎞

⎜⎝⎛ −

=−= ∫ − rrdqdxEV

r

rd x lndπε

, (3.2)

where r is a radius of a steel wire in the tire.

55


Equation (3.2) shows the electric capacitance of the condenser per unit length, C, as

rrdV

qC−

==ln

dπε. (3.3)

Considering the tire rubber as an electric resistor, the electric resistance between electrodes is expressed as

rldR

2ρ= , (3.4)

where ρ is resistivity of the rubber. The spacing d is enlarged when the steel wire layer is elongated, implying that the capacitance is reduced from Eq. (3.3), and the electric resistance increases from Eq. (3.4). This indicates that the tire deformation alters the tire’s electric resistance and the capacitance. The changes in the electric resistance and the capacitance of the tire belt enable us to know the applied strain of the actual tire.

3.3 Track/bus tire specimen

3.3.1 Experimental procedures

This Section presents a basic demonstration using a small simple specimen taken from a truck/bus tire. The truck/bus tire structure is simpler than that of a passenger-car radial tire, but both are similarly composed of conductive steel wire and dialectical rubber (see Fig. 3.3). The specimen also can be used as an attached or embeddable patch, similarly to a strain gage, instead of using the actual steel wires of a tire. Figure 3.4 shows the truck/bus tire specimen configuration. The specimen length, width and thickness are 250, 30 and 5 mm, respectively. The longitudinal direction of the specimen is the circumferential direction of tire. In this specimen, 1.0-mm diameter steel wire is embedded in parallel with spacing of about 2.5 mm. The fiber angle is perpendicular to the longitudinal direction of the specimen. To facilitate use the tire’s steel wires as electrodes of capacitance, a 5-mm section is removed from the edge of

56


the specimen surface with a normal grinder. The steel wire surface is typically covered with a special surface treatment to improve bonding between the steel and rubber. The wire must be polished with a sheet of sandpaper to remove that coating. After polishing, two lead wires are soldered to the steel wires to produce a pair of electrodes. Initial spacing between the electrodes without applied strain, d0, is 70 mm. A conventional strain gage designed for rubber is attached to the specimen surface between the two electrodes to measure the applied strain. First, a static tension test is performed to measure the capacitance change of the specimen during loading and unloading. Since the simple tire model can be expressed as a single condenser, the measurement of electric resistance is omitted here. Figure 3.5 shows the experimental setup: a tensile testing machine produced by Shimadzu Corp., an LCR meter (LCR meter no 3522) produced by Hioki E. E. Corp., a truck/bus tire specimen, and a computer. For measurement, the charged alternating current is 100 kHz. The applied strain is also measured with the conventional strain gage attached to the specimen surface. The test is performed with a static material-testing machine produced by Shimadzu Corp. The loading speed is 1.0 mm/min. The tensile test is performed up to 3 mm of displacement. Subsequently, unloading is performed. Electrical capacitance is measured without stopping the loading. A silicone rubber sheet is inserted between the jig and the specimen to prevent electrical shorts between the specimen and the testing-machine jigs.

3.3.2 Results and discussion

Figure 3.6 shows results of the truck/bus tire specimen capacitance changes that are attributable to tensile loading. The abscissa in that figure represents the applied strain measured with the attached strain gage; the ordinate represents the specimen capacitance change. This figure reveals that the capacitance decreases from about 12 to 8 pF with the increase of tensile loading. The increase of tensile loading expands the spacing between steel wires of the specimen. From Eq. (3.3), the increase of the spacing between steel wires implies a decrease in capacitance. These cause the decrease of capacitance with tensile loading. Although there is a small hysteresis loop of measured capacitance during loading and unloading, its effect for monitoring tire strain is small.

57


Figure 3.7 shows results of capacitance change of the specimen when the initial distance between electrodes, d0, is 20, 46, and 70 mm. This figure reveals that the capacitance change of the tire increases as the initial distance between electrodes increases. The initial distance must be shorter than 70 mm because the specimen configuration limits the initial distance between electrodes. Therefore, 70 mm is adopted as the distance separating electrodes, d0, because of the largest capacitance change under this limit.

3.4 Radial tire specimen


The second specimen employed in this Section is taken from a commercially available radial tire (see Fig. 3.8). The structure of the radial tire specimen is more complicated than the truck/bus tire specimen used the previous Section, but can simulate the capacitance change in an actual radial tire. Figure 3.9 shows the specimen configuration. The specimen length, width, and thickness are 270, 30, and 4 mm, respectively. The longitudinal direction of the specimen is the circumferential direction of the tire. In this specimen, steel wires of diameter of 1.0 mm are embedded in parallel in spacing of about 2.5 mm. The fiber angle is about ±20° to the longitudinal direction of the specimen. The steel wire surface is covered with a special surface treatment to improve the bonding between steel and rubber. For this study, the wire is polished with a sheet of sandpaper to remove this surface treatment. After polishing, two lead wires are soldered to the steel wires to produce two electrodes. A conventional strain gage designed for rubber is attached to the specimen surface between the two electrodes. It measures the applied strain. Two layers of steel wires (±20°) exist in the radial tire specimen (see Fig. 3.9). Three types of electrode alignment are proposed: upper layer, lower layer, and upper & lower layer. “Upper layer type” uses steel wires embedded in upper steel wire layer of the specimen, as shown in Fig. 3.10 (a). “Lower layer type” uses the steel wires embedded

58


in lower steel wire layer of the specimen. The “upper & lower layer type” use steel wires embedded in upper and lower steel wire layers from the specimen as two electrodes. The use of interdigital electrodes is proposed to increase the tire’s capacitance change attributable to applied strain, as shown in Fig. 3.10 (b). There are two types of interdigital electrodes: upper interdigital and lower interdigital. “Upper interdigital type” has interdigital electrodes in upper steel wire layer of the radial tire specimen and “lower interdigital type” has interdigital electrodes in lower steel wire layer of the radial tire specimen. Firstly, static tension tests are performed to measure the capacitance change of the specimen during loading and unloading. The electrical capacitance is measured with an LCR meter (no. 3522; Hioki E. E. Corp). For measurement, the charged alternating current is 100 kHz. The applied strain is also measured with a conventional strain gage attached to the specimen surface. Tensile tests are performed at stroke speed of 1.0 mm/min and up to 3 mm of displacement. Unloading is performed after tensile testing. Electrical capacitance is measured without stopping loading. A silicone rubber sheet is inserted between the jig and the specimen to prevent short-circuit between the specimen and the testing machine jigs. Secondly, the initial capacitance at the no-loading condition, C0, and capacitance change with the strain change from 0 μ to 3000 μ, ΔC, of the radial tire specimen are measured with the change of initial distance between two electrodes on the no-loading condition, d0, or the number of electrodes, Nd. The initial distance between electrodes, d0, is normalized by the distance between adjacent wires: 4 mm. Finally, aligning the interdigital electrodes, the initial capacitance on the no-loading condition, C0, and capacitance change, ΔC, of the radial tire specimen are measured with the change in the number of interdigital electrodes, Nd.

3.4.2 Capacitance changes

Figure 3.11 shows results of the capacitance change of the radial tire specimen (upper

59


layer type: d0=1, Nd=2) caused by tensile loading. The abscissa is the applied strain measured with the attached strain gage; the ordinate is the measured capacitance change of the specimen. This figure reveals that the capacitance increases with the increase of tensile loading from about 18 to 27 pF. From Eq. (3.3), the increase of the capacitance indicates the decrease of the spacing distance between the steel wires during tensile loading. This decreased distance can be explained using Fig. 3.12. Because the steel fiber angle is about 20°, the tensile loading causes wire rotation, as shown in Fig. 3.12. This rotation decreases the wire angle. In addition, the transverse compressive strain decreases the specimen width. These in turn decrease spacing during tensile loading. Although there is a small hysteresis loop of the measured capacitance during loading and unloading, its effect is negligible for monitoring tire strain. Figure 3.13 shows the frequency response characteristic of the radial tire specimen capacitance. The abscissa is the frequency of the charged alternating current; the ordinate is the measured capacitance and phase angle of the specimen impedance. This figure shows that the phase angle decreases and becomes near –90° with the increased frequency. Therefore, the radial tire specimen can be regarded as an electrical condenser in the high frequency range. Qualitatively, the same results are obtained in cases of the other electrode alignments: lower layer type, upper & lower layer type, upper interdigital type, and lower interdigital type.

3.4.3 Initial distance between electrodes

Figures 3.14 and 3.15 show results of initial capacitance, C0, and capacitance change, ΔC, with the change of initial normalized distance between electrodes, d0, respectively. The abscissa is the initial distance between electrodes, d0, and the ordinate is the initial capacitance, C0, in Fig. 3.14 and capacitance change, ΔC, in Fig. 3.15. In the upper layer type (solid circle symbol) and lower layer type (open circle symbol), C0 and ΔC decrease with the increase of d0, as shown in Figs. 3.14 and 2.15. These results agree with Eq. (3.3): the increase of the distance between electrodes, d, decreases the capacitance, C. Although C0 of the upper layer type is smaller than that of the lower layer type, ΔC of the upper layer type is larger than that of the lower layer type.

60


3.4.4 Number of electrodes

Figures 3.16 and 3.17 show results of the initial capacitance, C0, and capacitance change, ΔC, with the change of the number of electrodes, Nd, respectively. The abscissa shows the number of electrodes, Nd; the ordinate shows the initial capacitance, C0, in Fig. 3.16 and capacitance change, ΔC, in Fig. 3.17. In all three different types – the upper layer type (solid circle symbol), lower layer type (open circle symbol) and upper & lower layer type (cross symbol) – the value of C0 increases with increased Nd, as shown in Fig. 3.16. Increasing Nd indicates an increased number of capacitors in the radial tire specimen, which implies increased C0. Although C0 of the upper layer type is smaller than that of the lower layer type, ΔC of the upper layer type is larger than that of the lower layer type.

3.4.5 Interdigital electrodes

Figure 3.18 shows the electric capacitance change utilizing interdigital electrodes Nd=10 when tensile strain is applied. By comparing Figs. 3.11 and 3.18, it can be seen that the characteristics of the capacitance changes are same with each other, while the absolute value of the capacitance with interdigital electrodes becomes ten times as that without interdigital electrodes. Figures 3.19 and 3.20 show results of the initial capacitance, C0, and capacitance change, ΔC, with the change of the number of interdigital electrodes, Nd, respectively. The abscissa is the number of electrodes, Nd, and the ordinate is the initial capacitance, C0 in Fig. 3.19 and capacitance change, ΔC, in Fig. 3.20. These figures reveal that C0 and ΔC drastically increase with the increase of Nd. The cause of this result is similar to that for results shown in Figs. 3.16 and 3.17. The ΔC of the upper interdigital type (solid circle symbol) is twice as large as that of the lower interdigital type (open circle symbol). A large-ΔC sensor is desired because such a sensor offers high resolution of the measured strain. Consequently, the upper interdigital type (Nd=10) allows production of a specimen of very large ΔC. The Nd is set the maximum limit at 10 in this study to

61


prevent increase of the sensing area.

3.5 Actual radial tire


The shoulder part of the tire tread is cut off to measure the electric resistance and the capacitance between the electrodes in the tire belt as shown in Fig. 3.21. The lead wire is soldered to the steel wire of the tire belt and used as electrodes for measurement of the electric resistance and the capacitance. The capacitance between two adjacent steel wires is about 30 pF. This capacitance value is too small to use as a capacitor in the circuit because it is easily affected by the stray capacitance in the sensor circuit or between the electric elements. Inter-digital electrodes as shown in Fig. 3.21, therefore, have been developed as the measurement electrodes to increase the change in the capacitance. A conventional strain gage for rubber is attached to the inner surface of the tire for measurement of the tire strain. The electric resistance and the capacitance are measured by using LCR meter produced by Hioki E. E. Corp., at measuring frequency of 100 kHz. Appling strain to the tire is performed using a tensile testing machine produced by Shimadzu Corp. at cross head speed of 2 mm/min.


Figure. 3.22 shows the frequency response of the capacitance and the phase angle of an actual radial tire under no loading condition. The phase angle is between 0 and -90 degree under 1 MHz. This indicates that the belt in the actual tire can be assumed as a parallel circuit model of an electric resistor and a capacitor. Figure. 3.23 shows the measured strain in the radial and longitudinal direction to the tire rotation due to the applied compressive loads when the sensor part is located at the road surface as shown in Fig. 3.24. The abscissa is crosshead displacement and ordinate is the measured strain by means of the attached strain gage. The strain in the

62


longitudinal direction of the inner surface of the tire increases, and that in the transverse direction decreases with the increased compressive displacement. Figure 3.25 shows the changes in the electric resistance and the capacitance due to the applied strain using inter-digital electrodes on the tire belt. The abscissa is the measured strain in the longitudinal direction and the ordinate is the measured electric resistance and the capacitance. The electric resistance increases and the capacitance decrease with the increase of the applied strain. This result is opposite to the result using rectangular radial tire specimen as shown in Fig. 3.11. This is because the steel wires in the actual radial tire are aligned continuously not only at the contact area but also at the periphery of the contact. Therefore, the compression at the periphery increases the spacing between steel wires, that resulting in the capacitance decreasing. There is a large hysteresis of the measured electric resistance and the capacitance during loading and unloading, which makes it difficult to predict the strain precisely using only one variable of the electric resistance or the capacitance. Since the change in the capacitance is large enough, 600 pF, using the inter-digital electrodes, the effect of few stray capacitance is negligible.

3.6 Summary

We proposed the self-sensing method of tire strain using capacitance change of tire, and experimentally investigated the capacitance and resistance change due to applied tensile strain. The method has advantages in that: not causing debonding problems, light weight, low cost, and tough, owing to not requiring additional sensor. Three types of specimens were used: the truck/bus tire specimen, the radial tire specimen, and the actual radial tire for passenger car. Conducting tensile tests and compressive tests for actual tire, the following results were obtained: 1. Capacitance of the truck/bus tire specimen decreases as the tensile strain is applied.

This is because the spacing between electrodes increases due to the tensile strain. The initial electrode distance 70 mm is adopted as the largest capacitance change.

2. Capacitance of the radial tire specimen increases with increased tensile loading.

63


This increase of capacitance is caused by the decrease of the spacing of steel wires due to the wire angle rotation under tension loading. Capacitance measurements of the radial tire specimen indicate an appropriate alignment setup of electrodes.

3. When modeling the tire structure as a capacitance and a resistance parallel circuit

in the actual tire, the electric resistance decreases and the capacitance increase with the applied tensile strain. This would be due to the effect of compressive strains at the periphery of the road contact.

64


Carcass

Steel wire belt Tread

Carcass (Polyester fiber)


Bead wire

Carcass


Carcass (Polyester fiber)


Bead wire

Figure 3.1: Inner structure of a typical steel-wire-reinforced radial tire.

65


Distance dRadius r

q -qE

xRadius r

Dielectric constant εd

Steel wires

Resistivity ρ

C

R

Figure 3.2: Electric resistor-condenser parallel model of a steel wire belt in a radial tire.

66


Figure 3.3: Photographs of rectangular steel-wire belt specimen cut from a truck/bus tire.

250 mmt=3 mm

Strain gageSteel wires

Electrodes

Synthetic rubber

30 m

m

Figure 3.4: Configuration of the rectangular specimen cut from a truck/bus tire.

