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P T C . c o m

Case Study Relex A PTC InSight Product Page 1 of 2

Relex Weibull Helps Provide ConfidentCost EstimationsUsing Relex Weibull to Bid Confidently, HR Textron Wins Contracts and Saves Penalty Costs

HR Textron, Santa Clarita, CA HR Textron (currently known as Woodward HRT) produces componentsand systems used throughout the world in xed-wing, rotary-wing, andtilt-rotor aircraft, precision-guided missiles and munitions, unmannedair vehicles, spacecraft, turbine engines, and armored vehicles. HR

Textron must bid on various contracts for its work in this fast-paced,competitive eld.

The Challenge: Confident, Competitive Bid Proposals HR Textron must estimate the lifetime cost of components

Cost estimates must be competitive to win bids

HR Textron is liable for cost overruns on inaccurate bids

The Solution: Relex Weibull Helps Predict Costs Relex Weibull can use limited eld data to predict failures

Based on eld metr ics, the cost of a component can be predicted

A four-step process was developed to estimate costs

The Result: Success in Winning Highly Competitive BidsCompetitive, highly accurate bids can be made with condence

HR Textron wins key contracts thanks to highly competitive bids

HR Textron avoids penalty costs thanks to highly accurate bids

According to John Richardson,a System Engineer in the

Reliability, Maintainability,Safety/Integrated LogisticsSupport Area at HR Textron,

his use of Relex tools hasallowed him and his team toquickly turn around analy-ses, making them much moreeffective in responding to vari-ous proposals while saving thecompany time and money.

HR Textron produces

components andsystems for variouskinds of aircraft anddefense applications.

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Case Study Relex A PTC InSight Product Page 2 of 2

The CaseThe reliability metric frequently used to generate lifetime costs for aproposal bid is MTTF (Mean Time to Failure). Cost projections for newcomponents may be based on metrics of similar deployed components.

However, it is difcult to directly obtain MTTF values of aircraft com-ponents, as this measurement is often unavailable.

John Richardson, a Systems Engineer in the Reliability, Maintainability,Safety / Integrated Logistics Support Area at HR Textron, has createda procedure in response to demands placed on him and his team togenerate accurate, competitive bid proposals for contracts, often in ashort amount of time. By employing the following four-step processin Relex Weibull, Richardson and his team can condently predict thelifetime cost of components.

Step One: Gather Field DataIn this example, the team was provided with minimal eld data onhelicopter servo actuators. This data consisted only of:

Quantity received (the number of elded units for which data was

collected): 34

Time between overhaul (TBO, or the number of hours between

scheduled overhauls): 2400 hours

Utilization percentage (the percentage of units without failure at

time of overhaul): 85%

Step Two: Create Weibull Data Set from Field DataA Weibull data set consisting of failure time information and numberof failed units / non-failed units can be constructed using even this

very limited data:

Using the information, it was determined that ve units failed

before time of overhaul, with 29 operating successfully.

To create the Weibull data set, the ve failures were assumed to be

equally spaced over the duration of the time up to the overhaul.

In addition to the direction of the change of failure rate, the Beta value demonstrates how drastic the change is. For example, when Beta equals one, the failure rate is constant. When Beta is greater thanone, the failure rate curve slopes upward. The greater the Beta value,the steeper the curve becomes, indicating that failures are occurringmore frequently. Alternately, when Beta is less than one, the failurerate curve slopes downward. The smaller the value of the shape factor,the less steep the curve becomes, indicating that failures are occurringless frequently. The parameter results were as follows:

In this case, the analysis results yielded Eta (characteristic life) of

9456.0795 hours and Beta (shape factor) of 1.2243, or graduallyincreasing (wearout) failure rate, as displayed below, at right.

Step Four: Predict Life Metrics Using Relex Weibull With the Weibull parameters for the data set known, the SummaryCalculator in Relex Weibull may be used to calculate the component

MTTF:

The resulting MTTF is 8850.3637 hours.

The Results Using the calculated MTTF value, the team is able to calculate accuratelifetime cost metrics, help HR Textron win competitive bids, and helpensure minimal penalty costs. According to Richardson, his use of Relextools over the past ten years has allowed him and his team to quickly

turn around analyses, making them much more effective in respondingto various proposals.

For More InformationTo learn more about Relex Weibull, or to try a free product demo, visit

www.relex.com/products/weibull.asp.Step 2: From available eld data, input data set into Relex Weibull

Relex Weibull provides the Weibull probability plot (left) and the failure rate overtime (right), among other plots, to display the characteristics of the data.

Step Three: Analyze Weibull Data Set From the data set, Richardson and his team used Relex Weibull to cal-culate Eta (characteristic life, or the time at which 63.2% of units areexpected to have failed) and Beta (the Weibull shape parameter, whichindicates if the failure rate is increasing, decreasing, or constant).

Copyright 2009, Parametric Technology Corporation (PTC). All rights reserved. Information describedherein is furnished for informational use only, is subject to change without notice, and should not beconstrued as a guarantee, commitment, condition or offer by PTC. PTC, the PTC logotype, Relex, and allPTC product names and lo gos are trademarks or registered trademarks of PTC and/ or its subsidiaries inthe United States and in other countries. All other product or company names are the property of theirrespective owners.

4864- Relex-CS-1009