envelope besteld artikel

7/29/2019 Envelope Besteld Artikel

1/14

SKF Condition Monitoring

Abstract and Biography

Technical Association of thePulp and Paper Industry

Optimized Application ofFeature Extraction Techniques

by Andre J. Smulders SKF Condition Monitoring

Biography

ANDRE J. SMULDERS


Andre J. Smulders holds a master degree in

Electrical Engineering and a bachelor degree in

Mechanical Engineering. He worked in the

computer industry, the semi-conductor industry and

the sensor industry before joining SKF in 1981.

Has developed the Condition Monitoring

technologies as applied by SKF Condition

Monitoring today. He is the co-inventor of SEE

technology and holds patents in the fields of semi-

conductors, sensor technologies, measurement

techniques and in the field of signal analysis. He hasbeen a part time professor at a technical high school

for a number of years. He was involved of the start

up of SKF Condition Monitoring in 1989. He has

been involved in the development of techniques and

applications in the field of Condition Monitoring and

Quality Monitoring.

Abstract

Recent advancements in envelope enhancement

techniques as applied to acceleration and acoustics

emissions signals have led to new measurementsolutions for many vibration problems. This paper

discusses the theory of enveloping and how it is

implemented in practice. It presents a paper

machine case study that illustrates how a rolling

element bearing defect develops. Also some case

studies showing the strength of analysis in a

modulating environment will be discussed.

Measurement setups are very important for good

analysis and ease of recognition of symptoms. This

will be illustrated with a case study too.


2/14


Technical Association of thePulp and Paper Industry Optimized Application ofFeature Extraction Techniques

by Andre J. Smulders SKF Condition Monitoring

Copyright 2000 by SKF Condition Monitoring, Inc. ALL RIGHTS RESERVED

Abstract

Recent advancements in envelope enhancement

techniques as applied to acceleration and acoustics

emissions signals have led to new measurement

solutions for many vibration problems. This paper

discusses the theory of enveloping and how it is

implemented in practice. It presents a paper

machine case study that illustrates how a rolling

element bearing defect develops. Also some case

studies showing the strength of analysis in a

modulating environment will be discussed.

Measurement setups are very important for good

analysis and ease of recognition of symptoms. This

will be illustrated with a case study too.

Condition Monitoring An Historical

Review

The main reason for condition monitoring is to

prolong machinery life with the least overall cost.

There are several measurement parameters that

contribute to the evaluation of machinery health

such as vibration, bearing temperature, lubrication,

oil conditions, pressure that are measured on a

periodic basis to assess the long term prognosis of

the operating machine life. These machine condition

parameters as applied to a monitoring program, are

not the only factors in the attempt to achieve

maximum reliability with minimum cost. The

simplistic periodic visual inspection and the

experienced technicians ear are more often equally

important as diagnostic measures to augment the

predictive maintenance program and avert

catastrophic failures. An important aspect of the

data loggers programmed route is to assure periodic

visits to every machine based on a critical machine

priority that not only measures the assigned

vibration points, but to perform visual and acoustic

inspections as well. These machine conditions that

are subjective evaluations are entered as

maintenance notes to be reviewed later in themachine history file. In a sense, the periodic data

logging sequence as defined by the predictive

maintenance schedule serves inherently as a

watchmans clock to assure a prioritized organized

visitation by experienced personnel to every

machine. The assessment of machine conditions,

operating performance and status of auxiliary

components valves piping, packing, loose bolts,

flange leaks are then considered in total for

recommended corrective maintenance actions.

Condition monitoring has always existed where

engine room personnel have felt, smelled or listened

to machine sounds as symptomatic of abnormal

machine performance. In these times of higher

speeds, design limit operations, complex processes

involving large populations of finite life machine

components, more automatic controls resulting in

minimum operations staff combined with

spiralling maintenance costs and extreme down time

production loss the need was created for warning

diagnostic systems employing hardware andsoftware sophisticated technology. These modern

condition monitoring systems now include new

measurement techniques which were untried and

unproven in the immediate past. These modern

methods are known to be viable as evidenced by the

case studies that are incorporated in the last section

of this article.


