june 23 rd 2015 research area: reliability engineering institute of machine components failure...
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Ju
ne
23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
stu
ttg
art.
de
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
Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
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ttg
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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
2
Ju
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23rd
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
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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23rd
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
Failure prediction enhances classic diagnosis
Classic Diagnosis Condition monitoring Sharp fault criteria Clear failure indicators Reactive Limited / Expensive
4
Probability based diagnosis Probability solely based on
previous usage and Independent of condition Over a given timespan
Ju
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23rd
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
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
5
To predict a failure based on previous usage, the underlying failure mechanism must be usage-driven: It must be wear out.
Ju
ne
23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
stu
ttg
art.
de
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
6
Ju
ne
23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
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ttg
art.
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Failure Prediction By Means Of Advanced Usage Data Analysis
Availability of usage data improves predictions
Failure
e.g. mileage100k 200k 300k
Weightedusaget
Suspension
Sampled systems
e.g. mileage100k 200k 300k
Single System
Weightedusaget
7
F
FF
F
Distribution
Weightedusaget
Ju
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23rd
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
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
Ju
ne
23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
stu
ttg
art.
de
Failure Prediction By Means Of Advanced Usage Data Analysis
Outline
Introduction and Motivation
Basic Idea
MethodData BasisData Combination a-Method
Data Combination b-Method
Aspects of Application
Results and Conclusion
9
Ju
ne
23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
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a.u
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ttg
art.
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Failure Prediction By Means Of Advanced Usage Data Analysis
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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Ju
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23rd
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
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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Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
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ttg
art.
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Failure Prediction By Means Of Advanced Usage Data Analysis
Outline
Introduction and Motivation
Basic Idea
MethodData BasisData Combination a-Method
Data Combination b-Method
Aspects of Application
Results and Conclusion
12
Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
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Failure Prediction By Means Of Advanced Usage Data Analysis
a-Method: Knowledge for weighting and condensing
Physical Weighting Acceleration factors Standards Expert knowledge Physics of Failure …
13
Ju
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23rd
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15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
stu
ttg
art.
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Failure Prediction By Means Of Advanced Usage Data Analysis
Outline
Introduction and Motivation
Basic Idea
MethodData Basis
Data Combination a-Method
Data Combination b-Method Aspects of Application
Results and Conclusion
14
Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
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
Ju
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23rd
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
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.
Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
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)
Ju
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23rd
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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Failure Prediction By Means Of Advanced Usage Data Analysis
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)
Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
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ttg
art.
de
Failure Prediction By Means Of Advanced Usage Data Analysis
Outline
Introduction and Motivation
Basic Idea
MethodData Basis
Data Combination a-Method
Data Combination b-Method
Aspects of Application
Results and Conclusion
19
Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
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Failure Prediction By Means Of Advanced Usage Data Analysis
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
Ju
ne
23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
ni-
stu
ttg
art.
de
Failure Prediction By Means Of Advanced Usage Data Analysis
Outline
Introduction and Motivation
Basic Idea
MethodData Basis
Data Combination a-Method
Data Combination b-Method
Aspects of Application
Results and Conclusion
21
Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
.im
a.u
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Failure Prediction By Means Of Advanced Usage Data Analysis
Results and Conclusion
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
22
Ju
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23rd
20
15
Research Area: Reliability Engineering
Institute of Machine Componentsw
ww
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a.u
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Failure Prediction By Means Of Advanced Usage Data Analysis
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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Ju
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23rd
20
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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.im
a.u
ni-
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ttg
art.
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Failure Prediction By Means Of Advanced Usage Data Analysis
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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23rd
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Research Area: Reliability Engineering
Institute of Machine Componentsw
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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: frank.jakob@ima.uni-stuttgart.deWeb: www.ima.uni-stuttgart.de
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