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Optimal maintenance planning for repairable multi-component systems subject to dependent competing risks

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  • Nailong Zhang
  • Qingyu Yang

Abstract

Many complex multi-component systems suffer from dependent competing risks. The reliability modeling and maintenance planning of repairable dependent competing risks systems are challenging tasks because the repair of the failed component can change the lifetime of the other components when multiple components fail dependently. This article first proposes a generally dependent latent age model to capture the dependence of competing risks under general component repairs. Based on the proposed reliability model, both system- and component-level periodic inspection-based maintenance polices are considered for repairable multi-component systems that are subject to dependent competing risks. Under the system-level maintenance policy, the entire system is restored to the as-good-as-new state once a failure is detected. While under the component-level maintenance policy, only the failed component is repaired imperfectly. The optimal solution of the system-level policy is obtained by using renewal theory. The optimal solution of the component-level policy, however, cannot be obtained analytically, due to its complex failure and repair characteristics. A simulation-based optimization approach with stochastic approximation is developed to solve the optimization problem for the component-level policy. The developed methods are illustrated by using a cylinder head assembly cell that consists of multiple stations.

Suggested Citation

  • Nailong Zhang & Qingyu Yang, 2015. "Optimal maintenance planning for repairable multi-component systems subject to dependent competing risks," IISE Transactions, Taylor & Francis Journals, vol. 47(5), pages 521-532, May.
    • Handle: RePEc:taf:uiiexx:v:47:y:2015:i:5:p:521-532
    DOI: 10.1080/0740817X.2014.974115
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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. Zhang, Nailong & Si, Wujun, 2020. "Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Fu, Yuqiang & Wang, Jun, 2022. "Optimum periodic maintenance policy of repairable multi-component system with component reallocation and system overhaul," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Xiaohui Chen & Lin Zhang & Ze Zhang, 2020. "An integrated model for maintenance policies and production scheduling based on immune–culture algorithm," Journal of Risk and Reliability, , vol. 234(5), pages 651-663, October.
    5. Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Zhu, Wenjin & Fouladirad, Mitra & Bérenguer, Christophe, 2016. "A multi-level maintenance policy for a multi-component and multifailure mode system with two independent failure modes," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 50-63.
    7. Zhang, Lin & Chen, Xiaohui & Khatab, Abdelhakim & An, Youjun, 2022. "Optimizing imperfect preventive maintenance in multi-component repairable systems under s-dependent competing risks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    9. Zhaojun Yang & Xiaoxu Li & Chuanhai Chen & Hongxun Zhao & Dingyu Yang & Jinyan Guo & Wei Luo, 2019. "Reliability assessment of the spindle systems with a competing risk model," Journal of Risk and Reliability, , vol. 233(2), pages 226-234, April.

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