67


LCR Meter

Strain gageSpecimen

Insulator

Signal conditionerBridge circuit

Tensile testing machinePC

Figure 3.5: Experimental set-up for capacitance change measurements of the rectangular tire-belt specimens due to tensile loading using the tensile testing machine and the LCR meter.

68


0 1000 2000 3000 40007

8

9

10

11

12

13

Strain, με

Cap

acita

nce

(pF)

Loading Unloading

Figure 3.6: Measured capacitance change of the steel-wire belt specimen cut from a truck/bus tire due to applied tensile loading and unloading when initial distance between electrodes d0 = 70 mm.

0 1000 2000 3000 40006

8

10

12

Strain, με

Cap

acita

nce

(pF) 70 mm

46 mm

20 mm

Figure 3.7: Measured capacitance change of the steel-wire belt specimen cut from a truck/bus tire for various distances: d0= 20 mm; 46 mm; 70 mm.

69


Figure 3.8: Photographs of steel-wire belt specimen cut from a radial tire for a passenger car. The lead wires for measurements of capacitance are connected to the steel wires inside the tire belt.

Steel wire (upper layer)Steel wire (lower layer)Polyester fiber

30 mm

270 mm t = 4 mm

Figure 3.9: Configuration of rectangular specimen of steel-wire belt including polyester carcass fiber cut from a radial tire for a passenger car.

70


ElectrodesTire specimen


(a)



(b)

Figure 3.10: Two types of electrodes alignments of the radial tire specimen: (a) counter electrode (d0=3, Nd=8); (b) upper interdigital type (Nd=10)

71


0 1000 2000 300018

20

22

24

26

28

Strain, με

Cap

acita

nce

(pF)

Loading Unloading

Figure 3.11: Measured capacitance change in upper layer (d0=1, Nd=2) of the rectangular specimen cut from a radial tire due to tensile loading and unloading.

Load

Steel wire

Load

Steel wire

Figure 3.12: Schema of spacing decrease between adjacent steel wires of tire belt cut from a radial tire with the increase of tensile load.

72


0.001 0.01 0.1 1 10 1000

200

400

600

-80

-60

-40

-20

0

Frequency (kHz)

Cap

acita

nce

(pF)

Capacitance Phase angle

Phas

e an

gle

(deg

)

Figure 3.13: Measured frequency response of the capacitance and phase angle of the impedance of upper layer type specimen cut from a radial tire.

73


0 1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

35

d0

C0 (

pF)

Upper layer Lower layer Upper & lower layers

Figure 3.14: Measured relationship between initial capacitance C0 and spacing d0 of the radial tire specimen for three different types: upper layer; lower layer; upper & lower layers.

0 1 2 3 4 5 6 7 8 90

2

4

6

8

10

d0

ΔC (p

F)


Figure 3.15: Measured relationship between capacitance change ΔC and spacing d0 of the radial tire specimen for three different types: upper layer; lower layer; upper & lower layers.

74


0 2 4 6 8 100

10

20

30

40

50

60

Nd

C0 (

pF)


Figure 3.16: Measured relationship between initial capacitance C0 and number of wires Nd of the radial tire specimen for three different types: upper layer; lower layer; upper & lower layers. The initial distance between electrodes d0 is 1.

0 2 4 6 8 100

2

4

6

8

10

Nd

ΔC (p

F)


Figure 3.17: Measured relationship between capacitance change ΔC and number of wires Nd of the radial tire specimen for three different types: upper layer; lower layer; upper & lower layers. The initial distance between electrodes d0 is 1.

75


0 1000 2000 3000160

180

200

220

240

260

280

Strain, με

Cap

acita

nce

(pF)

Loading Unloading

Figure 3.18: Measured capacitance change of the radial tire specimen with interdigital electrodes: Nd=10.

76


2 4 6 8 10 120

50

100

150

200

250

Nd

C0 (

pF)

Upper layer Lower layer

Figure 3.19: Measured relationship between initial capacitance C0 and of number of wires Nd for two different types of interdigital electrodes. The initial distance between electrodes d0 is 1.

0 2 4 6 8 10 120

20

40

60

80

100

Nd

ΔC (p

F)

Upper layer Lower layer

Figure 3.20: Measured relationship between capacitance change ΔC and of number of wires Nd for two different types of interdigital electrodes. The initial distance between electrodes d0 is 1.

77


Electrodes

Tire belt

Steel wire

Electrodes

Tire belt

Steel wire

(a)

(b)

Figure 3.21: Alignment of interdigital electrodes connected to the steel wires in the tire belt of actual radial car tire: (a) schematic illustration; (b) photograph. The part of the shoulder of the tire is cut off for the interdigital-electrode connections.

78


100 101 102 103 104 105 106 10710-1110-1010-910-810-710-610-510-410-310-2

-80

-60

-40

-20

0

Frequency (Hz)

Cap

acita

nce

(pF)

Phase angle Capacitance

Phas

e an

gle

(deg

)

Figure 3.22: Frequency response of the phase angle of the impedance and the capacitance of an actual radial tire.

0 2 4 6 8 10 12-1500

-1000

-500

0

500

1000

1500

2000

2500

Displacement (mm)

Stra

in, μ

ε Longitiduanal direction

Transverse direction

Loading------ Unloading

Figure 3.23: Measured longitudinal strain and transverse strain vs. displacement of the actual radial tire during compressive loading tests, loading ( — ) and unloading ( - - - ).

79


Applying compressive load

Automobile tire

Sensing area

Strain gage

(175/70 R14)

Figure 3.24: Experimental setup for tire compression tests using a tire itself (175/70 R14) as a sensor.

80


0 500 1000 1500 2000420

440

460

480

500

Strain, με

Res

ista

nce

(kΩ

)

Loading Unloading

(a)

0 500 1000 1500 2000

15800

16000

16200

16400

Strain, με

Cap

acita

nce

(pF)

Loading Unloading

(b)

Figure 3.25: Measured relationship between electric properties and applied compressive loading and unloading of the actual radial tire using interdigital electrodes: (a) electric resistance; (b) capacitance.

81

Chapter 4 Active wireless monitoring using CR oscillator

Chapter 4 Active wireless monitoring

using CR oscillator

4.1 Background

This Chapter proposes a wireless strain-measurement method using a capacitance and resistance (CR) oscillating circuit. Contrary to the amplitude modulation method in Chapter 2, the CR oscillator method utilizes the frequency modulation. The frequency modulation has advantages in the robustness against radio noises; it allows for long radio range. The method wirelessly measures the alteration of electrical capacitance change during tire deformation as shown in the previous Chapter. This wireless strain-measurement system adopts the tire itself as a sensor. Therefore, the system does not engender any of the problems relating sensor bonding. Steel wire that is usually used for tires is adopted as an electrode of an electrical condenser, and is connected to the CR oscillating circuit. Tire deformation causes the electrical capacitance change of the CR oscillating circuit, leading to the change in oscillating frequency. Change of the oscillating circuit frequency indicates the deformed tire strain without the need for communication by wire. The system is applied to a rectangular specimen cut from a truck/bus tire. Then static and dynamic tension tests are performed to confirm the system. Finally, static compression and dynamic rotation tests with a commercially available passenger-car tire demonstrate the feasibility of the proposed system.


A typical capacitance-resistance (CR) type oscillating circuit creates high-frequency

82


electro-magnetic waves such as radio waves. Such a circuit usually emits constant-frequency waves when the capacitance C and resistance R of a CR oscillator are fixed. The frequency of the CR type oscillating circuit employed here (see Fig. 4.1) is determined as

CRf

2.2osc =1 . (4.1)

This equation means that a decrease in capacitance C causes an increase of the oscillating frequency when resistance R is fixed. Details of the CR type oscillating circuit are described in the reference (for example, see [78]). In this oscillating circuit, a condenser, which is one of the two key parts needed to calculate the oscillating frequency, is replaced with the tire specimen. This circuit is capable of emitting oscillating waves around 12 MHz with a resistance of 5 kΩ as R. A short-gate frequency counter was produced from a peripheral interface controller (PIC) to measure the frequency change of the oscillating circuit. This PIC frequency counter allows measurement of frequency change up to the loading frequency of 10 Hz. The circuit diagram of the PIC frequency counter is referred in Appendix A. A schematic representation of the wireless strain-measurement system is shown in Fig. 4.2. A tire is connected to the oscillating circuit as capacitance C. The electrical capacitance of the tire changes when the tire deforms. That capacitance change alters the oscillating frequency. Setting the oscillating circuit frequency to an appropriately high frequency can allow transmission of the oscillating waves wirelessly. Those oscillating waves can thereby be measured with a receiver (see Appendix A). The frequency of the received oscillating waves is measured using a frequency counter made from PIC. Then the frequency data are converted to voltage data using a D/A converter and are sent to a signal-processing unit. Measurement of the frequency change indicates the change of the tire’s capacitance, which in turn indicates the degree of tire deformation.

83



4.3.1 Specimens

Material used here is taken from a truck/bus tire. The truck/bus tire structure is simpler than that of a passenger-car radial tire, but both are similarly composed of conductive steel wire and dialectical rubber. The proposed system can be applied as an attached or embeddable patch, similarly to a strain gage, instead of using the actual steel wires of a radial tire. This patch-type system resembles a conventional strain gage. Using the actual steel wires of the tire can be more effective to measure tire deformation. It will be discussed in Section 6.5. The specimen configuration is shown in Fig. 3.4. The specimen length, width and thickness are 250, 30 and 5 mm, respectively. The longitudinal direction of the specimen is the circumferential direction of tire. In this specimen, 1.0-mm diameter steel wire is embedded in parallel with spacing of about 2.5 mm. The fiber angle is perpendicular to the longitudinal direction of the specimen. Initial spacing between the electrodes without applied strain, d0, is 70 mm. A conventional strain gage designed for rubber is attached to the specimen surface between the two electrodes to measure the applied strain.


During driving, tire rubber temperature rises because the friction heat is generated between a tire tread and a road surface. The tire tread temperature becomes 40 ºC after only 30 minutes’ driving. It increases up to about 60 ºC during a long drive. To provide stability in light of this temperature change that occurs during driving, a temperature increase test is performed at 40–70 °C. The tire temperature increase causes changes in characteristics of the CR oscillating circuit and tire, which engender changes in the oscillating frequency. Therefore, the sensor must be self-temperature compensated to prevent frequency change that would

84


occur along with the temperature change. A negative temperature characteristic (NTC) thermistor is used here for self-temperature compensation. The NTC thermistor resistance decreases with increased temperature. The oscillating frequency of the oscillating circuit with the NTC thermistor is measured in a temperature-controlled chamber (dry oven kosmos; Isuzuseisakusho Co. Ltd.).

4.3.3 Static and cyclic loading tests

Static and cyclic loading tests were performed to check the system feasibility. The truck/bus tire specimen is connected to the CR oscillating circuit shown in Fig. 4.1. First, a static tension test is conducted. Then the specimen is loaded cyclically to measure cyclic strain wirelessly. The stroke frequency is 1 and 10 Hz, which correspond to vehicular velocities of about 7 and 70 km/h, respectively. The distance between two antennas is 1 m, on the assumption that the transmitter is embedded in a tire and the receiver is mounted in a fender.

4.3.4 Feasibility study for a commercially available tire

The feasibility of the proposed system is examined using a commercially available tire of a passenger car by performing static compression and dynamic rotation tests. Table 4.1 shows specifications of the tire used here. The tire specimen cut from a truck/bus tire is attached to the inner surface of the tire as a patch-type sensor, as shown in Fig. 4.3. Alpha-cyanoacrylate adhesive is used to attach the tire sensor to the tire surface. CR oscillating circuit is attached to the tire surface with epoxy-type adhesive. A conventional strain gage is also attached next to the patch-type tire sensor. The static compression test and dynamic rotation test are performed with a dynamic tire-testing machine, as shown in Fig. 4.4. Measurement of the oscillating frequency is conducted with a wired system. The tire air-pressure is 200 kPa, which is a typical condition. In the static test, the tire is pressed statically up to 48.5 mm to the drum of a dynamic tire-testing machine. In the dynamic rotation test, the tire is rotated via the drum after the tire is pressed up to 48.5 mm to the drum.

85




Figure 4.5 shows results of the frequency change of an oscillating circuit with increased temperature of both the truck/bus tire specimen and the oscillating circuit. The abscissa is the measured temperature using a thermometer embedded in the chamber; the ordinate is the oscillation frequency change ratio fT/f40, where fT indicates the oscillation frequency at T °C. This figure shows that the oscillation frequency decreases as the tire temperature increases. The oscillating frequency increases with increased temperature. For that reason, Eq. (4.1) indicates the necessity of a resistance that decreases with increased temperature for the CR oscillating circuit. Resistance of the NTC thermistor used here for self-temperature compensation decreases with increased temperature, as shown in Fig. 4.6. In this figure, the abscissa is temperature and the ordinate is the thermistor resistance. The thermistor resistance Rth changes as

⎟⎠⎞

⎜⎝⎛∝

kTQR expth , (4.2)

where k is Boltzmann constant, Q is activation energy of semiconductor, and T is absolute temperature. Figure 4.7 shows a circuit diagram of the temperature compensated CR oscillator. The oscillating frequency, fosc, changes as

( )thosc 2.2

1RRC

f+

= (4.3)

Figure 4.8 shows temperature characteristics of the oscillating frequency of the self-temperature compensated CR oscillator. In this figure, the calculated curve is derived using Eqs. (4.1) and (4.3); the experimental result reveals that the frequency change ratio decreases from use of the NTC thermistor. The proposed CR oscillator with the NTC thermistor is confirmed to be effective for self-temperature compensation.

86


4.4.2 Static and cyclic loading tests

Figure 4.9 shows results of the static loading test. In this figure, the abscissa is the applied strain measured using the strain gage; the ordinate is the measured frequency change ratio Δf/f of the oscillating circuit. The frequency change ratio indicates the ratio of the frequency change Δf to frequency f in the no-loading condition. This figure shows the almost linear relation between applied strain and frequency change. The applied tensile strain widens the spacing between electrodes, thereby decreasing the electric capacitance of the truck/bus tire specimen. That capacitance decrease increases the frequency of the oscillating circuit. Figure 4.9 shows the slight hysteresis between loading and unloading. Figures 4.10 (a) and (b) show the measured frequency change attributable to cyclic loading at loading frequencies of 1 Hz and 10 Hz, respectively. In these figures, the abscissa is the time and the ordinate is the measured frequency and strain. These figures show that this system monitors tire strain wirelessly at loading frequencies of 1 Hz and even 10 Hz. Vehicular velocity of about 7 km/h produces frequency of loading of 1 Hz; therefore, 70 km/h velocity implies a frequency of about 10 Hz. The sampling frequency of the frequency counter made from PIC is sufficient for monitoring frequency change at high speed.

4.4.3 Feasibility study for a commercially available tire

Figure 4.11 shows static compression test results from use of the dynamic tire-testing machine. The abscissa shows the applied strain measured using the strain gage; the ordinate is the measured frequency change of the oscillating circuit. The figure shows the almost linear relation obtained between the applied strain and the frequency change. Figure 4.12 shows results of the dynamic rotation test. The abscissa is the time and the ordinate is the measured frequency and strain. As shown in this figure, the frequency changes according to the strain change. The results reveal that the inner surface of the tire is compressed just before the tire contacts with the road surface; it is then strained

87


during contact. It is compressed again after contact, as shown in Fig. 4.13. These results verify that the proposed method is applicable for wireless strain measurement of commercially available tires.