3/14

Technical Association of the Pulp andPaper Industry Optimized Applicationof Feature Extraction Techniques

T E C H N I C A L P A P E R

The Ultimate Goal

In simple terms the main aim of the monitoring

system is to first significantly reduce unexpected

mechanical failures, thereby minimizing downtime

production losses. This objective has been achieved

generally by predictive maintenance programs that

rely principally on a periodic data logging schedule,

involving instruments measuring overall vibration

data. These instruments often operate in conjunction

with computer based programs that will trend the

historical data of each measurement point and alsoallows the development of routes that can be

downloaded to field instruments. The next level of

the program goal is to recognize problems early

enough to schedule repairs with minimum disruption

to the operations.

Such maintenance decisions require a sufficient

assessment of the problem to assume a risk of failure

with the consequences of an unscheduled shutdown.

Todays predictive maintenance programs with the

many vibration measurement tools available and the

various analysis methods inherent in the associated

software, give the maintenance engineer the

opportunity to corrective measures that prolong

machine service life, improve product quality and

reduce production costs by running process speeds

closer to the design limits.

Although multiparameter measurements are

required for a complete assessment of operating

characteristic, vibration is the best measurement

parameter for evaluating machine dynamic

conditions that affect machine performance and

service life.

The effects of imbalance, misalignment, mechanical

looseness, bearing defects, ineffective lubrication,

shaft rubs are revealed as vibration characteristics

that are often identified by some spectrum signature.

The methods of feature extraction for problem

recognition have been developed and continue to berefined for both manually derived and automatic

diagnostic decisions. The probable accuracy of

these maintenance recommendations that is derived

from these problem recognition methods, increases

with more available detailed information concerning

specific machine characteristics and its associated

mechanical components.

Oil condition monitoring provides an estimate of the

deteriorating lubricating properties that can

contribute to machine damage.

Viscosity changes, contaminants and metal particles

are some of lube oil detectable trends, that will over

time affect, the wear of bearing surfaces.

In addition to automatic vibration measurements and

data transposition to organized files of data trending

diagnostic analysis and new software extensions

allow for expert analysis. These software additions

to the traditional predictive maintenance software

include programs that scan machine historical data

with feature extraction algorithms to generate

symptom files. These files are compared against

resident diagnostic rules that are used to estimate the

probable machine failure modes. The expert system

then recommends maintenance actions based on

these severity estimates.

Traditional Vibration ParameterVelocity

In the past field vibration measurements were

usually performed using velocity transducers that didnot require excitation. The electronic

instrumentation measured overall values and the data

was manually recorded. Vibration trends were

plotted manually to determine the machine health

status.

Velocity measurements remain today an important

measurable parameter since it is essentially related

to vibration energy.

2


4/14



Overall vibration velocity measurements are oftencompared to standardized alarm levels based on

accumulated experience. These velocity alarms are

constant levels applied over a wide frequency range.

Velocity is a parameter linear to vibration and is

proportional to sound pressure so correlated with the

sound impression generated in a machine

environment.

The more universal transducer in use today is the

piezoelectric accelerometer. The velocity

measurement parameter is obtained by simpleintegration of acceleration.

Feature Extraction Techniques

For Optimized Analysis and Ease of Diagnostics

capabilities the strongest techniques known today is

Acceleration Enveloping (or Demodulation in

general as it can also be applied on other signals like

motor currents or pressure signals).

Enveloping addresses the problem of isolating small

but significant impulse perturbations that aresummed, during measurement, with larger, low

frequency, stationary vibration signals, such as

imbalance and misalignment. These small impulse

signals come from the accelerometer response to

impulsive forces from bearing race defects, from

roller flat spots, from gear teeth interaction.

Specifically related to a paper machine press

sections, these signals may come from felt joint

connectivity and/or felt dewatering anomalies.

Although normal FFT spectrum analysis separatesthese signals into their fundamental and harmonics,

the individual amplitudes are often too small to see

above the instrumentation noise level. Because of

this low signal-to-noise ratio, these small spectral

components are not generally measurable in the

early onset of a bearing or other machine fault.

A small, narrow, repetitive impact signal, when

converted to the frequency domain, results in a plot

of small harmonic amplitudes with a frequencyseparation equal to the repetitive rate. Compare the

different amplitude/frequency relationships between

a sinusoidal pure tone signal and a repetitive

impulse.

The impulse signal amplitude is proportional to the

pulse width (-t) and pulse cycle interval (T). The

smaller this ratio is that is, the narrower the pulse

width the smaller are the spectrum amplitudes.

This ratio is, of course, related to the width of the

bearing defect.

