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A Bayesian reliability evaluation method with different types of data from multiple sources

Author

Listed:
  • Wang, Lizhi
  • Pan, Rong
  • Wang, Xiaohong
  • Fan, Wenhui
  • Xuan, Jinquan

Abstract

Bernoulli data (pass/fail), lifetime data, and degradation data are commonly encountered in product reliability assessment. Oftentimes these data are collected from different sources (such as field use, accelerated tests, history, and so on), and it is desirable to utilize these heterogeneous data within one computational framework to provide a comprehensive evaluation of product reliability. In this paper, three Bayesian inference models are proposed to establish the relationship among pass/fail-type Bernoulli data, lifetime data, and degradation data, and to integrate them to solve relevant problems and improve the accuracy of reliability prediction. The proposed methods are demonstrated by a synthetic example and two real examples. The evaluation results can be used for formulating product development strategies.

Suggested Citation

  • Wang, Lizhi & Pan, Rong & Wang, Xiaohong & Fan, Wenhui & Xuan, Jinquan, 2017. "A Bayesian reliability evaluation method with different types of data from multiple sources," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 128-135.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:128-135
    DOI: 10.1016/j.ress.2017.05.039
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    References listed on IDEAS

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    3. Jackson, Chris & Mosleh, Ali, 2016. "Bayesian inference with overlapping data: Reliability estimation of multi-state on-demand continuous life metric systems with uncertain evidence," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 124-135.
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    Cited by:

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    2. Xu, Yingchun & Yao, Wen & Zheng, Xiaohu & Chen, Xiaoqian, 2020. "An iterative information integration method for multi-level system reliability analysis based on Bayesian Melding Method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    3. Zhao, Xiujie & He, Shuguang & Xie, Min, 2018. "Utilizing experimental degradation data for warranty cost optimization under imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 108-119.
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    5. Chen, Wen-Bin & Li, Xiao-Yang & Kang, Rui, 2022. "Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Yingchun Xu & Xiaohu Zheng & Wen Yao & Ning Wang & Xiaoqian Chen, 2021. "A sequential multi-prior integration and updating method for complex multi-level system based on Bayesian melding method," Journal of Risk and Reliability, , vol. 235(5), pages 863-876, October.
    7. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    8. Liu, Di & Wang, Shaoping, 2021. "Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    9. Kowal, Karol, 2022. "Lifetime reliability and availability simulation for the electrical system of HTTR coupled to the electricity-hydrogen cogeneration plant," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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