4.5 Summary

This Chapter has presented an active wireless monitoring method of tire strain using CR oscillator. The tire is connected to an oscillator circuit as a condenser. The tire’s capacitance change is converted to a frequency change of oscillating waves using a CR oscillating circuit. The method does not use additional sensors. Consequently, it does not disturb the tire’s stress and deformation field, nor does it cause sensor debonding. The method was applied to a rectangular specimen cut from a truck/bus tire and has been investigated experimentally. The following results were obtained. 1. The frequency of the CR oscillating circuit changes concomitant with tire

capacitance change. 2. A self-temperature-compensated CR oscillator with the NTC thermistor is proposed

and proved to be effective. 3. The proposed wireless strain measurement is demonstrated experimentally with

cyclic loading tests at loading frequencies of 1 Hz and 10 Hz. 4. Static compression and dynamic rotation testing of the proposed method verifies it

to be applicable to commercially available tires.

88


R

74HCU04

CTire

Output

Figure 4.1: Circuit diagram of the CR oscillator for the wireless strain-measurement of the tire. The tire capacitance is embedded in the circuit as capacitor C.

Receiver PIC frequency counterDA converter

PCTire specimen CR oscillator Receiver PIC frequency counterDA converter

PCTire specimen CR oscillator

Capacitance change

Frequency change

Tire deformation

Transmitter

Voltage change

Strain measurement

Frequency change

Receiver

Radio wave

Capacitance change

Frequency change

Tire deformation

Transmitter

Voltage change

Strain measurement

Frequency change

Receiver

Radio wave

Figure 4.2: Schematic illustration of strain-monitoring system using CR oscillating circuit.

89


Table 4.1: Configuration of a radial tire for passenger car.

Tire width 175 mm Oblateness 70% Structure Radial

Diameter of rim 14 inches Road index 475 kg Max. speed 180 km/h

90


Figure 4.3: Photograph of the attached sensor on the inner surface of the radial tire using epoxy adhesive.

Figure 4.4: Photograph of the dynamic tire-testing machine in the Bridgestone Corporation.

91


40 50 60 70

0.94

0.96

0.98

1

1.02

Temperature (°C)

Freq

uenc

y ch

ange

ratio

, fT /

f 40

Figure 4.5: Temperature characteristics of the oscillation frequency of CR oscillating circuit.

40 50 60 700

0.5

1

1.5

2

Temperature (°C)

Res

ista

nce

(kΩ

)

Experiment Theory

Figure 4.6: Temperature characteristics of NTC thermistor resistance; ( — ) theoretical line is based on Eq. (4.2).

92


R

74HCU04

CTire

Output

Rth

Figure 4.7: Circuit diagram of the self-temperature compensated CR oscillator with the NTC thermistor Rth, and tire capacitance C.

93


40 50 60 70

0.94

0.96

0.98

1

1.02

Temperature (oC)

Freq

uenc

y ch

ange

ratio

, fT /

f 40

with thermistor (Experiment) with thermistor (Theory) without thermistor

Figure 4.8: Frequency change ratio fT/f40 of the CR oscillating circuit with/without self-temperature compensation: (∆) experimental results without NTC thermistor; () theoretical solution with NTC thermistor using Eq. (4.3); () experimental results with NTC thermistor for the purpose of the temperature compensation.

94


0 1000 2000 3000 40000

0.02

0.04

0.06

0.08

0.1

Strain, με

Freq

uenc

y ch

ange

ratio

, Δf /

f

Loading

Unloading

Figure 4.9: Oscillating frequency change ratio Δf/f of the CR oscillator due to the static loading-unloading tests.

95


0 1 2 3

0

0.05

0.1

0.15

-6000

-4000

-2000

0

2000

4000

Time (s)

Freq

uenc

y ch

ange

ratio

, Δf /

f

Stra

in, μ

ε

Strain

Frequency

(a)

0 0.1 0.2 0.3 0.4 0.50

0.025

0.05

0.075

0.1

-3000

-2000

-1000

0

1000

2000

3000

Time (s)

Freq

uenc

y ch

ange

ratio

, Δf /

f

Stra

in, μ

ε

Strain

Frequency

(b)

Figure 4.10: Frequency change ratio Δf/f of the CR oscillator and strain during dynamic loading tests: (a) cyclic frequency of 1 Hz; (b) cyclic frequency of 10 Hz.

96


0 1000 2000 3000 40002.4

2.5

2.6

2.7

2.8

Strain, με

Freq

uenc

y (M

Hz)

Figure 4.11: Measured relationships between oscillating frequency of CR oscillator and strain during static compression test of an actual radial tire. The patch-type truck/bus tire specimen is attached inner surface of the radial tire (175/60R14).

97


0 5 10

-0.02

0

0.02

0.04

0.06

-15000

-10000

-5000

0

5000

Time (s)

Freq

uenc

y ch

ange

ratio

, Δf /

f

Stra

in, µ

ε

Strain

Frequency

Figure 4.12: Frequency change ratio Δf/f and applied strain measured using a strain gage during dynamic rotation test of the actual radial tire (175/70 R14).

98


re

V

ω

Tread

Belt

CompressionTension

Deformation direction

Compression

Figure 4.13: Deformation of the inner surface of a radial tire near the ground contact area.

99

Chapter 5 Passive wireless monitoring using electromagnetic induction

Chapter 5 Passive wireless monitoring

using electromagnetic induction

5.1 Background

The method in the previous Chapter employed a capacitance resistance (CR) oscillating circuit to wirelessly transfer the capacitance change information. The steel wire of the tires was adopted as an electrode of the CR oscillating circuit. Tire deformation caused an electrical capacitance change in the circuit, which changed the oscillating frequency. Measurement of the frequency change in the oscillating circuit indicated, wirelessly, the strain of the deformed tire. However, the system requires batteries to activate the CR oscillating circuit. This fact may cause some drawbacks for long-term service as is described in Chapter 1. Harpster et al. [79, 80] have proposed a passive wireless humidity sensor consisting of a capacitive humidity sensor chip and a hybrid coil. This passive sensor requires no batteries to activate the sensor circuit. In this Chapter, a passive, wireless humidity-sensor system proposed by Harpster et al. is improved to produce the tire strain-measurement system. The system consists of an external antenna and a strain sensor, LC resonant circuit to which a tire is connected. The passive wireless system uses electromagnetic coupling between two inductors of the antenna and the strain sensor [81, 82]. When the specimen deforms, the specimen capacitance changes. That capacitance change then alters the strain sensor’s resonant frequency. That resonant frequency change is measured as a change in the phase angle of the external antenna using electromagnetic induction. This passive wireless method is applied to a radial tire specimen and the static applied strain is measured.

100



Figure 5.1 shows a schematic representation of the passive wireless strain-measurement system. This system comprises an external antenna and a strain sensor. A tire belt is connected to the strain sensor circuit as a capacitance. Wireless communication between the external antenna and a strain sensor is performed by means of the electromagnetic induction between the two coils. As shown in Fig. 5.1, the strain sensor circuit is a typical LC resonant circuit. Impedance of the strain sensor circuit, Zs(ω), is

( ) ( )⎭⎬

⎩⎨ +

−+=xCC

LjRZs

sss ωωω

⎫⎧ 1 , (5.1)

where j is 1− , ω is radian frequency, Ls is an inductance of the coil of the sensor circuit, Rs is a series resistance of the coil Ls, Cs is a capacitance of the sensor circuit and Cx is the tire specimen capacitance. From Eq. (5.1), the resonant frequency, fr, of the strain sensor circuit is calculated as

( )xCCLf

+=

ssr 2

1π

. (5.2)

In the LC resonant circuit, a condenser Cx, which is one of the two essential parts needed to calculate the resonant frequency, is replaced with the tire specimen. Therefore, the tire electrical capacitance changes when the tire deforms. That capacitance change engenders change of the resonant frequency, fr, of the strain sensor when inductance, Ls, is fixed. Measurement of the change of the resonant frequency, fr, indicates the tire capacitance change attributable to tire deformation. Figure 5.2 shows the equivalent circuit used to model the sensor system shown in Fig. 5.1. The impedance of the external antenna, Za(ω), is given as

( ) ( )ωωωω

s

22

aaa ZMLjRZ ++= , (5.3)

where La is the inductance of the coil of the antenna circuit, Ra is a series resistance of the coil La and M is a mutual inductance between coil La and Ls.

101


From Eqs. (5.1) and (5.3), the impedance of the external antenna at the resonant frequency, ω0, of the strain sensor is calculated as

( )s

220

a0a0a RM

LjRZω

ωω ++= . (5.4)

Figure 5.3 shows the typical angle phase of the antenna, φ(f), for measuring frequency f: the phase dip minimum point occurs at the resonant frequency fr. The frequency at the phase dip minimum point of the antenna impedance is independent of the mutual induction M and equal to the resonant frequency, fr, of the strain sensor. The impedance phase dip magnitude |Δφdip| is the difference in φ(ω0) between a coupled (M=finite) and uncoupled (M=0) antenna, and can be expressed as

( ) ( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛≅∠−+∠=Δ −

sa

201

0aa0adip tanRLM

ZLjRω

ωωϕ . (5.5)

The quality factor Qs, the sharpness of the resonant, is given by

xCCL

RQ

+=

s

s

ss

1 . (5.6)

From this equation, minimizing Rs and maximizing Ls increase Qs. Their change also increases the antenna impedance and the magnitude of the phase dip. Higher-Qs sensors are desired because it is thereby easy to find the phase-dip-minimum point from the sharp phase angle peaks. Higher-Qs sensors, therefore, enable us to measure the capacitance change at longer testing distance between the strain sensor and the external antenna. This monitoring system offers two main advantages. Firstly, the strain sensor system is a passive wireless type. Such a passive wireless sensor requires no batteries to activate the sensor circuit. Thereby, weight reduction and long-term stabilization are assured. Secondly, the proposed monitoring system using electromagnetic induction is not affected by mutual induction, M, and changed distance between the external antenna and the strain sensor, whereas passive sensors using electromagnetic induction are usually affected by the mutual induction change [82, 83].

102



Static tensile tests are performed to check the feasibility of this wireless passive monitoring system. Table 5.1 lists experimental values for the antenna circuit coil inductance (La) and the strain sensor circuit (Ls). A ceramic condenser of 0.01 μF is used as the constant condenser Cs. Figure 5.4 shows the experimental setup of the tensile testing machine, the radial tire specimen, strain sensor, external antenna, LCR meter, and computer. Static tensile strain is applied using tensile testing machine and frequency response is measured in terms of the phase angle of the antenna circuit impedance, φ. The proposed passive sensor assumes a pure electrical capacitance as a sensing target. However, the radial tire specimen capacitance is not a pure capacitance. Therefore, before using the radial tire specimen, a ceramic condenser, a pure electrical capacitance, is used as the capacitance Cx: Cx=2000 pF; 4000 pF. The frequency response characteristic of the phase angle, φ, is measured with the change of distance di to confirm the effect of the distance between two coils of the antenna circuit and strain sensor circuit, di. Using the radial tire specimen (upper interdigital type: Nd=10) as a capacitance Cx of the strain sensor circuit, static tension tests are performed at stroke speed of 1.0 mm/min and up to 3 mm of displacement. Subsequently, unloading is performed. The electrical capacitance is measured without stopping the loading. A silicone rubber sheet is inserted between the jig and the specimen to prevent electrical shorts between the specimen and the testing machine jigs.


Figure 5.5 shows the frequency response characteristic of phase angle, φ, using the ceramic condenser as a condenser Cx: Cx=0, 2000 and 4000 pF. In this figure, the abscissa is the measuring frequency of the LCR meter and the ordinate is the measured phase angle, φ, of the external antenna impedance. As shown in this figure, the phase dip minimum point observed at resonant frequency, fr, decreases with the increase of capacitance Cx, which agrees with Eq. (5.2). Figure 5.6 shows results of the frequency response characteristic of the phase angle, φ, with the change of distance between two

103


coils, di. The figure reveals that, although the phase dip magnitude |Δφdip| decreases with the increase of di, the measured frequency at phase dip minimum point is independent of di and equal to the resonant frequency. Figure 5.7 shows the results of the frequency response characteristic of the phase angle, φ, and impedance of the antenna circuit, Za, using the radial tire specimen as a condenser Cx on no-loading condition. The abscissa is the measuring frequency of the LCR meter and the ordinate is the measured impedance, Za: (solid circle symbol), and its phase angle, φ: (open circle symbol), of the external antenna impedance. This figure confirms that the phase angle, φ, has a dip minimum point at the resonant frequency (2.2 MHz). Figure 5.8 shows results of static tension tests of the radial tire specimen using the proposed wireless passive system. In this figure, the abscissa is the loading strain measured by means of attached strain gage and the ordinate is the measured resonant frequency, fr, obtained from the frequency at the phase dip minimum point of φ. These static tension tests are performed twice: first loading (solid circle symbol), first unloading (open circle symbol), second loading (solid triangle symbol), and second unloading (open triangle symbol). The measured resonant frequency, fr, decreases with the increase in the tensile strain, as shown in Fig. 5.8, corresponding to the increase of capacitance occurring with the increase in tensile strain, as shown in Fig. 3.18. Results obtained from Fig. 5.8 indicate that the proposed method is applicable for wireless strain measurement of commercially available tires. The data scatter observed in Fig. 5.8 is caused by the LCR-meter performance. Because the maximum measuring frequency of the LCR meter is limited less than 5 MHz, the resonant frequency, fr, of the sensor circuit must also be less than 5 MHz. To set the resonant frequency, fr, under this limit, the sensor circuit needs the condenser as Cs of 0.01 μF, in parallel with the radial tire specimen. Consequently, the change of the resonant frequency resulting from the capacitance change of the radial tire specimen is only about 4 kHz. This makes it difficult to measure the change of the phase dip minimum point precisely. If we use a LCR meter device measuring impedances at a higher frequency range, a condenser Cs to reduce the resonant frequency is not necessary. Thereby, this problem will be improved. In practical use, the strain sensor is embedded in a tire and an external antenna is

104


located on an axle. In this case, the radio range, i.e. the distance between a strain sensor and an external antenna, is about 300 mm. This means that the measurable distance, at present 5 mm, is insufficient. Therefore, the coil configuration requires improvement to obtain a longer wireless range, as implied by Eq. (5.6). Another difficulty is that the measuring the impedance requires about 2 minutes at current condition, which renders dynamic measurement difficult. However, this problem will be improved by means of a rapid and sensitive impedance-measurement device.

5.5 Summary

This Chapter proposed a passive wireless method to measure tire strain using electromagnetic induction. Steel wires of the tire are adopted as condenser electrodes of a simple LC resonant circuit and the capacitance change of the tire is converted to resonant frequency change of the LC circuit. That resonant frequency change is measured as a change in the phase angle of the external antenna using electromagnetic induction without battery in the sensor circuit. The method was applied to a rectangular specimen cut from a commercially available radial tire and has been investigated experimentally with static tension tests, and it has been verified to be effective. However, low precision of measuring strain, short radio range, and low sampling rate are problematic.

105


LCR

meter

Ra Rs

La Ls Cs Cx

Wireless communication

External antenna Strain sensor

M

Figure 5.1: Circuit diagram of wireless passive sensor and external antenna using electromagnetic induction change. The capacitance of the tire is embedded in the circuit as Cx.

LCR

meter

Ra

La

(ωM)2

Zs

Figure 5.2: Equivalent circuit model of wireless passive sensor using electromagnetic induction.

