june 23 rd 2015 research area: reliability engineering institute of machine components failure...

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Research Area: Reliability Engineering

Institute of Machine Componentsw

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Failure Prediction By Means OfAdvanced Usage Data Analysis

Reliability of Accelerators for Accelerator Driven SystemsCERN, Geneva, Switzerland, Tuesday, June 23rd, 2015

Dipl.-Ing. Frank JakobDipl.-Ing. Mathias BotzlerDr.-Ing. Peter ZeilerProf. Dr.-Ing. Bernd Bertsche

Institute of Machine Components Reliability Engineering

Based on the ASQ-Best-Paper of RAMS 2014 with the same title

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Failure Prediction By Means Of Advanced Usage Data Analysis

Outline

Introduction and Motivation

Basic Idea

MethodData BasisData Combination a-MethodData Combination b-Method

Aspects of Application

Results and Conclusion

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Component wear affects reliability performace

𝐵10 , h𝑉𝑒 𝑖𝑐𝑙𝑒>1.5 ⋅106𝑘𝑚 𝐵10 ,𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡<6 ⋅105𝑘𝑚

Components Ageing and Fatigue Wear-out

Entire System Long lifetimes High reliability

Achieved through maintenance Timely replacement

www.daerr.info

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Failure prediction enhances classic diagnosis

Classic Diagnosis Condition monitoring Sharp fault criteria Clear failure indicators Reactive Limited / Expensive

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Probability based diagnosis Probability solely based on

previous usage and Independent of condition Over a given timespan

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Predicted failures must be usage induced

Wear out behavior Increasing failure rate Probability of failure

increases with usage Condition of component

worsens For Weibull: b>1

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To predict a failure based on previous usage, the underlying failure mechanism must be usage-driven: It must be wear out.

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Outline


Basic Idea

MethodData BasisData Combination a-MethodData Combination b-Method



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Availability of usage data improves predictions

Failure

e.g. mileage100k 200k 300k

Weightedusaget

Suspension

Sampled systems

e.g. mileage100k 200k 300k

Single System

Weightedusaget

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F

FF

F

Distribution

Weightedusaget

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8 failures

7 8 9 10 11 12 13 14

8 failures

F(t

)

1000 weighted cranks

7 8 9 10 11 12 13 14

F(t

A)

1000 cranks

_______1) Fictive Numbers

Example1): Prediction of starter failures (# of cranks; engine oil temperature)

Case # cranks1 82182 92603 97554 99055 102336 104857 116798 13132

Case # cranks weighted

ϑoil total

1 8218 3823 2803 1592 9334

2 9260 4167 3241 1852 10418

3 9755 3902 4877 976 11218

4 9905 3565 3466 2874 10251

5 10233 1841 6139 2253 10027

6 10485 2097 6710 1818 10765

7 11679 583 6773 4323 9809

8 13132 656 8535 3941 11490

Sensible weighting of data increases sharpness

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8 failures

7 8 9 10 11 12 13 14

8 failures

F(t

)

1000 weighted cranks

7 8 9 10 11 12 13 14

F(t

A)

1000 cranks

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Outline


Basic Idea

MethodData BasisData Combination a-Method

Data Combination b-Method



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Rotational speed turbo charger Engine oil temperature Engine speed / torque

Number of Starts . . .

Battery SOC / ambient temperature Vehicle speed / gear in Clutch slip (kiss point) Steering angle Axle loads . . .

Lateral acceleration Longitudinal acceleration Duty cycles air compressor Brake pressure / vehicle speed . . .

Use data from existing sources for further analysis

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Available data is processed to usage matrices

Limitations Availability of signals Integrated sensors Memory usage Correlations A-Priori decisions

Torque and engine speed 1)

Engine speed(Rainflow) 1)

_______1) Fictive Examples

Data Collection Data and signals from ECU

become available Binned and counted

Usage matrices Low memory requirements

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Outline


Basic Idea

MethodData BasisData Combination a-Method




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a-Method: Knowledge for weighting and condensing

Physical Weighting Acceleration factors Standards Expert knowledge Physics of Failure …

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Outline


Basic Idea

MethodData Basis

Data Combination a-Method

Data Combination b-Method Aspects of Application


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b-Method step 1: Normalization of diverse data

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Case # cranksϑoil

1 4109 2465 16442 4167 3241 18523 3902 4877 9764 3565 3466 28745 1841 6139 22536 2097 6710 16787 583 6773 43238 656 8535 3941

