Reliability Prediction of Electronic Boards by

Analyzing Field Return DataAuthors: Vehbi Cömert (Presenter)

Mustafa Altun Hadi Yadavari Ertunç Ertürk

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•Performing a reliability analysis using a real field return data

•Motivation: Modeling hazard rate curve and making accurate reliability prediction

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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

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Field Return Data

• The field return data ,that we use, belongs to Arçelik (Beko), one of the biggest white apliance company in Europe • It is a warranty data and includes - 1 million sales - 3000 warranty claims - We have first 54 months of the data• Warranty involves 36 months.

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Electronics Reliability

• Good reliability• Expected long life• Usually catastrophic failures • Decreasing or constant hazard rate• Hard to see wear out signs

0 1000 2000 3000 4000 5000 60000

1

2

3x 10-5

Time /DayH

azar

d R

ate

A Sample Bathtub Curve for an Electronic Board

EarlyFailure

Wear out

Useful Life

exceeding 10 years

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FilteringEliminating errors in field return data

Step 1 : Eliminating Obvious Errors

Step 2 : Eliminating Hidden ErrorsStage 1: Forward analysis

Stage 2: Backward analysis

Stage 3: 6-month analysis

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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?

Assembly date Return Date

11 November 2011 12 July 2016

Missing Claims

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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 dateUnknown return dateZero time to failureNegative time to failureUnreasonable time to failure

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Filtering Process• Step -2: Investigate the 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 0-6 0-12 0-18 0-24 0-30 0-36 0-42 0-48 0-54

00.5

11.5

22.5

33.5

44.5

55.5

65.284

1.8621.485

0.707 0.663 0.679 0.701 0.702 0.714

Filtering Process• Step -2 stage1 : Forward Analysis

1 6 12 18 24 30 36 42 48 54

problematic

Assembly date/Month

Weibull Fitting

𝛽

values for forward time intervals

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Filtering Process

• Step-2 stage-2: Backward analysis

1 6 12 18 24 30 36 42 48 54

47--54 43--54 37--54 31--54 25--54 19--54 13--54 7--540

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6 1.517

1.140.995

0.923 0.865 0.8 0.771 0.744

values for backward time intervals

Lack of return data toward end of the time

Assembly date/Month

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Filtering Process• Step-2 stage-3: 6 - month periods analysis

0 6 12 18 24 30 36 42 48 54

0—6 7—12 13--18 19--24 25--30 31--36 37--42 43--48 49--540

0.51

1.52

2.53

3.54

4.55

5.56

5.284

2.157 1.943

0.6 0.747 0.842 0.954 11.517

values for 6-month periods

problematic

Assembly date/Month

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0-6 0-12 0-18 0-24 0-30 0-36 0-42 0-48 0-540

0.51

1.52

2.53

3.54

4.55

5.56

5.284

1.8621.485

0.707 0.663 0.679 0.701 0.702 0.714

Filtering Process

values for forward time intervals

problematic

47--54 43--54 37--54 31--54 25--54 19--54 13--54 7--540

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6 1.517

1.140.995 0.923 0.865 0.8 0.771 0.744

values for backward time intervals

0—6

7—12 13--18 19--24 25--30 31--36 37--42 43--48 49--540

1

2

3

4

5

65.284

2.157 1.943

0.6 0.747 0.842 0.954 11.517

values for 6-month periods

problematic

First three intervals (1-18 months) should be filtered.

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Modeling of hazard rate curve To obtain an accurate hazard rate curve Searching points where the hazard rate tendency changes Forward and Backward analysis

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Modeling of Hazard Rate Curve

0 1000 2000 3000 4000 5000 60000

1

2

3x 10-5

Time /Day

Haz

ard

Rat

e

A Sample Bathtub Curve for an Electronic Board

EarlyFailure

Wear out

Useful Life

exceeding 10 years

Change Point (): From decreasing rate trend to constant rate trend

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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++ and fitting with respect to different time intervals.

- Trying to find the point where the best fitting distribution changes by showing different hazard rate trend

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• Forward analysis

Modeling of Hazard Rate Curve

1 2 3 4 5 6 7 8 …………………………… Mf ……………………………………………..…………………………..36

Time to Failure/month

includes field returns that can have all TTF values between 1 and Mf

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 distributionsWhat is the hazard

rate trend?

Most likelihood distribution

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Modeling of Hazard Rate Curve

• Backward analysis

Results : - Exponential distribution for , constant hazar rate

- Weibull, Lognormal and Gamma Distribution for decreasing hazard rate

1 2 3 4 5 6 7 8 …………………………… Mb …………………………………….……………33 34 35 36

includes field returns that can have all TTF values between Mb and 36 month

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Modeling of Hazard Rate Curve

(𝑂𝑣𝑒𝑟𝑎𝑙h𝑎𝑧𝑎𝑟𝑑 𝑟𝑎𝑡𝑒 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛)h𝑜 (𝑡 )={ h1 ,𝑡<𝜏 (𝑊𝑒𝑖𝑏𝑢𝑙𝑙𝐷𝑖𝑠𝑡 .)h2 , 𝑡≥𝜏 (𝐸𝑥𝑝𝑜𝑛𝑒𝑛𝑡𝑖𝑎𝑙𝐷𝑖𝑠𝑡 .)

0 500 1000 1500 20002

4

6

810

t

Hazard rate vs. time plot of Weibull distribution

Haz

ard

Rat

e

x10-6

M < 14 Weibull Distribuiton

0 500 1.000 1.500 20000

2

4

6

8

10

t

Hazard rate vs. time plot of exp. distribution

Haz

ard

rate

x10-6

M > 14 Exponential Distribution

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Modeling of Hazard Rate Curve

• : overall hazar rate function : indicator function

0 500 1000 1500 20002

4

6

810

t

Hazard rate vs. time plot for ho(t), h1(t) and h2(t)

Hazard

rate Overall hazard rate function, ho(t)

Exponential hazard rate function, h2(t)

Weibull hazard rate function,h1(t)

x10-6

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Conclusion

• This study will be used by Arçelik • Usefull for high volume sales• This methods can be generalized for all field return datas

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

MODELING OF HAZARD RATE CURVE• We look for change 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 between 14 and 36 months

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THANK YOU