Initially, an accelerometer response signal is small in

amplitude and narrow in time as each ball rolls over

a newly developing fault. An acceleration spectrum

plot at this early stage of defect growth would

probably not show the defect as its amplitude is

below the dynamic range of the measuring

instrument. Vibration components identifying an

incipient bearing failure are then not seen in an

acceleration spectrum plot. However, enveloping

technology, now implemented in many dataloggersand on-line systems that incorporate FFT analysis,

has proven to be an effective measurement tool

because it modifies the raw vibration signal so as to

enhance the rolling element bearing defect signal

and other comparable signal.

The Basics of Enveloping

The envelope method separates a repetitive impulse

from a complex vibration signal by using a band

pass filter that rejects low frequency components

that are synchronous with vibration.

Although there are signal enhancements that result

from structural resonances, the envelope method is

completely independent on local resonance to isolate

rolling element defect signals. This is very

important as resonance frequencies and the damping

at resonance are often not stable so not useful for a

trend type analysis.

3


5/14



Filter criteria selection is based on suitable rejectionof the low frequency sinusoids while optimizing the

passband of the defect harmonics. This also creates

the possibility for separation of phenomenon. This

is illustrated in the following figure. The figure

provides the table of filter selections based on

rotational speeds and shows the optimal band of

analysis.

After filtering the vibration signal, the resultant

signal is enveloped by means of a circuit that

approximates the squaring process of the signal.

The enveloping process demodulates the signal

which approximates a squaring function. This

translates the signal in the frequency domain to a

baseband display of the repetition rate harmoniccomponents, where the component amplitude versus

frequency is equivalent to a sin x over x distribution.

These displays would only be seen if there are

repetitive impulse components in a part of the

overall raw vibration signal.

Another way of understanding this translation to

baseband is to consider the bandpass filtered signal

as only comprising the higher frequency harmonic

components of the repetitive impulse. When this

harmonic series is squared, sum and differencecomponents are created. The difference components

fold back into the analysis range while all of the

summed components are outside the analysis range.

4

Enveloping Settings Microlog/Multilog

PAPER MACHINE PRESS/FELTMONITORING

Filters Enveloping Frequency Speed Range Analyzing Range

Felts/Press Rolls

1 5 100 Hz 0 50 RPM 0 10 Hz

ROLL BEARINGS

2 50 1,000 Hz 25 500 RPM 0 100 Hz

ROLL BEARINGS / GEARS

3 500 10,000 Hz 250 5,000 RPM 0 1,000 Hz

GEARS

4 5,000 40,000 Hz 2,500 RPM 0 10,000 Hz

High Pass Filter: 24 dB/octave Bessel

Low Pass Filter: 12 dB/octave Bessel

REMARK: In a application where gears are involved sometimes a lower envelope filter needs to be chosen

to suppress the noise from gearmesh frequencies that are commonly very dominant.


6/14



Fundamental Properties ofAcceleration Enveloping/Demodulation/Rectification

This feature extraction technique has a number of

principles advantages and properties that make it

ideal for signal extraction of non-sinusoidal signals

and signals that are modulated by some carrier

phenomenon.

1. SELECTIVE FILTERING so excludes specific

sinusoidal signals.

2. DISCRIMINATIONOF PHENOMENONby energy

estimation in a specific selected frequency band.

3. PULSE ENHANCEMENTVERSUS SINUSOIDAL SIGNALS.

Energy estimation focuses on peak phenomenon

with correlated phase characteristics versus

wavy type phenomenon.

4. SIGNAL-TO-NOISE IMPROVEMENT. An energy

estimation enhances localized energy,

concentrating FFT distributed peaks into itsbasebands.

5. Speed varying compensation as small phase shifts

during rotation (non-constant rotational velocity)

will be averaged-out.

6. INSTANTANEOUS SYNCHRONOUS TIME AVERAGING.

Bringing energy to baseband frequency

components enables the time record to be longer

and so inherently does better synchronous

averaging.

Preconditions for Optimal Enveloping

1. Sufficient signal-to-noise ratio in the measuring

chain before Enveloping is performed.

2. Pre-filtering with Constant Time Delay filters forgood Peak reproduction.

3. Large bandwidth for optimal summation of

energy.

4. Signal source separation by Optimal Pre-filter

selection.

5. Time domain analysis so extraction is done

without separation of coherent frequency

components.

6. Low pass filter selection after Enveloping for

rejection of Out-of-Band components.

Conclusion

The acceleration enveloping technique is emerging

as a very practical measurement tool for assessing

initial problems associated with bearings, rollers,

and felt rotation. The very low speeds at which

these measurements occur are often at sensitivity

limits of transducers and electronics. In the past,

synchronous time averaging over very long intervalswas required to isolate problems to a particular roll

by establishing external trigger references.