106


1.8 1.85 1.9 1.95 2 2.05 2.181

82

83

84

Frequency (MHz)

Phas

e an

gle,

ϕ (d

eg)　

Phase dip pointLoading

|⊿ϕdip|

Figure 5.3: Frequency shift of phase dip point caused by the capacitance changes due to the changes in the applied strain to the tire.

107


Table 5.1: Measured coil parameters in the strain sensor and an external antenna.

La Lｓ

Length (mm) Number of windings

Diameter of coil (mm) Diameter of wire (mm)

Measured L (nH) Calculated (nH) Resistance (Ω)

2.69 5

6.56 0.600 188 248

0.331

18.2 19

6.56 0.600 725 670

0.214

PC

StrainSensor

LCR meter

ExternalAntenna

Tensile testing machine

Strain gage

Insulator

Specimen

Bridge circuit Signal conditioner

Passive & Wireless

PC

StrainSensor

LCR meter

ExternalAntenna

Tensile testing machine

Strain gage

Insulator

Specimen

Bridge circuit Signal conditioner

Passive & Wireless

Figure 5.4: Experimental set-up for tensile test with wireless passive sensor using electromagnetic induction.

108


2 2.2 2.4 2.6 2.8 3

88

89

90

91

Frequency (MHz)

Phas

e an

gle,

ϕ (d

eg)　

0pF

2000pF4000pF

fr decreases

Figure 5.5: Measured phase angle shift with capacitance changes in the sensor capacitance using a ceramic condenser: Cx = 0 pF; 2000 pF; 4000 pF.

1.8 2 2.2 2.4 2.6 2.8 386

87

88

89

90

91

Frequency (MHz)

Phas

e an

gle,

ϕ (d

eg)　

3mm2mm1mm

0mm

|Δϕdip| decreases

Figure 5.6: Measured phase angle shift with distance di change between passive sensor and the external antenna: di = 0 mm; 1 mm; 2 mm; 3 mm.

109


0 1 2 3 4 50

0.5

1

1.5

2

88

90

92

94

96

Frequency (MHz)

Impe

danc

e, Z

a (Ω

)

Za ϕ

Phas

e an

gle,

ϕ (d

eg)

Figure 5.7: Measured impedance Za and phase angle φ of the antenna impedance of wireless passive sensor using the radial tire specimen as sensor capacitor Cx.

0 1000 2000 3000 4000-0.008

-0.004

0

0.004

0.008

Strain, με

Res

onan

t fre

quen

cy c

hang

e (M

Hz)

Loading 1 Unloading 1 Loading 2 Unloading 2

Figure 5.8: Measured resonance frequency change of wireless passive sensor and strain measured using attached strain gage due to tensile loading tests: first loading () and unloading (); second loading () and unloading (∆).

110

Chapter 6 Passive wireless monitoring using tuning circuit

Chapter 6 Passive wireless monitoring

using tuning circuit

6.1 Background

This Chapter presents another type of a passive strain measurement method: the method is based on signal filtering using tuning circuit. The transmitted white noise from an external transmitter is picked up with the tuning circuit; the signal is tuned at the frequency depending on the applied strain; the tuned wave is received at an external receiver wirelessly without batteries in the sensor. The method overcomes the drawback owing to electromagnetic induction: short wireless range, and low sampling frequency. A specimen made from a commercially available tire is connected to a tuning circuit comprising an inductance and a capacitance as a condenser. The change in capacitance of the tire alters the tuning frequency. This frequency change allows wireless measurement of the applied strain of the specimen using no external power supply. This novel passive wireless method is applied to a specimen and the static applied strain is measured. However, compared to the radial tire specimen, an actual tire has a large hysteresis between the measured strain of the inner tire surface and the capacitance in a tire belt. The large hysteresis in the actual tire makes it difficult to measure a tire strain precisely. In the Section 6.5, to measure the strain precisely, multiple power spectral features of the sensor output are used to estimate the strain with a statistical method. As the spectral features, a peak power spectrum and a sharpness of the resonance in addition to a tuning frequency are used for the estimating.

111



The method adopts a simplified passive tuning circuit as shown in Fig. 6.1 for sending the capacitance change of the tire to a receiver wirelessly. Figure 6.2 shows a schematic image of the present passive wireless monitoring system. Systems comprise an external transmitter, a strain sensor, and an external receiver. The wireless strain measurement system uses four antennas, including one for the output of the external transmitter, two for the input and the output of the sensor module and one for the input of external receiver. The antennas used are wire type and 150 mm long. The transmitter is employed here to emit radio waves of white noise. It is easily produced using a normal function generator. The transmitted white noise is picked up with the tuning circuit antenna. The tuning circuit comprises the inductance (L in henries) of a coil, the capacitance (Cx in farads) of a tire specimen, and a resistance. This is a pure L–C parallel resonator circuit. The impedance of the resonant circuit, Z, is given as

⎟⎠⎞

⎜⎝⎛ −

=

LCj

Zx ω

ω 11 , (6.1)

where j is 1− , and ω is the radian frequency in hertz. The denominator of Eq. (6.1) is set equal to zero to find the strain-sensor tuning frequency, ft:

01=−

LC x ω

ω . (6.2)

Solving Eq. (6.2), the tuning frequency is given as

xLCf

ππω

21

2t == . (6.3)

Rearranging Eq. (6.3), the capacitance Cx can be determined as follows:

( )2t2

1fL

Cxπ

= . (6.4)

112


This equation shows that the increase in capacitance Cx decreases the tuning frequency when inductance L is fixed. When the applied stress deforms the tire specimen, the tire specimen capacitance changes. Consequently, deviation occurs from the tuning circuit resonance. The tuned radio wave at frequency ft is picked up at the external receiver. Its frequency is calculated by means of fast Fourier transform (FFT). Equation (6.4) shows that measurement of the frequency change indicates the change of the tire’s capacitance, which indicates the tire deformation. Since the antenna at the external receiver receives the signal from the sensor as well as the direct signal from the transmitter, electromagnetic shield is needed between the white noise area and sensor output area. The white noise area includes the output antenna of the external transmitter and the input antenna of the sensor module. The sensor output area includes the output antenna of the sensor module and the input antenna of external receiver. In a practical use, the output antenna of the external transmitter and the input antenna of the sensor are placed inside of the tire, and the output antenna of the sensor and the input antenna of the external receiver are placed outside of the tire such as a tire wheel. The sensor using electromagnetic induction in the previous Chapter for wireless passive communication is apt to be very short radio range, low sampling frequency, and low accuracy of measurement. Since the sensor proposed here uses electric waves instead of electromagnetic induction, the sensor does not have those problems.


6.3.1 Specimens

The specimen employed is taken from a commercially available radial tire. Figure 3.9 shows the specimen configuration. The specimen length, width and thickness are 270, 30 and 4 mm, respectively. The longitudinal direction of the specimen is the circumferential direction of tire. In this specimen, 1.0-mm diameter steel wires are embedded in parallel with 2.5-mm spacing. The fiber angle is about ±20° to the longitudinal direction of the specimen. A conventional strain gage that is designed for rubber is attached to the specimen surface between the two electrodes to measure the applied strain.

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An interdigital electrode has been investigated to enhance the tire capacitance change attributable to applied strain as shown in the Section 3.4.5. Steel wires in the radial tire specimen are used as interdigital electrodes. The number of electrodes used, Nd, is 10. Increasing Nd implies increasing the number of capacitors in the tire specimen, thereby increasing the capacitance change. The value of Nd is set to a maximum limit at 10 in this study to prevent an increase of the sensing area.

6.3.2 Static tension tests

Static tension tests using the sensor are performed to check the feasibility of the wireless passive monitoring system. A micro-inductor of 10 mH is used as the constant inductor of the tuning circuit, L. A tire specimen with interdigital electrodes (Nd=10) is used as the capacitance Cx. A static tensile test is performed at stroke speed of 1.0 mm/min and up to 3 mm of displacement. Unloading is performed after that. Tuning frequency change attributable to the change of the applied tensile strain of the tire is measured during loading and unloading using a digital oscilloscope. A silicone rubber sheet is inserted between the jig and the specimen.

6.3.3 Cyclic loading tests

Since the tuning frequency of the sensor output changes dynamically with the tire deformation, time-frequency analysis is needed to analyze this non-stationary tuning frequency. Time-frequency analysis of a non-stationary signal provides a simultaneous time and frequency domain representation of the signal. However, the FFT, which is most common method for frequency analysis, assumes a stationary analysis target. A frequency function is produced when FFT is performed on a non-stationary signal. It has no information regarding the time. Short-time Fourier transform (STFT) has been developed as a solution for time-frequency analysis. The STFT is based on the assumption that signals are stationary over a short time. The STFT divides the signal into small time segments

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using window function and performs Fourier transforms on each segment of time to derive the spectrum. The power density spectrum, S(t, ω), of a signal s(t) obtained through STFT is expressed as

( ) ( ) ( ) τττω ωτ dentwstS j−−= ∫ ,, , (6.5)

where w(t,n) is a window function centered at time t, and n is an integer indicating data sequence, with values 0 n N-1. N represents the width of a discrete-time window function. The energy density spectrum of the STFT is defined as

≤ ≤

( ) ( ) 2,, ωω tStP = . (6.6)

The length of short-term stationarity determines the frequency resolution. An advantage of the STFT method is that it allows signals of long duration to be captured in small fragments with the assumption of a stationary representation for the short capture duration. The present study uses the following window function w(t, n) for a Hanning window, which is a suitable for the analysis of continuous signals [84]:

( )( )

( )⎪⎩

⎪⎨

⎧

Δ>

Δ≤⎟⎠⎞

⎜⎝⎛

−−

=2/ 0

2/ 1

2cos5.05.0,

tt

ttN

nntw

π. (6.7)

However, the STFT has the indeterminacy principle: increased time resolution decreases the frequency resolution. In this respect, the maximum entropy method (MEM) [85] is known to be a method offering better frequency resolution than FFT. The MEM is effective for frequency measurement at the spectrum peak because it applies a polynomial approximation to the spectrum peak. The MEM also offers the advantage of noise insensitivity. To analyze non-stationary signals, short-time MEM (STMEM) is performed by means of repeating MEM on the assumption that signals are stationary on a short time basis, as is the case with the STFT. Details of STFT and MEM are described in the references (for example, see [86, 87]). Figure 6.3 shows the software window to monitor the tuning frequency in real time. Table 6.1 shows software specifications. The method time-frequency analysis is optional: either STFT or STMEM can be used. Sampling frequency of the input signal is up to 500 Hz; the sampling frequency of the power spectrum is about 100 Hz. This specification allows measurement of the tuning frequency change up to 10 Hz. The cyclic loading test is performed to investigate the feasibility of monitoring change

115


in the tuning frequency using STFT or STMEM. The specimen shown in Fig. 3.9 is loaded cyclically to measure the tuning frequency change wirelessly with the software. Stroke displacement is 3.0 mm, and the stroke frequencies are 0.5, 1, 5, and 10 Hz, which correspond to vehicle velocities of about 3.5, 7, 35, and 70 km/h, respectively. The strain gage is easily damaged because of different stiffness between the tire surface and the strain gage during cyclic loading tests; tire strain is determined using the stroke displacement.


6.4.1 Static tension tests

Figure 6.4 shows the result for power spectra of white noise, sensor output, and a radio wave picked up with the receiver under a no-loading condition. In this figure, the abscissa is the frequency; the ordinate is the measured power spectrum. Although the power spectrum of white noise is constant for a frequency change, the power spectra of the sensor output and the received wave have peaks at the tuning frequency of 100 kHz. This means that only the tuned waves in the white noise are passed through the passive wireless sensor. Figure 6.5 shows results of static tension tests of the specimen using the proposed passive wireless system. In this figure, the abscissa is the applied strain measured using the attached strain gage. The ordinate is the tuning frequency picked up with the receiver. The tuning frequency, ft, is determined from the frequency at a maximum point of the power spectrum obtained with FFT. Static tension tests are performed twice: first loading (solid circle symbol), first unloading (open circle symbol), second loading (solid triangle symbol), and second unloading (open triangle symbol). The measured resonant frequency, fr, decreases with increased tensile strain, as shown in Fig. 6.5, corresponding to an increase of capacitance accompanying an increase in tensile strain, as shown in Fig. 3.18. Results obtained from Fig. 6.5 indicate that the proposed method is applicable for wireless strain measurement of commercially available tires.

116


6.4.2 Cyclic loading tests

Figures 6.6 (a) and (b) show the measured frequency change attributable the cyclic loading at stroke frequencies of 1 Hz and 10 Hz, respectively by means of the STMEM. Because measurement using STFT is sensitive to noise, it is difficult to distinguish the spectrum peak at the tuning frequency from other peaks because of the noise. In these figures, the abscissa is the time and the ordinate is the measured frequency and strain. These figures suggest that the tuning frequency decreases with the increase of the applied tensile strain. This characteristic agrees with the static tensile test shown in Fig. 3.18. From Fig. 6.6 (b), the tuning frequency changes according to the strain change wirelessly at the stroke frequency of 10 Hz. Figure 6.7 shows results of the relationship between the tuning frequency and the applied strain at stroke frequencies of 0.5, 1, 5, and 10 Hz. The tuning frequency and the applied strain mutually correspond. These results indicate that the proposed method is applicable for wireless strain measurement of commercially available tires.

6.5 Application to an actual tire

6.5.1 Strain measurements using multiple spectral features

In the previous section, we have demonstrated dynamic strain monitoring of tires using a change in capacitance of a radial tire specimen. The structure of an actual tire is, however, three-dimensionally layered with a carcass, a belt, and a tread. Moreover, in the actual tire, a compressive load is applied to a tire due to contacting a road surface. It causes a bending load to an actual tire surface unlike the tensile test using a radial tire specimen. Compared to the radial tire specimen, the actual tire has a large hysteresis between a strain of the inner tire surface measured by means of an attached strain gage and the capacitance in the tire belt. The hysteresis is caused by following reasons: the deformation paths in loading and unloading are different from each other due to the complexity of the actual tire structure; the viscoelastic belt in the actual tire

117


deforms delaying against the deformation of an elastic strain gage, while the deformation of the thin tire specimen does not delay because it behaves like an elastic material due to the adhesion of a strain gage. The large hysteresis in the actual tire makes it difficult to measure a tire strain precisely because the each capacitance value corresponds to two different strain values. Since the tire rubber is not pure dielectric material and acts also as an electrical resistor [88], it can be assumed as a capacitance-resistance parallel model. To measure a tire strain precisely, we utilize multiple kinds of variables: the electric resistance and the capacitance of an actual tire. The values of the multiple variables enable us to know whether the condition is loading or unloading, and it is possible to measure the strain precisely. In the Section 6.5, the tuning circuit is also employed to transmit the electric resistance and the capacitance data wirelessly to an external receiver. To measure the tire strain precisely and wirelessly, multiple spectral features of the sensor output instead of the electric resistance and the capacitance are used for estimating the strain with a statistical method. As the spectral features, a peak power spectrum and a sharpness of the resonance in addition to the tuning frequency are used for the estimating. The tuning frequency and the peak power spectrum reflect the capacitance and the electric resistance, respectively; the sharpness of the resonance reflects both the electric resistance and the capacitance. Although the resonance point features like the tuning frequency and the peak power spectrum are easily affected by an environmental noise, the sharpness of the resonance is not affected because it is measured using whole power-spectrum shape. Using these multiple spectral features and a statistical method, the proposed sensor system is applied to a commercially available tire, and the applicability of the proposed sensor is examined.

