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Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring

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  • Tang, Diyin
  • Makis, Viliam
  • Jafari, Leila
  • Yu, Jinsong

Abstract

In this paper, we present an optimal preventive maintenance policy and develop a procedure for residual life estimation for a slowly degrading system subject to soft failure and condition monitoring at equidistant, discrete time epochs. An autoregressive model with time effect is considered to describe the system degradation, which utilizes both the system current age and the previous state observations. The class of control-limit maintenance policies with two different inspection strategies is considered, and the optimization problem is formulated and solved in a semi-Markov decision process framework. The objective is to minimize the long-run expected average cost. A formula for the mean residual life is derived for the proposed degradation model and a control limit policy, which enables the estimation of the remaining useful life and early maintenance planning based on the observed degradation process. An example is presented to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Tang, Diyin & Makis, Viliam & Jafari, Leila & Yu, Jinsong, 2015. "Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 198-207.
    • Handle: RePEc:eee:reensy:v:134:y:2015:i:c:p:198-207
    DOI: 10.1016/j.ress.2014.10.015
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    References listed on IDEAS

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    Cited by:

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    6. Zengqiang Jiang & Dragan Banjevic & Mingcheng E & Bing Li, 2017. "Optimizing the re-profiling policy regarding metropolitan train wheels based on a semi-Markov decision process," Journal of Risk and Reliability, , vol. 231(5), pages 495-507, October.
    7. Chen, Zhen & Li, Yaping & Xia, Tangbin & Pan, Ershun, 2019. "Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 123-136.
    8. Yaping Li & Enrico Zio & Ershun Pan, 2021. "An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction," Journal of Risk and Reliability, , vol. 235(5), pages 831-844, October.
    9. Zhang, Yunzheng & Zhang, Xiaohong & Zeng, Jianchao & Wang, Jinhe & Xue, Songdong, 2019. "Lessees’ satisfaction and optimal condition-based maintenance policy for leased system," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Lee, Jinwoo & Kwon, Daeil & Kim, Namhun & Lee, Changyong, 2019. "PHM-based wiring system damage estimation for near zero downtime in manufacturing facilities," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 213-218.

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