Enveloping has proven its capabilities to extract

impact force signals developed by roll eccentricity,

flat spots, rolling element bearing defects and many

other impulse type or modulating type signals.

Although enveloping is not the panacea for

diagnosing all machine problems, it is proving to be

an adaptable and effective measurement method in

the tool box of analysis techniques.

5


7/14



Case Studies

BEARING TESTRIG

DEMONSTRATION

Figure 1.

Standard Velocity Measurement

with defective bearing.

Although bearing defect

frequencies noticeable no clear

indication as still many other

frequency components are ofthe same level.

Figure 2.

Zoomed Velocity spectrum with

rotational components visible

but no significant bearing defect

pattern.

6

Figure 1.

Figure 2.


8/14



Case Studies

BEARING TESTRIG

DEMONSTRATION

Figure 3.

Enveloped Acceleration

showing a clear discriminative

spectrum of an Inner-race

Defect Pattern.

Figure 4.

SEEspectrum (Enveloped

Acoustic Emission spectrum)

also showing the bearing defect

pattern as indicating friction

(progress of wear). The extra

sidebands around the bearing

defect modulation frequency

peaks indicate a modulation by

a low-frequency phenomenonlikely uneven coupling loading.

7

Figure 3.

Figure 4.


9/14



Case Studies

BEARING DEFECTDEVELOPMENT

ONA DRYER FELTROLL

Figure 5.

Trend Plot of the Standard

Velocity Measurement. No

indication of a bearing defect

visible.

Figure 6.

Velocity Spectrum showing a

number of harmonic patterns

but no clear indication of an

Inner-race Defect Pattern.

8

Figure 5.

Figure 6.


10/14



Case Studies

BEARING DEFECTDEVELOPMENT

ONA DRYER FELTROLL

Figure 7.

Trend Plot of the Acceleration

Enveloping Measurement.

Good indication of a bearing

defect development.

Figure 8.

Enveloped Acceleration

Spectrum showing a clear

discriminative spectrum of an

Innerrace Defect Pattern.

9

Figure 7.

Figure 8.


11/14



Case Studies

OPTIMAL MEASUREMENTSETUP

ONDRYER CAN

Figure 9.

Spectrum Plot of the

Acceleration Enveloping

Measurement. Although the

bearing defect is visible the

pattern is not extremely clear.

The measurementTIMELENGTHwas too short.

This is defined by the selected

Bandwidth versus the chosen

RESOLUTION (LINES).

Timelength = Lines / Bandwidth

Optimal timelength is 10 15X

the time for one shaft rotation.

Figure 10.

Time plot belonging to Figure 9.

The measurement Timelength

does not contain sufficient

revolutions of the shaft to built a

clear spectral pattern.

10

Figure 9.

Figure 10.


12/14



Case Studies

OPTIMAL MEASUREMENTSETUP

ONDRYER CAN

Figure 11.



Measurement. The bearing

defect is clearly visible with a

clear sideband pattern so

indicative for an innerracedefect pattern. The selected

measurement Timelength is

optimally chosen.

Figure 12.

Time plot belonging to Figure

11. The measurement

Timelength does contain

sufficient revolutions of the

shaft (modulation) to built a

clear spectral pattern.

11

Figure 11.

Figure 12.


13/14



Figure 13.

Figure 14.

Case Studies

PRESS SECTIONFELTANOMALY

WITHMODULATIONDRIVE TRAIN

PATTERN

Figure 13.

Time plot indicating the Felt

repetition pattern (see also

Figure 14) modulated by a

DRIVE TRAINcontrol loop

problem.

Figure 14.

Zoomed Time plot indicating

the Felt repetition pattern.

These patterns are indicative of

uneven dewatering

characteristics in the felt.

12


14/14



Figure 15.

Figure 16.

Case Studies

PRESS SECTIONFELTANOMALY

WITHMODULATIONDRIVE TRAIN

PATTERN

Figure 15.



Measurement. The FELT

pattern is clearly visible. The

sideband pattern indicative for a

modulation pattern becomes

clearer after zooming (see

Figure 16).

Figure 16.

Zoomed Spectrum Plot of the


Measurement.

The modulation caused by the

drive train driving the Fourth

press is clearly positioned

around the spectral Felt Pattern.

13

envelope besteld artikel

Documents