June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Reliability of Accelerators for Accelerator Driven Systems CERN, Geneva, Switzerland, Tuesday, June 23rd, 2015 Dipl.-Ing. Frank Jakob Dipl.-Ing. Mathias Botzler Dr.-Ing. Peter Zeiler Prof. Dr.-Ing. Bernd Bertsche Institute of Machine Components Reliability Engineering Based on the ASQ-Best-Paper of RAMS 2014 with the same title

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Outline IIntroduction and Motivation BBasic Idea MMethod DData Basis DData Combination a-Method DData Combination b-Method AAspects of Application RResults and Conclusion 2

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Component wear affects reliability performace Components  Ageing and Fatigue  Wear-out Entire System  Long lifetimes  High reliability  Achieved through maintenance  Timely replacement 3

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components 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

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components 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.

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Outline  Introduction and Motivation  Basic Idea  Method  Data Basis  Data Combination a-Method  Data Combination b-Method  Aspects of Application  Results and Conclusion 6

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Availability of usage data improves predictions Failure e.g. mileage100k200k 300k Weighted usage t Suspension Sampled systems e.g. mileage 100k200k 300k Single System Weighted usage t 7 F F F F F F F F Distribution Weighted usage t

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis _______ 1) Fictive Numbers Example 1) : Prediction of starter failures (# of cranks; engine oil temperature) Case# cranks Case# cranksweighted ϑ oil total Sensible weighting of data increases sharpness 8

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Outline  Introduction and Motivation  Basic Idea  Method  Data Basis  Data Combination a-Method  Data Combination b-Method  Aspects of Application  Results and Conclusion 9

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components 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 10

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components 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 11

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Outline  Introduction and Motivation  Basic Idea  Method  Data Basis  Data Combination a-Method  Data Combination b-Method  Aspects of Application  Results and Conclusion 12

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components 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

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Outline  Introduction and Motivation  Basic Idea  Method  Data Basis  Data Combination a-Method  Data Combination b-Method  Aspects of Application  Results and Conclusion 14

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis b-Method step 1: Normalization of diverse data 15 Case# cranks ϑ oil Normalization  Dimensionless numbers  Similar magnitude order  Important prerequisite for combination # cranks normalized

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis b-Method step 2: Numeric weighting of diverse data 16 Optimization  Variation of b-parameters  Spread of F(t) changes  Minimization of spread with optimization algorithm (e.g. evolutionary) The optimal set of b-parameters maps usage data over true wear- out mechanisms. The resulting spread will thus be minimal.

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis First experience with real data shows decent results 17 Spread-Minimum, mixing (Brute Force, 3 b-parameters) Failure probabilities after evolutionary optimization (>10 b-parameters)

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis With combined application, strengths come to shine 18

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Outline  Introduction and Motivation  Basic Idea  Method  Data Basis  Data Combination a-Method  Data Combination b-Method  Aspects of Application  Results and Conclusion 19

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Basis: 3par Weibull b=2 t 0 =3;4;5 T=t 0 +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 depend on boundary conditions 20 Influence of failure free period Costs Cost of component 0% 50% 100% Cost of failure benefit loss

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis Outline  Introduction and Motivation  Basic Idea  Method  Data Basis  Data Combination a-Method  Data Combination b-Method  Aspects of Application  Results and Conclusion 21

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components 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

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components Failure Prediction By Means Of Advanced Usage Data Analysis References (1/2)  B. Bertsche, Reliability in Automotive and Mechanical Engineering, Berlin: Springer,  M. Botzler, P. Zeiler, and B. Bertsche, “Failure Prediction By Means Of Advanced Usage Data Analysis,” in Annual Reliability and Maintainability Symposium,  T. Duchesne, “Multiple Time Scales in Survival Analysis,” Diss, University, Waterloo (Ontario),  I. Gertsbakh, Reliability Theory: With applications to preventive maintenance, Berlin: Springer,  P. D. T. O'Connor, Practical reliability engineering, 5 th ed. Chichester: Wiley,

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components 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,

June 23 rd 2015 Research Area: Reliability Engineering Institute of Machine Components 25 Thank you for your attention! University of Stuttgart Institute of Machine Components Dipl.-Ing. Frank Jakob Pfaffenwaldring Stuttgart Germany Phone: Fax: Web: