Reliability Prediction of Electronic Boards by Analyzing Field Return Data

1. ReliabilityPrediction of Electronic BoardsbyAnalyzing Field Return Data Authors: Vehbi Cömert (Presenter) Mustafa Altun Hadi Yadavari Ertunç Ertürk

2. Performing a reliability analysis using a real field return data • Motivation: Modeling hazard rate curveandmakingaccuratereliabilityprediction

3. Introduction • Field Return Data • Electronics Reliability • Filtering • Field return data may have obvious and hidden errors. • Surveying accuracy of the field return data to find errors • Based on beta parameter of Weibull distirubtion • Modeling of hazard rate curve • Reliability prediction with filtered field return data • Investigation of distributions that fits to data. • Change of hazard rate shape with respect to ‘Time to Failure’ • Two phase hazard rate curve

4. Field Return Data • The field return data ,that we use, belongstoArçelik (Beko), one of thebiggestwhiteapliancecompany in Europe • It is a warranty data andincludes - 1 million sales - 3000 warranty claims - Wehavefirst 54 monthsof the data • Warranty involves 36 months.

5. ElectronicsReliability • Good reliability • Expected long life • Usually catastrophic failures • Decreasingorconstant hazard rate • Hard to see wear out signs  

6. Filtering Eliminating errors in field return data Step 1 : Eliminating Obvious Errors Step 2 : Eliminating Hidden Errors Stage 1: Forward analysis Stage 2: Backward analysis Stage 3: 6-month analysis

7. Errors in field return data • Obvious error : The errors that can be seen easily by checking claims • Hidden error : The errors that cannot be seen at first glance What can be a hidden error? Missing Claims

8. Filtering Process • To ensure the accuracy of the analysis, errors must be eliminated !!! Step 1 : Obvious errors must be filtered by checking hand Records with; Unknown assembly date Unknown return date Zero time to failure Negative time to failure Unreasonable time to failure

9. FilteringProcess • Step -2: Investigatethe data using Weibull distribution to find hidden errors. • Weibull distribution parameters; - Beta(): shape parameter - Alfa (): scale parameter <1 Decreasing Failure Rate =1 Constant Failure Rate >1 Increasing Failure Rate

10. Filtering Process Assembly date/Month • Step -2 stage1 : Forward Analysis 1 6 12 18 24 30 36 42 48 54 valuesforforward time intervals Weibull Fitting problematic

11. FilteringProcess Assembly date/Month • Step-2 stage-2: Backwardanalysis 1 6 12 18 24 30 36 42 48 54 valuesforbackward time intervals Lack of return data towardend of the time

12. FilteringProcess Assembly date/Month • Step-2 stage-3: 6 - monthperiodsanalysis 0 6 12 18 24 30 36 42 48 54 valuesfor 6-month periods problematic

13. FilteringProcess valuesforbackward time intervals valuesforforward time intervals problematic valuesfor 6-month periods First threeintervals (1-18 months) should be filtered. problematic

14. Modeling of hazard rate curve To obtain an accuratehazard rate curve Searchingpointswherethehazard rate tendencychanges Forward and Backward analysis

15. Modeling of Hazard Rate Curve Change Point (): Fromdecreasing rate trend toconstant rate trend

16. Modeling of Hazard Rate Curve • Method to find change point via Reliasoft Weibull++ • Analyzing filtered field return data in terms of time to failure (TTF) - Using ‘’best fit’’ option in Weibull++andfitting with respect to different time intervals. - Tryingtofind the point where the best fitting distribution changesbyshowingdifferent hazard rate trend

17. Modeling of Hazard Rate Curve Time to Failure/month • Forward analysis 1 2 3 4 5 6 7 8 …………………………… Mf……………………………………………..…………………………..36 includesfieldreturnsthat can haveall TTF valuesbetween 1 andMf Mostlikelihooddistribution • Results : At end of each interval analysis, decreasing hazard rate trend was observed for this filtered data. Weibull++ offered most commonly Weibull distribution in addition to Lognormal and Gamma distributions What is the hazard rate trend?

18. Modeling of Hazard Rate Curve • Backward analysis Results : - Exponential distribution for , constant hazar rate - Weibull, Lognormal and Gamma Distribution for decreasinghazard rate 1 2 3 4 5 6 7 8 …………………………… Mb …………………………………….……………33 34 35 36 includesfieldreturnsthat can haveall TTF valuesbetweenMband 36 month

19. Modeling of Hazard Rate Curve M > 14 Exponential Distribution M < 14 Weibull Distribuiton

20. Modeling of Hazard Rate Curve • : overall hazar rate function : indicatorfunction

21. Conclusion • FILTERING • A systematic approach is offered for elimination errors in field return data • To determine hidden errors. • 1) Forward Analysis • 2) Backward Analysis • 3) 6-month Analysis • 18 months at begining of the data seem as problematic • MODELINGOF HAZARD RATE CURVE • Welookforchange of hazard rate tendency • 1) Forward Analysis • 2) Backward Analysis • In the forward analysis we didn’t see a change in the hazard rate shape • But in the backward analysis,exponential distribution fits best between14and 36 months • Thisstudywill be usedby Arçelik • Usefullforhighvolumesales • Thismethods can be generalizedforallfieldreturndatas

22. THANK YOU