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

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  • Altun, Mustafa
  • Comert, Salih Vehbi

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.

Suggested Citation

  • Altun, Mustafa & Comert, Salih Vehbi, 2016. "A change-point based reliability prediction model using field return data," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 175-184.
    • Handle: RePEc:eee:reensy:v:156:y:2016:i:c:p:175-184
    DOI: 10.1016/j.ress.2016.07.024
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    References listed on IDEAS

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    Cited by:

    1. Pan, Xing & Wang, Huixiong & You, Weijia & Zhang, Manli & Yang, Yuexiang, 2020. "Assessing the reliability of electronic products using customer knowledge discovery," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    2. Zhang, Jian-Xun & Hu, Chang-Hua & He, Xiao & Si, Xiao-Sheng & Liu, Yang & Zhou, Dong-Hua, 2017. "Lifetime prognostics for deteriorating systems with time-varying random jumps," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 338-350.
    3. Gupta, Sanjib Kumar & Bhattacharya, Debasis, 2022. "Non-parametric estimation of bivariate reliability from incomplete two-dimensional warranty data," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Lin, Kunsong & Chen, Yunxia, 2021. "Analysis of two-dimensional warranty data considering global and local dependence of heterogeneous marginals," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Gupta, Sanjib Kumar & Chattopadhyay, Gaurangadeb, 2022. "Early detection of reliability related problems from two-dimensional warranty data considering labour code priority index," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Cai, Xia & Tian, Yubin & Ning, Wei, 2019. "Change-point analysis of the failure mechanisms based on accelerated life tests," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 515-522.
    7. Liu, Di & Wang, Shaoping, 2021. "An artificial neural network supported stochastic process for degradation modeling and prediction," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Alessandra R. Brazzale & Helmut Küchenhoff & Stefanie Krügel & Tobias S. Schiergens & Heiko Trentzsch & Wolfgang Hartl, 2019. "Nonparametric change point estimation for survival distributions with a partially constant hazard rate," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 301-321, April.

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