𝑢 𝑗 ,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑=𝑢 𝑗

𝐵10

Normalization Dimensionless numbers Similar magnitude order Important prerequisite

for combination

# cranks normalized

5.02 0.85 1.405.09 1.12 1.574.77 1.69 0.834.36 1.20 2.442.25 2.13 1.912.56 2.32 1.420.71 2.34 3.670.80 2.95 3.35

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b-Method step 2: Numeric weighting of diverse data

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Optimization Variation of b-parameters Spread of F(t) changes Minimization of spread with

optimization algorithm (e.g. evolutionary)

𝑠𝑝𝑟𝑒𝑎𝑑=𝐵90

𝐵10

The optimal set of b-parameters maps usage data over true wear-out mechanisms. The resulting spread will thus be minimal.

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First experience with real data shows decent results

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Spread-Minimum, mixing(Brute Force, 3 b-parameters)

Failure probabilities after evolutionary optimization(>10 b-parameters)

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With combined application, strengths come to shine

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a-Method b-MethodStep # 1 2

Kind of data to be combined

Alike Data(e.g. all # of operations)

Diverse Data(e.g. mileage and times)

Number of parameters Unlimited Limited by Computational

Resources and Interpretation

Derivation of parameters Physical Correlations Numeric Algorithms

Required Input Knowledge Failure Data

Purpose Pre-CondensationInput for b-Method Final Optimization

𝑡 𝐴𝐵=∑𝑗

𝑛𝑝𝑎𝑟 (𝑏 𝑗 ⋅∑𝑖

𝑛𝑏𝑖𝑛

𝑎𝑖 ⋅𝑢𝑖)

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Outline


Basic Idea

MethodData Basis





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Basis: 3par Weibullb=2 t0=3;4;5 T=t0+3

Influences Cost of premature exchange Wasted lifetime in distribution tail

Cost of failure is purely customer induced

The more likely a future failure, the more desirable preventive measures

Economic benefit depends on exterior factors

Preventive decisions highly dependon boundary conditions

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Influence of failure free period

: Predicted failure probability to time

Cost

s

Cost of component

0% 50% 100%

Cost of failure

benefit

loss

𝐹 ( 𝑡𝑝𝑟𝑒𝑑 ) ⋅𝐶 𝑓𝑎𝑖𝑙𝑢𝑟𝑒

𝐶𝑟𝑒𝑠𝑖𝑑

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Outline


Basic Idea

MethodData Basis





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Motivation: Components of reliable systems can be prone to wear Goal: Prediction of wear failures Sources: ECU data to be used in reliability analysis Method: Analysis with and without knowledge

a-Method b-Method

First Results: Method was successfully applied to real field data Decision-Making: Trade-Off is highly individual

Method for reliability analyses with lots of information on failures Outlook: Validation requires failures

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References (1/2)

B. Bertsche, Reliability in Automotive and Mechanical Engineering, Berlin: Springer, 2008.

M. Botzler, P. Zeiler, and B. Bertsche, “Failure Prediction By Means Of Advanced Usage Data Analysis,” in Annual Reliability and Maintainability Symposium, 2014.

T. Duchesne, “Multiple Time Scales in Survival Analysis,” Diss, University, Waterloo (Ontario), 1999.

I. Gertsbakh, Reliability Theory: With applications to preventive maintenance, Berlin: Springer, 2000.

P. D. T. O'Connor, Practical reliability engineering, 5th ed. Chichester: Wiley, 2012.

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References (2/2)

C. Prothmann, M. Kokes, and S. Liu, “Deterioration modeling strategy for pro-active services of commercial vehicles,” Proceedings of the 2010 American Controls Conference (ACC 2010), 2010, pp. 6157–6162.

M. Maisch, “Reliability Based Test Concept for Commercial Vehicle Transmissions in Consideration of Operation Data,” Diss. (published in German), IMA, University, Stuttgart, 2007.

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Thank you for your attention!

University of StuttgartInstitute of Machine ComponentsDipl.-Ing. Frank JakobPfaffenwaldring 970569 StuttgartGermany

Phone: +49 711 685 69954Fax: +49 711 685 66319Mail: [email protected]: www.ima.uni-stuttgart.de

june 23 rd 2015 research area: reliability engineering institute of machine components failure...

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