6.5.2 Tuning frequency and peak power spectrum

The transmitter is also employed here to emit radio waves of white noise in this Section. The transmitted white noise is picked up with the tuning circuit antenna. The tuning circuit comprises the inductance (L in henries) of a coil, the tire electric resistance (R in ohms) and the tire capacitance (C in farads). This is a pure LCR

118


parallel resonator circuit. The combined admittance of the resonant circuit, Y, is given as

⎟⎠⎞

⎜⎝⎛ −+=

LCj

RY

ωω 11 , (6.8)

where j is 1− and ω is the radian frequency in hertz. The right side of Eq. (6.8), (ωC−1/ωL), is set equal to zero to find the tuning frequency of strain-sensor, ft:

LCf

ππω

21

2t −== . (6.9)

From Eq. (6.8), the admittance at the tuning frequency, Y0, is given as

RY 1

0 = . (6.10)

The peak power spectrum at the tuning frequency, Pp, is given as

RIYIP 2

0

2

p == , (6.11)

where I is applied current to the LCR parallel circuit. Equation (6.9) shows that the increase in the capacitance C decreases the tuning frequency ft, and the Eq. (6.11) shows that increase in the electric resistance R increases the peak power spectrum Pp when the inductance L is fixed. When the applied stress deforms the tire specimen, the change in the spacing between steel wires causes the change in the electric resistance and the capacitance of the tire belt. Consequently, deviation occurs from the tuning circuit resonance as the tuning frequency and the peak power spectrum. The tuned radio wave at the frequency ft is picked up at the external receiver. The tuning frequency and the peak power spectrum of the received wave are calculated by means of fast Fourier transform (FFT). Eqs. (6.9) and (6.11) show that measurement of the tuning frequency and the peak power spectrum indicates the changes in the tire’s electric capacitance and the resistance, respectively. Since the antenna at the external receiver receives the signal from the sensor as well as

119


the direct signal from the transmitter, electromagnetic shield is needed between the white noise area and sensor output area. The white noise area includes the output antenna of the external transmitter and the input antenna of the sensor module. The sensor output area includes the output antenna of the sensor module and the input antenna of the external receiver. In a practical use, the output antenna of the external transmitter and the input antenna of the sensor are placed inside of the tire, and the output antenna of the sensor and the input antenna of the external receiver are placed outside of the tire such as a outer surface of the wheel.

6.5.3 Quality factor

The sharpness of the resonance is widely expressed as quality factor Q. The quality factor is defined by the half power bandwidth (HBW) method otherwise known as the 3 dB method [89]. The half power points are points where the amplitude response is reduced by 0.707 of its peak value or at which the power drops to 3 dB level. The quality factor is expressed as

12 fffQ t

−= , (6.12)

where f1 and f2 are frequencies when the voltage drop E is the half value of the E0 at the tuning frequency. Since the voltage drop E is in inverse proportion to admittance Y, following equation is obtained:

2

2

0

0 11

1

⎟⎠⎞

⎜⎝⎛ −+

==

LC

RR

YY

EE

ωω

. (6.13)

Since the fraction E/E0 is 1/2 when the frequency is equal to f1 or f2, the frequency f1 and f2 are the root of following equation:

12

12 ±=⎟⎟⎠

⎞⎜⎜⎝

⎛− R

fLfC

ππ . (6.14)

Solving Eq. (6.14) for frequency f, the frequency f1 and f2 are obtained as

120


( )22

1

2 44

1 LCRLLLCRf

f++±=

π. (6.15)

From Eqs. (6.9), (6.12) and (6.15), the quality factor Q of the parallel LCR resonance is expressed as

LCRQ = . (6.16)

Equation (6.16) indicates that the quality factor Q is in proportion to the electric resistance and square root of the capacitance of the tire. The strain measurement of the tire is conducted by using the changes in the electric resistance and the capacitance due to the deformation of the tire steel belt. The change in the tuning frequency corresponds to the change in the capacitance of the tire; the change in the peak power spectrum corresponds to the change in the electric resistance. Thus, monitoring the tuning frequency and peak power spectrum enables us to know the electric resistance and the capacitance of the tire. To improve the accuracy of estimation, the quality factor is added to the measurement data because the quality factor is measured by using the whole spectrum figuration, and more stable than the tuning frequency or the peak power spectrum. The applied strain of the tire, y, is estimated using three measurement data, ft, Pp and Q instead of C and R as

).,,(),(

pt QPffRCgy

==

(6.17)

When multiple channels of N are demanded, the required number of sensors is N in this case, but only a single receiver is needed. To distinguish each output signal, each sensor has to use different initial tuning frequencies from each other: f1, f2, …, fN. The power spectrum of the sensor of the number i, Pi, has a peak at the initial tuning frequency fi. The power spectrum P of the received signal with the external receiver is the sum of the power spectrum Pi of each sensor as follows:

( )∑=

=n

ii fPP

1. (6.18)

Since the power spectrum, P, has N peaks at the each initial tuning frequency, fi, measurement of the frequency change of each peak enables us to obtain the electrical resistance changes of the multi-channels when each peak of the initial frequency has enough spacing.

121


6.5.4 Response surface method

For an actual tire, it is very difficult to find the precise model how the electric resistance and the capacitance change due to the deformation of tire because of the complexity of the tire structure. The response surface methodology [90] is adopted as a solver for the prediction of the applied strain to the actual tire. The response surface is a widely adopted tool for quality engineering fields. The response surface methodology brings two advantages: the inverse problems can be approximately solved without consideration of modeling of functions, and the approximated response surface can be evaluated using statistical tools. The tire strain is estimated from measured multiple spectral features such as the tuning frequency, the peak power spectrum and the quality factor using response surface method. The complicated three-dimensional deformation of tire belt is easily approximated statistically. For most of the response surfaces, the functions for the approximations are polynomials because of the simplicity. For the cases of quadratic polynomials, the response surface is described as

∑∑∑∑= >==

+++=k

i

k

ijjiij

k

jjj

k

jjj xxxxy

11

2

10 ββββ , (6.19)

where k is the number of variables. In this study, the tuning frequency, the peak power spectrum and the quality factor are the variables: x1, x2 and x3. The response surface is expressed as

.1393282172

36

225

2143322110

xxxxxxx

xxxxxy

ββββ

ββββββ

+++

++++++= (6.20)

Substituting as x4=x12, x5=x2

2, x6=x32, x7=x1x2, x8=x2x3 and x9=x3x1, Eq. (6.20) becomes

a liner regression model as

∑=

+=9

1i0

ii xy ββ . (6.21)

In the case that the total number of experiments is n, the response surface can be expressed as follows using matrix expression

122


εXβY += , (6.22)

where Y is response vector, X is experimental coordinate, β is coefficient vector and ε is error vector. The unbiased estimator, b, of the coefficient vector β is obtained using a least square error method as follows:

( ) YXXXb TT 1−= . (6.23)

In order to judge the goodness of the approximation of the response surface, the adjusted coefficient of multiple-determination Radj

2 is used as

( )( )1

112adj −

−−−=

nSknSSR

yy

E , (6.24)

where SSE is the error sum of squares, Syy is the total sum of squares. Each coefficient of the response surface can be tested by using t-statistic. When the absolute value of the t-statistics is smaller than the threshold value of t-distribution (t0.025, n−k−1), the coefficient is eliminated from the response surface as a non-significant coefficient to obtain higher Radj

2.


The shoulder part of the tire tread is cut off to measure the electric resistance and the capacitance between the electrodes in the tire belt as shown in Fig. 2.21. The lead wire is soldered to the steel wire of the tire belt and used as electrodes for measurement of the electric resistance and the capacitance. Inter-digital electrodes as shown in Fig. 2.21, therefore, have been developed as the measurement electrodes to increase the change in the capacitance. A conventional strain gage for rubber is attached to the inner surface of the tire for measurement of the tire strain. Appling strain to the tire is performed using a tensile testing machine produced by Shimadzu Corp. at cross head speed of 2 mm/min. The sensor module is produced using the coil inductance of 100 μH as a sensor coil L. The output wave transmitted from a sensor module is picked up at the antenna of an external receiver wirelessly. The received wave is analyzed using a digital oscilloscope produced by LeCroy Corp.

123


The quality factor of the power spectrum is obtained using the frequency f1 and f2 at HBW as shown in Eq. (6.12). The problem in the used of the 3 dB method lies in the basic procedure to find the half power points. The obtained power spectrum is discrete instead of a continuous function, and not necessarily including the tuning frequency or the 3 dB bandwidth. Since this reduces the accuracy of measurement of the tuning frequency, the peak power spectrum or the quality factor, they have to be determined from the measurements by applying digital algorithms. In this study, the nonlinear fitting to a Lorentizian curve is applied to estimate the spectral features precisely and robustly [91, 92]. The Lorentzian function used as approximation function is expressed as

( )( )

22

21

21

1

⎟⎠⎞

⎜⎝⎛ Γ+−

Γ=

μπ

ffP , (6.25)

where Γ is full width at half maximum (FWHM), and μ is the frequency at mean power spectrum. Since this Γ and μ are the constants which have to be adjusted in order to agree with the experimental data, high calculation cost is required. The quadratic polynomial approximation is possible using inverse of Lorentzian function as follows:

( )

( )210

22

2

21

21

1 kfkfkf

fP++=

Γ

⎟⎠⎞

⎜⎝⎛ Γ+−

=μ

π , (6.26)

where k0, k1 and k2 are the coefficients which are easily obtained using least square method without high calculation cost. From Eq. (6.26), the tuning frequency ft, the peak power spectrum Pp and the quality factor Q are calculated as

0

1t 2k

kf −= , (6.27)

0

21

2p 4kkkP −= , (6.28)

124


2120

1

414 kkk

kQ−

−= . (6.29)


Figure 6.8 shows the calculated tuning frequency, the peak power spectrum and the quality factor obtained from Eqs. (6.9), (6.11) and (6.16) using the experimental results of the changes in the electric resistance and the capacitance as shown in Fig. 3.25 (a) and (b). The abscissa is tire strain in the longitudinal direction, and the ordinate is the change ratio in the spectral features, Δf/f0, ΔPp/Pp0 and ΔQ/Q0. The tuning frequency, the peak power spectrum and the quality factor increase with the increase of the applied strain as shown in Fig. 6.8. The figuration of power spectrum, therefore, changes with the applied strain as shown in Fig.6.9: the mean frequency and maximum power spectrum increases and the spectral peak becomes sharper. Figure 6.10 shows the power spectrum of output from a sensor module using the tire belt as a sensor. The abscissa is the frequency and the ordinate is power spectrum. In the actual monitoring, the spectrum is swept from 450 kHz to 625 kHz, and the tuning frequency, the peak power spectrum and the quality factor are obtained from the power spectrum using Lorentzian curve fitting in Eq. (6.26) as shown in Fig. 6.10. Figures 6.11, 6.12 and 6.13 show the wirelessly measured tuning frequency, the peak power spectrum and the quality factor due to the loading and unloading tests using an actual tire. The abscissa is measured strain and the ordinate is the change ratio in the spectral features, Δf/f0, ΔPp/Pp0 and ΔQ/Q0. The tuning frequency decreases until the strain of 1000 μ and increases after 1000 μ. Since the tuning frequency does not change linearly to the applied strain, it is difficult to estimate the applied strain using only tuning frequency data. The peak power spectrum and the quality factor change monotone increasing. Figures. 6.11, 6.12 and 6.13 agree with the calculated results as shown in Fig. 6.8. These results confirm that the changes in the tuning frequency, the peak power spectrum and the quality factor are caused by the changes in the electric resistance and the capacitance of a tire.

125


Using the relationship between the spectral features and the applied strain, multiple regress function is obtained using response surface method. High accurate estimation is possible using three variables over the large strain range. Figure 6.14 shows the estimation results substituting the tuning frequency, the peak power spectrum and the quality factor to the multiple regression model. The abscissa is the measured strain by means of a strain gage, and the ordinate is the estimated strain using response surface method. In this figure, the open circle symbols indicate experimental data used for making the regression model, and the solid triangle symbols show new experimental data. Since the Radj

2 is 0.974, the accuracy of the regression is high. The new experimental data is among the error band of 500 μ strain. Since the strain of the tire neither be measured nor obtained in the commercially available automobile, this estimation accuracy is enough useful for improving the automobile control. Table 6.2 shows the accuracy of estimation using the other variable sets as the response surface. The column shows the Radj

2 that response surface is made from only loading data, only unloading data or both loading and unloading data. From table 1, the highest accurate estimation is obtained using three variables of ft, Pp and Q.

6.6 Summary

The tire is connected to a simple tuning circuit as a condenser. Thereby, the capacitance change of the tire is converted to a tuning frequency change of the LC resonant circuit. Since the tuning circuit performs as a frequency filter, the tuning frequency of the sensor can be wirelessly measured without any batteries to the sensor circuit. The method is demonstrated using a rectangular specimen cut from a commercially available radial tire. In order to improve the accuracy of strain measurements, the electric resistance and the capacitance parallel circuit is adopted for the tire electrically equivalent model. The sensing system uses the changes in the tuning frequency, the peak power spectrum and the quality factor as variables of the response surface method for the improved estimation of the applied tire strain. The following results were obtained: 1. The radial tire specimen is connected to the sensor circuit as a condenser. The

tuning frequency of the sensor circuit is measured wirelessly without any power supply to the sensor circuit.

126


2. The proposed passive wireless strain-measurement method is demonstrated

experimentally with static tension tests and cyclic loading tests. The tuning frequency of the sensor circuit decreases with the increase of tensile loading even at high stroke frequency of 10 Hz.

3. The tuning frequency, the peak power spectrum and the quality factor of the output

sensor signal increase with the applied strain. The tuning frequency corresponds the capacitance, and the peak power spectrum corresponds the electric resistance.

4. Using the spectral features of the tuning frequency, the peak power spectrum and

the quality factor, the tire strain is estimated accurately using response surface method.

127


Figure 6.1: Photograph of the tuning circuit consisting of a resistor, an inductor, and capacitor as the sensing target.

External Transmitter(Function generator)

External Receiver(Digital oscilloscope)

Strain Sensor

L C (Tire) R (Tire)

R

White noise

Tire deformation

Electric resistance andcapacitance change

Power spectrum change

Power spectrum change

FFT analysis

Strain measurement

RF RF

Figure 6.2: Schematic illustration of the wireless passive strain-measurement system using tuning circuit.

128


Strain change

STFT (STMEM) result(Time-frequency plane)

Tuning frequency change

FFT (MEM) result

Strain change

STFT (STMEM) result(Time-frequency plane)

Tuning frequency change

FFT (MEM) result

Figure 6.3: Software for real-time spectrum monitoring. The software calculates tuning frequency of the strain sensor using short-time maximum entropy method in real time.

Table 6.1: Specification of the real-time spectrum monitoring software.

Sampling Frequency of Voltage 1–500 Hz Frequency Range 0–250 Hz Spectrum Analysis Method STFT, STMEM Spectrum Sampling Frequency –100Hz

129


10 100 1000-50

-40

-30

-20

-10

0

Frequency (kHz)

Pow

er sp

ectru

m (d

Bm

) White noise

Sensor output

Received RF

Figure 6.4: Measured power spectrum of white noise, the tuning sensor output and received RF at a remote receiver.

