A change-point based reliability prediction model using field return data

Mustafa Altun*, Salih Vehbi Comert

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

In this study, we propose an accurate reliability prediction model for high-volume complex electronic products throughout their warranty periods by using field return data. Our model has a specific application to electronics boards with given case studies using 36-month warranty data. Our model is constructed on a Weibull-exponential hazard rate scheme by using the proposed change point detection method based on backward and forward data analysis. We consider field return data as short-term and long-term corresponding to early failure and useful life phases of the products, respectively. The proposed model is evaluated by applying it to four different board data sets. Each data set has between 1500 and 4000 board failures. Our prediction model can make a 36-month (full warranty) reliability prediction of a board with using its field data as short as 3 months. The predicted results from our model and the direct results using full warranty data match well. This demonstrates the accuracy of our model. We also evaluate our change point method by applying it to our board data sets as well as to a well-known heart transplant data set.

Original languageEnglish
Pages (from-to)175-184
Number of pages10
JournalReliability Engineering and System Safety
Volume156
DOIs
Publication statusPublished - 1 Dec 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier Ltd

Funding

This work is cooperated with Arcelik A.S. and supported by the TUBITAK (The Scientific and Technological Council of Turkey) University-Industry Collaboration Grant Program (1505) #5130034 . The support is gratefully acknowledged.

FundersFunder number
Scientific and Technological Council of Turkey5130034
TUBITAK

    Keywords

    • Change point problem
    • Electronics reliability
    • Field return data
    • Warranty forecasting

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