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Semi-Markov models for degradation-based reliability

Author

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  • Jeffrey Kharoufeh
  • Christopher Solo
  • M. Ulukus

Abstract

This article presents hybrid, degradation-based reliability models for a single-unit system whose degradation is driven by a semi-Markov environment. The primary objective is to develop a mathematical framework and associated computational techniques that unite environmental data and stochastic failure models to assess the current or future health of the system. By employing phase-type distributions, it is possible to construct a surrogate environment process that is amenable to analysis by exact Markovian techniques to obtain reliability estimates. The viability of the proposed approach and the quality of the approximations are demonstrated in two numerical experiments. The numerical results indicate that remarkably accurate lifetime distribution and moment approximations are attainable.

Suggested Citation

  • Jeffrey Kharoufeh & Christopher Solo & M. Ulukus, 2010. "Semi-Markov models for degradation-based reliability," IISE Transactions, Taylor & Francis Journals, vol. 42(8), pages 599-612.
    • Handle: RePEc:taf:uiiexx:v:42:y:2010:i:8:p:599-612
    DOI: 10.1080/07408170903394371
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    Cited by:

    1. Girish Kumar & Vipul Jain & Umang Soni, 2019. "Modelling and simulation of repairable mechanical systems reliability and availability," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1221-1233, October.
    2. Jeffrey Kharoufeh & Steven Cox & Mark Oxley, 2013. "Reliability of manufacturing equipment in complex environments," Annals of Operations Research, Springer, vol. 209(1), pages 231-254, October.
    3. Sun, Xuxue & Cai, Wenjun & Li, Mingyang, 2021. "A hierarchical modeling approach for degradation data with mixed-type covariates and latent heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Zhu, Qiushi & Peng, Hao & Timmermans, Bas & van Houtum, Geert-Jan, 2017. "A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs," International Journal of Production Economics, Elsevier, vol. 193(C), pages 365-380.
    5. Liang, Qingzhu & Yang, Yinghao & Peng, Changhong, 2023. "A reliability model for systems subject to mutually dependent degradation processes and random shocks under dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Ram Niwas & M. S. Kadyan & Jitender Kumar, 2016. "MTSF (mean time to system failure) and profit analysis of a single-unit system with inspection for feasibility of repair beyond warranty," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 198-204, December.
    7. Moghaddass, Ramin & Zuo, Ming J., 2012. "A parameter estimation method for a condition-monitored device under multi-state deterioration," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 94-103.
    8. Peng, Weiwen & Li, Yan-Feng & Mi, Jinhua & Yu, Le & Huang, Hong-Zhong, 2016. "Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 75-87.
    9. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    10. Liang, Zhenglin & Liu, Bin & Xie, Min & Parlikad, Ajith Kumar, 2020. "Condition-based maintenance for long-life assets with exposure to operational and environmental risks," International Journal of Production Economics, Elsevier, vol. 221(C).

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