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Bi-level corrected residual life-based maintenance for deteriorating systems under competing risks

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  • Duan, Chaoqun
  • Gong, Ting
  • Yan, Liangwen
  • Li, Xinmin

Abstract

The accurate prediction of residual life in deteriorating systems is a challenging task due to uncertainties and idealized assumptions during data acquisition and model construction. To address this challenge, the paper proposes a bi-level maintenance model that incorporates a corrected residual life (CRL) approach for a deteriorating system under competing risks. Specifically, the degradation failure is modeled using a Gamma process, while the competing sudden failure is described by a proportional hazards (PH) model. To reduce inconsistencies in residual life prediction, a CRL model is presented and continuously updated with a dynamic corrective factor at each monitoring epoch. Based on the updated CRL, a bi-level maintenance model is developed, which considers both system availability and maintenance cost objectives, enabling dynamic monitoring of the system's degradation state and residual life. The optimal decision variables in the maintenance model are determined through a multi-objective optimization algorithm formulated within a semi-Markov decision process (SMDP) framework. The unique aspect of this work lies in the consideration of prediction errors in residual life and the incorporation of multi-attribute optimization for maintenance design. The proposed method is validated through a case study on light-emitting diodes, confirming its effectiveness in improving residual life prediction and optimizing maintenance decisions.

Suggested Citation

  • Duan, Chaoqun & Gong, Ting & Yan, Liangwen & Li, Xinmin, 2024. "Bi-level corrected residual life-based maintenance for deteriorating systems under competing risks," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
  • Handle: RePEc:eee:reensy:v:247:y:2024:i:c:s0951832024001431
    DOI: 10.1016/j.ress.2024.110069
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    References listed on IDEAS

    as
    1. Sanling Song & David W. Coit & Qianmei Feng, 2016. "Reliability analysis of multiple-component series systems subject to hard and soft failures with dependent shock effects," IISE Transactions, Taylor & Francis Journals, vol. 48(8), pages 720-735, August.
    2. Zheng, Rui & Zhou, Yifan, 2021. "Comparison of three preventive maintenance warranty policies for products deteriorating with age and a time-varying covariate," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Rafiee, Koosha & Feng, Qianmei & Coit, David W., 2017. "Reliability assessment of competing risks with generalized mixed shock models," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 1-11.
    4. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    5. Zheng, Rui & Chen, Bingkun & Gu, Liudong, 2020. "Condition-based maintenance with dynamic thresholds for a system using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Jafari, L. & Makis, V., 2015. "Joint optimal lot sizing and preventive maintenance policy for a production facility subject to condition monitoring," International Journal of Production Economics, Elsevier, vol. 169(C), pages 156-168.
    7. Najafi, Seyedvahid & Zheng, Rui & Lee, Chi-Guhn, 2021. "An optimal opportunistic maintenance policy for a two-unit series system with general repair using proportional hazards models," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Eryilmaz, Serkan, 2020. "Age-based preventive maintenance for coherent systems with applications to consecutive-k-out-of-n and related systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Olde Keizer, Minou C.A. & Flapper, Simme Douwe P. & Teunter, Ruud H., 2017. "Condition-based maintenance policies for systems with multiple dependent components: A review," European Journal of Operational Research, Elsevier, vol. 261(2), pages 405-420.
    10. 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).
    11. Fan, Mengfei & Zeng, Zhiguo & Zio, Enrico & Kang, Rui, 2017. "Modeling dependent competing failure processes with degradation-shock dependence," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 422-430.
    12. Akram Khaleghei & Viliam Makis, 2016. "Reliability estimation of a system subject to condition monitoring with two dependent failure modes," IISE Transactions, Taylor & Francis Journals, vol. 48(11), pages 1058-1071, November.
    13. Zeng, Zhiguo & Barros, Anne & Coit, David, 2023. "Dependent failure behavior modeling for risk and reliability: A systematic and critical literature review," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    14. Mosayebi Omshi, E. & Grall, A. & Shemehsavar, S., 2020. "A dynamic auto-adaptive predictive maintenance policy for degradation with unknown parameters," European Journal of Operational Research, Elsevier, vol. 282(1), pages 81-92.
    15. Lee, Juseong & Mitici, Mihaela, 2023. "Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. Ji Hwan Cha & Maxim Finkelstein, 2018. "Point Processes for Reliability Analysis," Springer Series in Reliability Engineering, Springer, number 978-3-319-73540-5, September.
    17. Alexandros Bousdekis & Babis Magoutas & Dimitris Apostolou & Gregoris Mentzas, 2018. "Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1303-1316, August.
    18. Zheng, Rui & Wang, Jingjing & Zhang, Yingzhi, 2023. "A hybrid repair-replacement policy in the proportional hazards model," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1011-1021.
    19. de Pater, Ingeborg & Reijns, Arthur & Mitici, Mihaela, 2022. "Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    20. Duan, Chaoqun & Deng, Tongxin & Song, Lei & Wang, Min & Sheng, Bo, 2023. "An adaptive reliability-based maintenance policy for mechanical systems under variable environments," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    21. Xiao Liu & Jingrui Li & Khalifa Al-Khalifa & Abdelmagid Hamouda & David Coit & Elsayed Elsayed, 2013. "Condition-based maintenance for continuously monitored degrading systems with multiple failure modes," IISE Transactions, Taylor & Francis Journals, vol. 45(4), pages 422-435.
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