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A generalized model for recurrent failures prediction

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  • Yevkin, Alexander
  • Krivtsov, Vasiliy

Abstract

There is a variety of models available for repairable systems with general repairs. Most popular are the Kijima models (reflecting the generalized renewal process of recurrent failures) and the Lam model (reflecting the geometric process). The Kijima models relating system's real and virtual ages can be thought of as the time shift transformation, whereas the Lam model – as the time scale transformation. In this paper, a new model is proposed that combines these two fundamental transformations within a new probabilistic formulation. Besides probabilistic aspect of the model, the maximum likelihood estimation of model parameters is discussed, and its performance is illustrated through several numerical examples using both simulated and real–life data. The efficient Monte Carlo calculation method is suggested for the expected number of recurrent events, unavailability, and the failure intensity function.

Suggested Citation

  • Yevkin, Alexander & Krivtsov, Vasiliy, 2020. "A generalized model for recurrent failures prediction," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306268
    DOI: 10.1016/j.ress.2020.107125
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    References listed on IDEAS

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    1. Tanwar, Monika & Rai, Rajiv N. & Bolia, Nomesh, 2014. "Imperfect repair modeling using Kijima type generalized renewal process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 24-31.
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    3. Wu, Shaomin, 2019. "A failure process model with the exponential smoothing of intensity functions," European Journal of Operational Research, Elsevier, vol. 275(2), pages 502-513.
    4. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    5. Shaomin Wu, 2018. "Doubly geometric processes and applications," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(1), pages 66-77, January.
    6. W. John Braun & Wei Li & Yiqiang Q. Zhao, 2005. "Properties of the geometric and related processes," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(7), pages 607-616, October.
    7. Doyen, Laurent & Gaudoin, Olivier & Syamsundar, Annamraju, 2017. "On geometric reduction of age or intensity models for imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 40-52.
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    Cited by:

    1. Hu, Wei & Westerlund, Per & Hilber, Patrik & Chen, Chuanhai & Yang, Zhaojun, 2022. "A general model, estimation, and procedure for modeling recurrent failure process of high-voltage circuit breakers considering multivariate impacts," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Chehade, Abdallah & Savargaonkar, Mayuresh & Krivtsov, Vasiliy, 2022. "Conditional Gaussian mixture model for warranty claims forecasting," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    3. Hu, Wei & Yang, Zhaojun & Chen, Chuanhai & Wu, Yue & Xie, Qunya, 2021. "A Weibull-based recurrent regression model for repairable systems considering double effects of operation and maintenance: A case study of machine tools," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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