0 1000 2000 300080

85

90

95

100

105

Strain, με

Freq

uenc

y (k

Hz)


Figure 6.5: Tuning frequency change and applied tensile strain to the radial tire specimen using an inductor of 10 mH.

130


140

145

150

155

160

165

170

0 1000 2000 3000 4000 5000Time (ms)

Freq

uenc

y (k

Hz)

-1000

0

1000

2000

3000

4000

5000

6000

7000

Stra

in (

)

Strain

Frequency

Stra

in, μ

ε

(a) Stroke frequency: 1 Hz

140

145

150

155

160

165

170

0 100 200 300 400 500

Time (ms)

Freq

uenc

y (k

Hz)

-1000

0

1000

2000

3000

4000

5000

6000

7000

Stra

in (

)

Frequency

Strain

Stra

in μ

ε

(b) Stroke frequency: 10 Hz

Figure 6.6: Tuning frequency change of the tuning sensor using inductor of 10 mH and applied tensile strain change under dynamic loading tests: (a) stroke frequency of 1 Hz, (b) stroke frequency of 10 Hz.

131


0 1000 2000 3000155

160

165

170

Strain, με

Tuni

ng F

requ

ency

(kH

z) 0.5 Hz 1 Hz 5 Hz 10 Hz

Figure 6.7: Measured relationships between tuning frequency of the tuning sensor using an inductor of 10 mH and applied tensile strain of the rectangular radial tire specimen at various cyclic frequencies: 0.5 Hz; 1 Hz; 5 Hz; 10 Hz.

132


0 500 1000 1500 2000-0.05

0

0.05

0.1

0.15

Strain, με

Δft/f

t0, Δ

P p/P

p0, Δ

Q/Q

0

Δft/ft0 (Loading) Δft/ft0 (Unloading) ΔPp/Pp0 (Loading) ΔPp/Pp0 (Unloading) ΔQ/Q0 (Loading) ΔQ/Q0 (Unloading)

Figure 6.8: Calculated Δft / ft0, ΔPp / Pp0 and ΔQ/Q0 from Eqs. (6.9), (6.11) and (6.16).

133


Frequency

Pow

er sp

ectru

mLoading

Tuning frequency, ft

- Mean frequency increases- Maximum spectrum increases- Spectrum peak becomes sharper

Peak power spectrum, Pp

Frequency

Pow

er sp

ectru

mLoading

Tuning frequency, ft

- Mean frequency increases- Maximum spectrum increases- Spectrum peak becomes sharper

Peak power spectrum, Pp

Figure 6.9: Schematic illustration of the changes in the power spectrum figuration due to tensile loading.

0 500 1000 1500 2000 2500-70

-60

-50

-40

-30

-20

-10

Pow

er S

pect

rum

(dB

m)

ft = 550kHz

400 500 600-55

-50

-45

Frequency (kHz)

Experimental value Approximated curve

Figure 6.10: Measured power spectrum of wirelessly received signal at external receiver. The spectrum curve ( — ) obtained using a digital oscilloscope is approximated using inverse of Lorentzian function ( - - - ).

134


0 500 1000 1500 2000-0.004

-0.002

0

0.002

0.004

Strain, με

Δft /

f t0

Loading 1 Unloading 1 loading 2 Unloading 2

Figure 6.11: Measured relationship between the change in the tuning frequencies and the strain due to tensile loading: first loading () and unloading (); second loading () and unloading (∆).

0 500 1000 1500 2000

0

0.05

0.10

Strain, με

ΔPp /

Pp0


Figure 6.12: Measured relationship between the change in the peak power spectrum and strain due to tensile loading and unloading.

135


0 500 1000 1500 2000-0.05

0

0.05

0.10

Strain, με

ΔQ/Q

0


Figure 6.13: Measured relationship between the change in the quality factor of resonance and strain due to tensile loading and unloading.

136


0 1000 2000 3000 4000

0

1000

2000

3000

4000

Strain, με

Estim

ated

stra

in, μ

ε

Used data for regression New data

Figure 6.14: Estimated strain using three spectral features and response surface methodology and measured strain of tire obtained using a strain gage. The circle symbols show used data for regression of the response surface and the triangle symbols show new experimental data.

137


Table 6.2: Comparison of Radj2 using different parameter.

Loading Unloading Loading and unloading ft -0.043 0.502 0.292 Q 0.781 0.927 0.708 Pp 0.791 0.909 0.806

ft and Q 0.955 0.961 0.930 ft and Pp 0.977 0.982 0.919 Q and Pp 0.791 0.981 0.730

ft, Q and Pp 0.996 0.998 0.974

138

Chapter 7 Obtaining tire configurations and applied forces using deformation data

Chapter 7 Obtaining tire

configurations and applied forces using

deformation data

7.1 Background

This Chapter investigates the relationship between strain data obtained from proposed sensors and tire configurations, such as effective radius, contact patch length and braking torques. Effective radius is the key parameter in that it enables one to estimate the slip ratio of tires. There are several ways to define slip ratio [53, 93], but this thesis adopts the global definition used by Society of Automotive Engineers (SAE) to describe the ABS mechanism. SAE defines slip ratio as:

VrV

VV es ωϑ −

== , (7.1)

where Vs is tire slip speed, ω tire angular velocity, re effective rolling radius and V vehicle (or tire) absolute velocity. The absolute velocity of a driven wheel is computed from the velocities of the two non-driven wheels and geometric relations in a straightforward manner:

ff

e1rr

ωω

ϑ −= , (7.2)

where rf and ωf are the radius and angular velocity of a free rolling tire, respectively.

139


The friction coefficient μ is defined by the normal force (wheel load), FN, divided by the longitudinal tire force (driving/braking force), Ft, as follows:

N

t

FF

=μ (7.3)

The slip ratio becomes positive where the tractive force Fx is positive and vice versa. From Eq. (7.1), it is found that slip ratio ϑ becomes 1 when the tire is completely locked (i.e. ω=0), while a zero slip ratio indicates perfect rolling without slipping. Figure 7.1 [55] shows a typical curve of the frictional coefficient versus slip ratio. It can be seen from the curve that the peak friction coefficient occurs at a slip ratio equal to 0.15, regardless of road conditions (dry, wet, snowy or icy). Therefore, driving and braking with the slip ratio constant at 0.15 enables one to perform optimal tire control with maximum driving and braking torque. Referring to the definition of slip ratio in Eq. (7.1), the 0.15 slip ratio can be numerically implemented by specifying the tire slip velocity Vs =reω, such that it becomes 85% of the vehicle (or tire) velocity V. Since the effective rolling radius re serves as an alternative measure of slip ratio of a rolling tire, it can be concluded that the effective radius measurements will allow the performance of optimal control with maximum braking torque. In this Chapter, FEM analysis are performed on tire deformation under various wheel loads and braking torques, and the relationship between strain distribution along the sensor sampling point, circumferential strain at the center of the inner tire surface and tire configurations calculated. Since driving and braking torques alter the strain distribution between the tire contact points, the braking and driving torque are also estimated using the strain sensor. Thus, the effects of braking torque on the strain distribution of the tire are investigated. Finally, we address the utilization model of the strain data from the proposed tire sensor for improving tire safety.

140


7.2 Obtaining tire configurations and applied forces

7.2.1 Finite element analysis

To examine the relationship between tire strain distribution and other tire properties, such as effective length, braking torque, etc., calculations are performed using the FEM application program, ANSYS ver. 10. A representative automobile tire 195/60R14 is considered. Figures 7.2 and 7.3 show the three-dimensional FEM model, where ANSYS SOLID186, TARGET170, CONTACT174 and MPC184 elements are employed. SOLID186 is a 3-D 20-node solid element that exhibits quadratic displacement behavior. CONTACT174 is a 3-D 8-node surface-to-surface contact element, which is used to represent contact and sliding between 3-D “target” surfaces (TARGE170) and a deformable surface, defined by this element. The contact elements themselves overlay the solid elements describing the boundary of a deformable body and are potentially in contact with the target surface, defined by TARGE170. The MPC184 element restricts the kinematic constraints at the rim nodes and represents the wheel loads and braking torque. The total number of elements is 4904 and there are 20 823 nodes. Modern pneumatic tires consist of a specific combination of rubber compounds, cord and steel belts. The main components are the body, sidewall, beads and tread. The finite element model of Holscher et al. [94] is used as reference for set-up. The simulation procedure includes the following features: Specific behavior of rubber: Rubber shows a nonlinear stress–strain relationship and, furthermore, is incompressible. This behavior is well described by the Mooney–Rivlin function, with the strain-energy density function W defined by [93]:

( ) ( ) ( ) ( )23

1201110321 1133,, −+−+−= J

DJCJCJJJW , (7.4)

where C01 and C10 are material constants characterizing the deviatoric deformation of the material and determined experimentally. D1 is the parameter associated with material incompressibility, and J1, J2, and J3 are invariants of the Green–Lagrangian

141


strain tensor. Three material constants are related to the shear modulus τ and the bulk modulus κ as follows:

20110τ

=+ CC , (7.5)

κ21 =D . (7.6)

Then, one can derive the next relation for determining the Poisson ratio:

2623

+−

=τκτκv , (7.7)

( )0110

1

4 CCD+

=τκ . (7.8)

Since ν approaches 0.5 as D1 increases for a given C10 and C01, the incompressibility of rubber-like hyperelastic materials can be asymptotically enforced. Mooney constants used are listed Table 7.1 [94, 95]. Constant inflation pressure: The inflation pressure acts perpendicular to its inner surface. This effect is well described by the pressure option of ANSYS: a constant surface force is added to every element at the inner surface of the tire and the direction of this force is kept perpendicular to the surface during each solution increment. An inflation pressure of 200 kPa is adopted in the finite element analysis. Coulomb friction at tire/street contact: The ground contact of the tire complicates the finite element model since contact and friction problems are highly nonlinear. The contact problem in the FEM model is described by the option of a deformable body (tire) to a rigid body (street). The friction is modeled by the algorithm in ANSYS; the coefficient of kinetic friction between tire and road is set to 0.3. Tire rotation: The tire rotation is performed by a discrete ration of the rim for each increment. Symmetry: It is assumed that the tire rolls straight forward along the roadway. In this case, there are no additional lateral forces. Therefore, the simple symmetry of the tire is used and only one half of the pneumatic tire is modeled by the finite element model. The nodes at the symmetry lane are perpendicularly fixed to it.

142


The major numerical difficulties are the geometric non-linearities due to the large deformations, the incompressibility of elastomers and the specific boundary conditions of the tire/street contact. Since these increase the running time of simulation, the finite element tire model is simplified in that composite structure, including the bead wire or steel belt layer, are modeled as three different material parts: tread, shoulder, bead, so that a simulation run could be calculated in a realistic time. Since the tire is strongly deformed in the contact area between tire and road, more elements must be used in this region. To obtain a finer grid in the contact areas, the tire is split into sections with elements of different size and angle. The sector in contact with the road contains elements covering 1.25° of the tire circumference. The angle of the other sector is 5° since this part of the tire is only slightly deformed. Tire loading with an external force is possible by the introduction of a special node, from now on referred to as the central node (Cn) [96, 97]. It belongs to no finite element and its coordinates are (0, 0) (see Fig. 7.4). The degrees of freedom of each bead node are constrained to be dependent on the degrees of freedom of the central node with a multipoint constraint element MPC184 in ANSYS. First, a numerical analysis is carried by applying internal pressure and vertical wheel loads using the central node (Fig. 7.4). After a nonlinear calculation with air pressure and vertical wheel loads, the driving or braking torque is applied to the central node if it is considered. The strain in the circumferential direction at the center of the inner surface of the tire, as shown in Fig. 7.3, is calculated and the relationship between the obtained strain and tire deformation is considered.

7.2.2 Contact patch length and effective radius

Figure 7.5 shows the tire configurations when various wheel loads are applied. The abscissa is x and the ordinate y, as shown in Fig. 7.4. The origin of x- and y-axes is defined at the tire center under no loading. As the wheel load increases, the contact patch length increases and the effective radius decreases. Figure 7.6 shows the strain distributions on the inner surface of the tire when various vertical wheel loads are applied. The abscissa is rotational angle θ, where the contact point is set to zero, as shown in Fig. 7.4, while the ordinate is the calculated strain at the inner surface in the circumferential direction or simulated sensor signal. As can be seen, the tensile strain

143


occurs at the contact point, θ = 0, while compressive strain occur at the periphery of the contact point, around θ = ±10. The tensile strain is due to bending of the circular tire at the contact point; tensile strain arises at the inner surface. Compressive strains at the periphery are due to the compressive effect of the tensile strain area, as discussed in Section 4.4.3. Morinaga et al. [98] proposed an estimation method for contact patch length by calculating the waveform of time derivative of strain: two peaks of the waveform are the points with the highest deformation speed and are edges of the contact patch. Figure 7.7 shows the waveform of the time derivative of strain; the distance between two peaks corresponds to contact patch length. The precise contact patch length can also be calculated using analytical results of FEM. Figure 7.8 shows the relationship between contact patch length obtained using FEM (abscissa) and strain data (ordinate). The solid line indicates the ideal estimation. It shows that there is clear relationship between the contact patch lengths obtained using FEM and strain data; the estimation results (open circles) are close to the ideal estimation (solid line). Therefore, the contact patch length, one of the most important indicators of tire deformations, can be estimated in service using the proposed strain sensor. Contact patch length enables one to know the overall tire figuration. There is also a relationship between contact patch length and effective radius. Increasing contact patch length indicates larger deformations of a tire; the center of the circular tire becomes lower, resulting in a reduction in the effective radius. Figure 7.9 shows the relationship between contact patch length and effective radius. It clarifies the fact that there is negative linear relation between these two values. From the definition of slip ratio in Eq. (7.2), the slip ratio can be numerically calculated using the obtained effective radius.

7.2.3 Wheel loads

It is also found from Fig. 7.6 that, as the applied wheel loads increase, the compressive strain increases monotonically. On the other hand, the tensile strain increases up to 500

144


N but decreases at 1000 N. This is because the tire is in complete contact and bent at the contact point due to the load of 500 N, but bending does not increase at larger wheel loads. Figure 7.10 shows the maximum compressive strain at various wheel loads. The abscissa is maximum compressive strain and the ordinate is wheel load. The maximum compressive strain increases as the wheel loads increase. Therefore, the applied wheel loads can be estimated using maximum compressive strain.

7.2.4 Driving and braking torques

When the driving or braking torque is applied, the ratio between forward and backward compressive strains changes. The braking torque increases the compressive strain at the backward area εb, where the rotation angle θ is positive, and decreases the compressive strain at the forward area εf, as can be seen in Fig. 7.4. The compressive strain ratio between backward and forward is defined as:

pb

pffb εε

εε+

+=r , (7.4)

where εp is the strain due to inflation pressure. Figure 7.11 shows the calculated strain variations of the sensor at braking torques of 0, 144 and 342 Nm. As braking torque increases, the backward compressive strain increases, while the forward compressive strain decreases. Figure 7.12 shows the ratio between the backward and forward compressive strain of the contact point, rfb. due to braking torque changes. The abscissa is compressive strain ratio and the ordinate is applied braking torque. As the applied torque increases, the compressive strain ratio increases linearly, which means the backward strain decreases (or compressive strain increases) and forward strain increases. Therefore, the braking torque can also be measured using the compressive strain ratio obtained from the strain curve of the sensor. Using the ratio between braking/driving torque and wheel loads, the friction coefficient between the tire and road surface is measurable. Using the friction coefficient and slip ratio obtained from effective radius measurements, one can represent the slip ratio/friction curve or slip slope curve, as shown in Fig. 7.1. Then, measurements of the

145


slip slope curve enable one to optimized vehicle control by keeping the slip ratio constant at the maximum friction coefficient. Implementation of a road condition warning system is also possible. This would warn a driver if the recorded friction coefficient at a certain slip ratio is lower than the reference value previously measured under dry road conditions.

