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Reliability performance for dynamic multi-state repairable systems with regimes

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  • Jingyuan Shen
  • Lirong Cui

Abstract

It is well known that many factors can influence the rate at which a machine degrades. In this article, we study a multi-state repairable system subject to continuous degradation and dynamically evolving regimes. The degradation rate of the system depends not only on the system states but also on the regimes driven by the varying external environments. Movements between the system states are governed by continuous-time Markov processes but with different transition rate matrices due to different regimes; meanwhile, the evolution of regime is also governed by a Markov process. Such a system can be developed by the Markov regime-switching model. To derive the system performance such as the first passage time distribution, a Markov renewal process is introduced by giving its semi-Markov kernel. We also consider the system in the context of periodic inspections and maintenance and give the limiting average availability. Finally, some numerical examples are given to demonstrate and validate the proposed framework.

Suggested Citation

  • Jingyuan Shen & Lirong Cui, 2017. "Reliability performance for dynamic multi-state repairable systems with regimes," IISE Transactions, Taylor & Francis Journals, vol. 49(9), pages 911-926, September.
    • Handle: RePEc:taf:uiiexx:v:49:y:2017:i:9:p:911-926
    DOI: 10.1080/24725854.2017.1318228
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    References listed on IDEAS

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    1. Alan Hawkes & Lirong Cui & Zhihua Zheng, 2011. "Modeling the evolution of system reliability performance under alternative environments," IISE Transactions, Taylor & Francis Journals, vol. 43(11), pages 761-772.
    2. Julia S. Benoit & Wenyaw Chan & Rachelle S. Doody, 2015. "Joint coverage probability in a simulation study on continuous-time Markov chain parameter estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2531-2538, December.
    3. Yisha Xiang & David W. Coit & Qianmei (May) Feng, 2014. "Accelerated burn-in and condition-based maintenance for -subpopulations subject to stochastic degradation," IISE Transactions, Taylor & Francis Journals, vol. 46(10), pages 1093-1106, October.
    4. Lei Jiang & Qianmei Feng & David W. Coit, 2015. "Modeling zoned shock effects on stochastic degradation in dependent failure processes," IISE Transactions, Taylor & Francis Journals, vol. 47(5), pages 460-470, May.
    5. 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.
    6. John A. Flory & Jeffrey P. Kharoufeh & Nagi Z. Gebraeel, 2014. "A switching diffusion model for lifetime estimation in randomly varying environments," IISE Transactions, Taylor & Francis Journals, vol. 46(11), pages 1227-1241, November.
    7. Linkan Bian & Nagi Gebraeel & Jeffrey P. Kharoufeh, 2015. "Degradation modeling for real-time estimation of residual lifetimes in dynamic environments," IISE Transactions, Taylor & Francis Journals, vol. 47(5), pages 471-486, May.
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    Cited by:

    1. Ji Hwan Cha & Maxim Finkelstein, 2019. "On some characteristics of quality for systems operating in a random environment," Journal of Risk and Reliability, , vol. 233(2), pages 257-267, April.
    2. Jingyuan Shen & Alaa Elwany & Lirong Cui, 2018. "Reliability modeling for systems degrading in K cyclical regimes based on gamma processes," Journal of Risk and Reliability, , vol. 232(6), pages 754-765, December.
    3. Wenjie Dong & Sifeng Liu & Zhigeng Fang & Yingsai Cao & Ye Ding, 2019. "A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems," Journal of Risk and Reliability, , vol. 233(3), pages 369-378, June.

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