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Reliability analysis of repairable systems with recurrent misuse-induced failures and normal-operation failures

Author

Listed:
  • Peng, Weiwen
  • Shen, Lijuan
  • Shen, Yan
  • Sun, Qiuzhuang

Abstract

Failure of a repairable system may be attributed to operators’ misuse or system deterioration. The misuse may further deteriorate the system under normal operating conditions. Motivated by a real-world data set that records the recurrence times of misuse-induced failures and the normal-operation failures, this study proposes a stochastic process model for recurrence data analysis, where one type of failures is affected by the other. A non-homogeneous Poisson process and a trend-renewal process are separately used as the baseline event process models for the misuse-induced failures and the normal-operation failures, respectively. These two models are then combined by treating the event count of misuse-induced failures as covariate of the event process of normal-operation failures. A Bayesian framework is developed for parameter estimation and dependence tests of the two failure modes. A simulation study and the recurrence data from a manufacturing system are used to demonstrate the proposed method.

Suggested Citation

  • Peng, Weiwen & Shen, Lijuan & Shen, Yan & Sun, Qiuzhuang, 2018. "Reliability analysis of repairable systems with recurrent misuse-induced failures and normal-operation failures," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 87-98.
    • Handle: RePEc:eee:reensy:v:171:y:2018:i:c:p:87-98
    DOI: 10.1016/j.ress.2017.11.016
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    Citations

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    Cited by:

    1. Almeida, Marco Pollo & Paixão, Rafael S. & Ramos, Pedro L. & Tomazella, Vera & Louzada, Francisco & Ehlers, Ricardo S., 2020. "Bayesian non-parametric frailty model for dependent competing risks in a repairable systems framework," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Zhongzhe Chen & Shuchen Cao & Zijian Mao, 2017. "Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach," Energies, MDPI, vol. 11(1), pages 1-14, December.
    3. Yang, Li & Ye, Zhi-sheng & Lee, Chi-Guhn & Yang, Su-fen & Peng, Rui, 2019. "A two-phase preventive maintenance policy considering imperfect repair and postponed replacement," European Journal of Operational Research, Elsevier, vol. 274(3), pages 966-977.
    4. Jiang, Renyan & Li, Fengping & Xue, Wei & Cao, Yu & Zhang, Kunpeng, 2023. "A robust mean cumulative function estimator and its application to overhaul time optimization for a fleet of heterogeneous repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    5. Brenière, Léa & Doyen, Laurent & Bérenguer, Christophe, 2020. "Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Zhou, Yu & Kou, Gang & Xiao, Hui & Peng, Yi & Alsaadi, Fawaz E., 2020. "Sequential imperfect preventive maintenance model with failure intensity reduction with an application to urban buses," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    7. de Oliveira, Cícero Carlos Felix & Firmino, Paulo Renato Alves & Cristino, Cláudio Tadeu, 2019. "A tool for evaluating repairable systems based on Generalized Renewal Processes," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 281-297.
    8. 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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