7.3 Summary

In this Chapter, we investigated the relationship between variations in strain on the inner surface of pneumatic tires and mechanical parameters, such as contact patch length, effective radius, wheel load and braking torque. The calculations have been carried out using finite element analysis and simulate the strain sensor signal when a tire rotates. The following results were obtained: 1. Contact patch length is estimated using the distance between two time derivative

peaks of strain data. The contact patch length leads to the effective radius; it enables one to obtain slip ratio.

2. Wheel load can be estimated using maximum compressive strain and braking

torque is estimated using the compressive strain ratio. The friction coefficient can be obtained from the ratio of those two values.

3. It is suggested that optimized braking control and road condition warning systems

use strain data. Optimized braking control is performed by keeping the slip ratio constant. The road condition warning system would be actuated if the recorded friction coefficient at a certain slip ratio is lower than a reference value for safe road conditions.

146


Figure 7.1: Wheel slip versus friction coefficient ratio for various road condition: dry, wet, snowy and icy. After [55].

147


Figure 7.2: Finite element model of passenger automobile tire. The blue element uses the material constant of tread in Table 7.1; the purple element is sidewall; the red element is bead.

Strain sensor

Figure 7.3: Contact area between tire and road surface (half and cut mode). The strain sensor is attached to the middle right on the inner surface of the tire.

148


Table 7.1: Material properties of the Mooney–Rivlin model used in finite element analysis.

Rubber material C10 C01 ν Bead filler 14.14 MPa 21.26 Mpa 0.45 Sidewall 171.8 kPa 830.3 kPa 0.45 Tread 806.1 kPa 1.805 MPa 0.45

re R

V

Tb

Fw

ω

x

y

0º

90º-90º

180º

Cn

Figure 7.4: Tire deformation at wheel load Fw and braking torque Tb.

149


-0.15 -0.1 -0.05 0 0.05 0.1 0.15-0.33

-0.32

-0.31

-0.30

-0.29

-0.28

x direction (m)

y di

rect

ion

(m)

0 N100 N

250 N

500 N

1000 N

Contact patch

Figure 7.5: Tire deformation at wheel loads of 0, 100, 250, 500 and 1000 N.

-20 -10 0 10 20-0.02

-0.01

0

0.01

0.02

Rotation angle (degree)

Stra

in

1000 N500 N

250 N

100 N

Figure 7.6: Strain and rotation angle at wheel loads of 100, 250, 500 and 1000 N.

150


-20 -10 0 10 20-0.01

0

0.01


Tim

e D

iffer

entia

l

Contact Patch (100 N)

Contact patch (1000 N)

Figure 7.7: Time derivative of strain at wheel loads of 100 N () and 1000 N (). Two peaks of the waveform are edges of the contact patch.

0 0.05 0.1 0.150

0.05

0.10

0.15

Contact patch length using FEM (m)

Estim

ated

con

tact

pat

ch le

ngth

(m)

Figure 7.8: Relationship between contact patch lengths obtained using FEM and estimated from strain data. The solid line indicates the ideal estimation.

151


0 0.05 0.1 0.150.25

0.26

0.27

0.28

0.29

0.30

Contact patch length (m)

Effe

ctiv

e ra

dius

, re (

m)

Figure 7.9: Effective radius versus contact patch length obtained using strain data.

152


0 0.01 0.02 0.030

500

1000

1500

2000

2500

Maximum compressive strain

Whe

el lo

ad (N

)

Figure 7.10: Relationship between wheel load Fw and maximum compressive strain.

153


-20 0 20 40

-0.04

-0.02

0

0.02

0.04


Stra

in

F=500N, T=0

F=500N, T=144Nm

F=500N, T=342Nm

Figure 7.11: Strain distribution when braking torque is applied at 0, 144 and 342 Nm. Wheel load Fw is set to 500 N.

1 1.1 1.2 1.3 1.40

100

200

300

400

Compressive strain ratio, rfb

Torq

ue (N

m)

Figure 7.12: Compressive strain ratio rfb versus applied braking torque Tb.

154

Chapter 8 Conclusions


This thesis examined the feasibility of wireless, in-service strain measurements of automobile tires using capacitance changes. The key technologies developed are: a relatively low stiffness and high elongation sensor, wireless communication without battery and a utilization model of obtained strain data. As discussed in Chapter 1, these technologies are advantageous for improved driving safety and comfort, as well as providing other services for different user groups. Chapter 2 presented the patch-type flexible sensor using ultra-flexible resin. The sensor is made from flexible polyimide substrates and ultra-flexible epoxy resin, which makes the whole structure low in stiffness and high in elongation. The sensor was applied to an automobile tire and compression tests performed. The capacitance of the proposed flexible sensor decreases monotonously due to tensile strain when the offset of the TC sensor is –0.75, and there is no hysteresis due to loading or unloading. Using an amplitude modulation corresponding to the sensor capacitance, the proposed sensor is feasible for wireless strain measurement in tires. Although it is not suitable for strain measurement as the capacitance of the sensor increases with temperature, temperature compensation is possible using a dummy sensor. Chapter 3 presented the basic concept of a self-sensing method utilizing the tire structure itself. The method allows for a more direct strain measurement than with a sensor attached to the inner surface. Since the actual tire structure acts as a sensor, no additional sensor is required. Therefore, there is no debonding of the sensor, even during prolonged service, since there is no stiffness difference between the sensor and tire rubber. The measurement system could be small, lightweight and capable of withstanding harsh conditions. The electrical properties, especially capacitance changes, of tires were also investigated. Three types of specimens were used: a truck/bus tire specimen, a radial tire specimen

155


and an actual radial tire for a saloon car. Conducting tensile and compressive tests on an actual tire, the capacitance of the truck/bus tire specimen decreases as the tensile strain is applied, while the capacitance of the radial tire specimen increases. The variation in the capacitance change characteristics is due to the different steel wire alignments in the tire belts of truck/bus tires and automobile radial tires. Table 8.1 shows a comparative chart indicating the two proposed sensing methods: a flexible sensor and self-sensing method. The flexible sensor has advantages in that it is simple and easy to attach to existing tires. The self-sensing method does not suffer from sensor-debonding problems and is resilient to environmental changes. At present, the problem is difficulty in manufacture as the method utilizes the steel wires completely embedded inside the tire. Since it is necessary to connect the steel wires to a wireless communication circuit, the current tire-manufacturing process would have to be altered. However, some production methods for intelligent tires have been proposed, where a monitoring device is embedded within the tire during its manufacture [99] (Fig. 8.1). Chapter 4 proposed and experimentally investigated an active wireless strain measurement system using capacitance changes in tires and a small CR oscillating circuit. The frequency of the oscillating circuit varies concomitantly with changes in tire capacitance. Although the CR oscillator requires a battery to activate the circuit, the emitted oscillating frequency is relatively strong and it is easy to receive at long range, as opposed to the amplitude modulation method. A self-temperature-compensated CR oscillator with an NTC thermistor was proposed and proved to be effective. The proposed wireless strain measurement system was demonstrated experimentally with cyclic loading tests at frequencies of 1 and 10 Hz. Static compression and dynamic rotation testing of the proposed method verified it to be applicable to commercially available tires. Chapter 5 proposed a passive wireless method to measure tire strain using electromagnetic induction changes. The tire was connected to a simple LC resonant circuit as condenser and the capacitance changes in the tire was converted to resonant frequency changes of the LC circuit. The resonance frequency can be wirelessly measured without a battery by using electromagnetic induction. The method was applied to a rectangular specimen cut from a commercially available radial tire and investigated experimentally. Low precision in measuring strain, short range and low sampling rate are all problematic.

156


An improved battery-less sensor, using tuning frequency, was proposed in Chapter 6. The method comprises a tire sensor (or tuning circuit), external transmitter and external receiver. Since the tuning circuit performs as a frequency filter, the tuning frequency of the sensor can be wirelessly measured without any batteries to the sensor circuit. The method was demonstrated experimentally with static tension and cyclic loading tests. The tuning frequency of the sensor circuit decreases with increased tensile loading, even at a high stroke frequency of 10 Hz. Using spectral features of the tuning frequency, the peak power spectrum and quality factor, tire strain was estimated accurately using a response surface method. The comparative chart in Table 8.2 shows the four proposed types of wireless data transmitters: amplitude modulation in Chapter 2, CR oscillator in Chapter 4, electromagnetic induction in Chapter 5 and tuning frequency in Chapter 6. Comparing the characteristics of the above wireless methods, wireless communication using tuning frequency is preferable in that it is: battery-less, has a high sampling frequency, low cost and is practical to manufacture. Finally, Chapter 7 showed the utilization model of strain data for the optimized braking control and road condition warning system. The relationships between strain sensor outputs and tire mechanical parameters, including braking torque, effective radius and contact patch length, were calculated using finite element analysis. The optimized braking control and road condition warning systems are also suggested, using strain data. Optimized braking control can be achieved by keeping the slip ratio constant. The road condition warning would be actuated if the recorded friction coefficient at a certain slip ratio is lower than a reference value for safe road condition. A schematic illustration of the proposed system is shown in Fig. 8.2. Through this thesis, significant progress has been made on the development of a wireless, strain measurement system for automobile tires. Conventional problems, which hinder tire deformation and cause sensor debonding due to differences in stiffness, can now be resolved using an ultra-flexible senor and the tire structure itself as a sensor. Another important contribution of this thesis is that a total model of strain utilization is shown for improved tire safety, as well as providing a practical, battery-less, wireless communication system.

157


Table 8.1: Comparative chart showing the two proposed types of strain sensors for tires. Symbols: Excellent; good; fair; poor.

Patch-type sensor Self-sensing Manufacturability Debonding problem Short gage length Capacitance change ratio Lifespan Robustness Low cost Applicability to existing structures Temperature toughness

Patch type sensor Self-sensing- easy to be installed

- applicable to existing tires

- capable of withstanding harsh conditions

- does not cause debonding problems

Improved manufacturing

technology for intelligent tire

Present stage Future stage

Figure 8.1: Present and future stages of the proposed tire strain sensor models.

158


Table 8.2: Comparative chart showing the four proposed types of wireless data transmitters. Symbols: Excellent; good; fair; poor.

Amplitude modulation

CR oscillatorElectromagnetic

induction Tuning

frequency Battery-less Data resolution Robustness Sampling frequency Multiple sensors Low cost Temperature compensation

- -

Radio range Manufacturability

159


Slip ratio

Fric

tion

coef

ficie

nt

Braking pressure control

Strain sensorFlexible patch

Self-sensing

Wireless transmitter

active CR with batteries

passive electro magnetic

passive tuning circuit

Tire Vehicle

External transmitter for passive sensor

External reciver

Strain data

Spectrum analysis using MEM

Response suface methodology

Effective radius Braking/driving torque

Wheel load

Optimized braking/driving control

Road condition warning system

Wheel

Improved drinving safety

Slip ratio Friction coefficient

dry or wet?

Slip slope estimation

Maximum friction?

Figure 8.2: Schematic illustration of optimized braking control and road condition warning systems.

160

Appendix A Circuit diagrams

Appendix A

Circuit diagrams

Figure A.1: Circuit diagram of the receiver described in Section 4.2.

161


Figure A.2: Photograph of the receiver circuit described in Section 4.2.

162


FigureA.3: Circuit diagram of PIC frequency counter described in Section 4.2.

163


Crystal moduleDA converter

Power supplyPIC16F877LCD

Crystal moduleDA converter

Power supplyPIC16F877LCD

FigureA.4: Photograph of PIC frequency counter circuit described in Section 4.2.

164

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173

List of publications


Published papers [P1] Ryosuke Matsuzaki and Akira Todoroki, Wireless strain monitoring of tires using

electrical capacitance changes with an oscillating circuit, Sensors and Actuators A, 119-2 (2005) 323-331.

[P2] Ryosuke Matsuzaki, Akira Todoroki, Hideo Kobayashi, and Yoshinobu

Shimamura, Passive wireless strain monitoring of a tire using capacitance and electromagnetic induction change, Advanced Composite Materials, 14-2 (2005) 147-164.

[P3] Ryosuke Matsuzaki and Akira Todoroki, Passive wireless strain monitoring of tires

using capacitance and tuning frequency changes, Smart Materials and Structures, 14-4 (2005) 561-568.

[P4] Ryosuke Matsuzaki and Akira Todoroki, Passive wireless strain monitoring of

actual tire using capacitance-resistance change and multiple spectral features, Sensors and Actuators A, 126 (2006) 277-286.

[P5] Ryosuke Matsuzaki and Akira Todoroki, Wireless flexible capacitive sensor based

on ultra-flexible epoxy resin for strain measurement of automobile tires, Sensors and Actuators A, (in review).

[P6] Ryosuke Matsuzaki and Akira Todoroki, Wireless intelligent tires for estimating

slip ratio and road friction using longitudinal strain, (to be submitted).

174


Other researches [P7] Ryosuke Matsuzaki and Akira Todoroki, Wireless detection of internal

delamination cracks in CFRP laminates using oscillating frequency changes, Composites Science and Technology, 66, (2006) 407-416.

[P8] Ryosuke Matsuzaki and Akira Todoroki, Wireless strain monitoring of CFRP

laminates using electric resistance change with oscillating circuit, Key Engineering Materials, 324-325 (2006) 1415-1418.

[P9] Ryosuke Matsuzaki, Motoko Shibata, and Akira Todoroki, Evaluation of dimple

treatment for GFRP/metal co-cured joint, Key Engineering Materials, 324-325 (2006) 1729-1732.

[P10] Ryosuke Matsuzaki and Akira Todoroki, Stacking sequence optimization using

fractal branch and bound method for unsymmetrical laminates, Composite Structures, 78 (2007) 537-550.

[P11] Ryosuke Matsuzaki and Akira Todoroki, Time-synchronized wireless strain and

damage measurements at multiple locations in CFRP laminate using oscillating frequency changes and spectral analysis, Composite Structures, (in review).

175


Awards

(1) 自動車技術会大学院研究奨励賞, タイヤの電気容量変化を用いたひずみ

無線モニタリング (2004). (2) 東京工業大学大学院理工学研究科機械物理工学専攻修士論文発表会グッ

ドプレゼンテーション賞, タイヤの電気容量変化を用いたひずみ無線モニ

タリング (2004). (3) 日本材料学会第 34 回 FRP シンポジウム論文賞, 電気抵抗変化法を用いた

CFRP はく離の自己温度補償型ワイヤレスセンサ (2005). (4) 東京工業大学 21 世紀 COE コース（開発プロジェクト）最優秀賞, 多機能

4 足歩行ロボットの開発 (2005). (5) 強化プラスチック協会協会賞（論文賞）, 孔穴処理法による GFRP/金属一

体成形継手の開発 (2005). (6) 日本複合材料学会林学生賞, 拡張フラクタル分枝限定法による非対称積

層構成最適化 (2005). (7) 日本機械学会材料力学部門優秀講演表彰, 電気容量変化を用いたパッチ

型フレキシブルセンサによるタイヤのひずみ測定 (2006).

176


International conferences (1) Ryosuke Matsuzaki, Akira Todoroki, and Yoshinobu Shimamura, Wireless Strain

Monitoring of Tires Using Electric Capacitance Changes, The Japan Society of Mechanical Engineers, Abstracts of International Conference on Advanced Technology in Experimental Mechanics 2003 (ATEM'03), 254, (2003.9.11-12), Nagoya Congress Center (Nagoya, Japan).

(2) Ryosuke Matsuzaki and Akira Todoroki, Wireless strain monitoring of tire using electrical capacitance change of tire, JSME P-SCD33, 2nd Workshop on Structural Health Monitoring, 34-37, (2003.9.19), Tokyo Institute of Technology (Tokyo, Japan).

(3) Ryosuke Matsuzaki, Akira Todoroki, Yoshinobu Shimamura, and Hideo Kobayashi, Passive Wireless Strain Monitoring of Tire using Capacitance and Tuning Frequency change, The Japan Society for Composite Materials, Proceedings of The 4th Korea-Japan Joint Symposium on Composite Materials, 121-122, (2003.10.16), KAIST (Daejeon, Korea).

(4) Ryosuke Matsuzaki and Akira Todoroki, Passive wireless strain monitoring of tire using capacitance change, SPIE International Symposium Smart Structures and Materials, Proceedings of SPIE Vol. 5394, 239-247, (2004.3.14-18), Town and Country Resort & Convention Center, (San Diego, San Francisco USA).

(5) Ryosuke Matsuzaki and Akira Todoroki, Passive Wireless Strain Monitoring using Capacitance Change for Smart Tire, 2nd European Workshop on Structural Heath Monitoring, 1132-1139, (2004.7.7-9), Amazeum Conference Centre (Munich, Germany).

(6) Ryosuke Matsuzaki, Akira Todoroki, and Hideo Kobayashi, Wireless Strain Monitoring for Smart Tire using Capacitance Change, 1st KAIST-Tokyo Tech Joint Workshop on Fracture, Reliability, and Advanced Materials, 44, (2004.8.5-8), KAIST (Daejeon, Korea).

(7) Motoko Shibata, Ryosuke Matsuzaki, Akira Todoroki, and Hideo Kobayashi, Evaluation of the Strength of CFRP/Metal Joints, 1st KAIST-Tokyo Tech Joint Workshop on Fracture, Reliability, and Advanced Materials, 46, (2004.8.5-8), KAIST (Daejeon, Korea).

(8) Ryosuke Matsuzaki and Akira Todoroki, Passive wireless strain monitoring of tire using capacitance and electromagnetic induction change, 5th Canada-Japan Workshop on Composites, 381-388, (2004.9.6-8), Yamagata University (Yamagata,

177


Japan). (9) Ryosuke Matsuzaki and Akira Todoroki, Passive wireless strain monitoring of tire

using electromagnetic induction, 11th US-Japan Conference on Composite Materials, Damage 17, (2004.9.9-11), Yamagata University (Yamagata, Japan).

(10) Motoko Shibata, Ryosuke Matsuzaki, and Akira Todoroki, Application of Dimple Treatment to GFRP/Metal joint, Proceedings of 2nd Tokyo Tech-KAIST Joint Workshop on Fracture, Reliability and Advanced Materials, 26-27, (2005.8), Tokyo Institute of Technology (Tokyo, Japan)

(11) Ryosuke Matsuzaki and Akira Todoroki, Wireless detection of internal delamination cracks for carbon/epoxy composite using electrical resistance method, American Society for Composites, 20th Annual Technical Conference, CD-ROM No.164, (2005.9.7-9), (Philadelphia, USA).

(12) Ryosuke Matsuzaki and Akira Todoroki, Passive wireless deformation measurement of actual tire using capacitance-resistance change and multiple spectral features, The Japan Society for Composite Materials, Proceedings of The 5th Japan-Korea Joint Symposium on Composite Materials, 83-84, (2005.10.19-21), Ehime University (Ehime, Japan).

(13) Ryosuke Matsuzaki, Motoko Shibata, and Akira Todoroki, Evaluation of dimple geometry in CFRP/Metal co-cured joints, The Japan Society for Composite Materials, Proceedings of The 5th Japan-Korea Joint Symposium on Composite Materials, 69-70, (2005.10.19-21), Ehime University (Ehime, Japan).

(14) Ryosuke Matsuzaki and Akira Todoroki, Temperature-compensated wireless sensor for Detection of Delamination Cracks in CFRP laminates using oscillating frequency changes, 9th Japan International SAMPE Symposium & Exhibition JISSE-9, 1146-1151, (2005.11.29-12.2), Tokyo Big Sight (Tokyo, Japan).

(15) Ryosuke Matsuzaki, Motoko Shibata, and Akira Todoroki, Application of dimple treatment to CFRP/Metal joint, 9th Japan International SAMPE Symposium & Exhibition JISSE-9, 685-688, (2005.11.29-12.2), Tokyo Big Sight (Tokyo, Japan).

(16) Motoko Shibata, Ryosuke Matsuzaki, and Akira Todoroki, Investigation of Bonding Mechanism of Dimple-Treated Co-Cured Joint, Fourteenth International Conference on COMPOSITES/NANO ENGINEERING (ICCE - 14), (2006.7.2-8), Boulder (Colorado, USA).

(17) Ryosuke Matsuzaki and Akira Todoroki, Wireless strain monitoring of CFRP laminates using electric resistance change with oscillating circuit, The International Conference on Experimental Mechanics 2006 (ICEM06), vol.324-325, p.1415-1418, (2006.9.27-29), Hyatt Regency Cheju (Jeju, Korea).

178


(18) Ryosuke Matsuzaki, Motoko Shibata, and Akira Todoroki, Evaluation of dimple treatment for GFRP/metal co-cured joint, The International Conference on Experimental Mechanics 2006 (ICEM06), vol.324-325 p.1729-1732, (2006.9.27-29), Hyatt Regency Cheju (Jeju, Korea).

(19) Ryosuke Matsuzaki, Motoko Shibata, and Akira Todoroki, Metal projection method for reinforcing GFRP/metal co-cured joint, 2006 Asian Pacific Conference for Fracture and Strength (APCFS’06), 283, (2006.11.25-28), International Asia Pacific Convention Center (Hainan Island, China).

(20) Ryosuke Matsuzaki, Stacking sequence optimization using Fractal Branch and Bound method for unsymmetrical composite laminates, International Workshop on Multidisciplinary Design Exploration in Okinawa 2006, 101-104, (2006.12.12-14), Seinenkaikan (Okinawa, Japan).

(21) Ryosuke Matsuzaki and Akira Todoroki, Time-synchronized wireless strain and damage measurements at multiple locations in CFRP laminate using oscillating frequency changes and spectral analysis, Sixth International Conference on Composite Science and Technology (ICCST/6), CD-ROM, (2007.1.22-24), (Durban, South Africa).

(22) Keisuke Kumagai, Akira Todoroki, Ryosuke Matsuzaki, Fordable CFRP structure using partially-flexible composites for morphing wing, 3rd Tokyo Tech-KAIST Joint Workshop for Mechanical Engineering Students in Tokyo, 33-34, (2007.2.14), Tokyo Institute of Technology (Tokyo, Japan).

179


Domestic conferences (1) 轟章, 松崎亮介, 島村佳伸, 小林英男, タイヤの無線モニタリングシステム

の開発 , 日本複合材料学会 , 2003 年度研究発表講演会予稿集 , 65-66, (2003.5.26-27), 早稲田大学国際会議場 (東京).

(2) 松崎亮介, 轟章, 小林英男, 島村佳伸, タイヤの無線ひずみモニタリングシ

ステム , 日本機械学会 , M＆M2003 材料力学部門講演会講演論文集 , 1047-1048, (2003.9.24-26), 富山大学 (富山).

(3) 松崎亮介, 轟章, 島村佳伸, 小林英男, タイヤの電気容量と同調周波数変化

を用いたひずみ無線パッシブモニタリング, 日本複合材料学会, 第 28 回複

合材料シンポジウム講演予稿集, 65-66，(2003.10.23-24), 秋田大学 (秋田). (4) 松崎亮介, センサ不要のスマートタイヤ用無線パッシブひずみモニタリン

グ装置の開発，東京工業大学平成 15 年度ベンチャービジネス推進研究，ベ

ンチャー・ビジネス・ラボラトリー祭り 2003，28-31, (2003.12.18) 東京工業

大学 (東京). (5) 松崎亮介, 轟章, 島村佳伸, 小林英男, タイヤの電気容量と同調周波数変化

を用いたひずみ無線パッシブモニタリング, 日本機会学会, 関東支部創立 10周年記念関東支部第 10 期総会講演会講演論文集, 205-206, (2004.3.4-6), 工学

院大学 (東京). (6) 轟章, 松崎亮介, タイヤ用無電源無線ひずみモニタリング，材料力学学会，

第 33 回 FRP シンポジウム講演, 277-281, (2004.3.17-19), キャンパスプラザ京

都 (京都). (7) 松崎亮介, 轟章, スマートタイヤ用無電源無線ひずみモニタリング，複合材

料学会，2004 年度研究発表講演会予稿集, 137-138, (2004.5.24-25), 慶応大学

創想館 (東京). (8) 松崎亮介, 轟章, スマートタイヤ用無線パッシブひずみモニタリング, 日本

機会学会 , M&M2004 材料力学カンファレンス講演論文集 , 311-312, (2004.7.21-23), 秋田大学 (秋田).

(9) 松崎亮介, 轟章, 電気抵抗変化と発振周波数変化を用いた CFRP 積層板の無

線はく離検出, 日本複合材料学会, 第 29 回複合材料シンポジウム講演要旨

集, 61-62, (2004.10.28-29), 沖縄青年会館 (沖縄). (10) 松崎亮介 , 柴田元子 , 轟章 , Effect of Surface Roughness on Strength of

CFRP/Aluminum Joints, 日本複合材料学会, 第 29 回複合材料シンポジウム講

演要旨集, 83-84, (2004.10.28-29), 沖縄青年会館 (沖縄). (11) 松崎亮介, 轟章, 電気抵抗変化法を用いた CFRP 積層板の無線はく離検出,

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日本機械学会, M&M 信州スプリングシンポジウム, 93-96, (2005.3.14-15), 公立学校共済浅間温泉みやま荘 (長野).

(12) 松崎亮介, 轟章, 電気抵抗変化法を用いた CFRP はく離の自己温度補償型ワ

イヤレスセンサ , 日本材料学会 , 第 34 回 FRP シンポジウム (JSMS COMPOSITE-34), 173-177, (2005.3.17-19), 同志社大学 (京都).

(13) 松崎亮介, 柴田元子, 轟章, 島村佳伸, CFRP／金属接着継手へのディンプル

処理法の適用 , 日本材料学会 , 第 34 回 FRP シンポジウム (JSMS COMPOSITE-34), 214-218, (2005.3.17-19), 同志社大学 (京都).

(14) 松崎亮介, 多機能 4 足歩行ロボットの開発, 第 1 回 21 世紀 COE8 大学拠点合

同シンポジウム, ポスター, (2005.3.19), 早稲田大学 (東京). (15) 松崎亮介, 轟章, 複数スペクトル特徴量を用いた実タイヤのひずみ無線パッ

シブモニタリング, 日本複合材料学会, 2005 年度研究発表講演会, 173-174, (2005.5.23-24), 東京工業大学 (東京).

(16) 松崎亮介, 轟章, 電気抵抗変化を用いた CFRP はく離のワイヤレスセンサ, 日本材料学会, 複合材料部門委員会講演, (2005.729), 大阪市立大学 (大阪).

(17) 松﨑亮介, 轟章, 積層構成最適化のフラクタル分枝限定法の非対称積層への

拡張, 日本機械学会, 第 15 回設計工学・システム部門講演会, 05-27, 45-48 (2005.8.03-05), 北海道大学 (北海道).

(18) 松崎亮介, 柴田元子, 轟章, 島村佳伸, ディンプル処理法による CFRP/金属

継手強度に及ぼすディンプル形状の影響, 日本機械学会, 2005 年度年次大会 (MECJ-05), 315-316, (2005.9.19-22), 電気通信大学 (東京).

(19) 松崎亮介, 柴田元子, 轟章, 島村佳伸, 孔穴処理法による GFRP/金属一体成

形継手の開発 , 強化プラスチック協会 , 50th FRP CON-EX2005 講演会 , A-8/1-8/3, (2005.9.26-27), 幕張メッセ (千葉).

(20) 松崎亮介, 轟章, 拡張フラクタル分枝限定法による非対称積層構成最適化, 日本複合材料学会, 第 30 回複合材料シンポジウム, 257-258, (2005.10.19-21), 愛媛大学 (愛媛).

(21) 轟章, 松崎亮介, タイヤひずみのパッシブモニタリング, 日本機械学会 , M&M2005材料力学カンファレンス, 349-350, (2005.11.4-6), 九州大学 (福岡).

(22) 松﨑亮介, 轟章, CFRP 四足歩行ロボット脚部構造の積層構成最適化, 日本機

械学会, 第 18 回計算力学講演会, 573-574, (2005.11.19-21), 筑波大学 (茨城

県). (23) 松﨑亮介, 轟章, 発振周波数変化を用いた CFRP 積層板の無線ひずみセンサ

(Wireless strain sensor for CFRP laminates using oscillating frequency change), 日本機械学会, 埼玉ブロック大会 2005（講演会）, 113-114, (2005.11.25), ラフレ

さいたま (埼玉).

181


(24) 松崎亮介, 柴田元子, 轟章, ニットファブリック/アルミ合金一体成形継手の

強度評価,日本機械学会(Evaluation of the strength of knit fabric/aluminum alloy co-cured joint), 埼玉ブロック大会2005(講演会), 121-122, (2005.11.25), ラフレ

さいたま (埼玉). (25) 松崎亮介, 鷲巣敬太, 轟章, 電気容量変化を用いたパッチ型フレキシブルセ

ンサによるタイヤのひずみ測定, 日本機械学会, M&M2006 材料力学カンフ

ァレンス, 197-198, (2006.8.4-6),静岡大学 (静岡). (26) 松崎亮介, 柴田元子, 轟章, ニットファブリックを用いた GFRP/金属ボルト

埋め込み一体成形継手の評価, 日本機械学会, M&M2006 材料力学カンファ

レンス, 593-594, (2006.8.4-6),静岡大学 (静岡). (27) 松崎亮介, 鷲巣敬太, 轟章, 電気容量型柔軟センサを用いたタイヤのひずみ

測定 , 日本機械学会 , 2006 年度年次大会講演概要集（ 1） , 875-876 (2006.9.18-21), 熊本大学 (熊本).

(28) 松崎亮介, 柴田元子, 轟章, ボルト一体成形によるニットファブリック/アル

ミ合金継手の評価, 日本機械学会, 2006 年度年次大会講演概要集（6）, 167-168, (2006.9.18-21), 熊本大学 (熊本).

(29) 松崎亮介, 轟章, 発振周波数変化とスペクトル解析を用いた CFRP 積層板の

ひずみ・損傷複数点同期無線測定, 日本複合材料学会, 第 31 回複合材料シン

ポジウム, 113-114, (2006.10.26-27), 信州大学 (長野).

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