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Conditional reliability-based importance measures

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  • Do, Phuc
  • Bérenguer, Christophe

Abstract

Importance measures have been widely used as important decision-aiding indicators in various purposes such as reliability studies, risk analyses and maintenance optimization. In this work, we propose a novel time-dependent importance measure for systems composed of multiple non repairable components. The proposed importance measure of a component/group of components is defined as its ability to improve the system reliability during a mission given the current conditions (states or degradation levels) of its components. To take into account economic aspects (e.g., maintenance costs, economic dependence between components and the cost benefit thanks to maintenance operations), an extension of the proposed importance measure is then investigated. Thanks to these proposed importance measures, the component/group of components can be “optimally†selected for preventive maintenance regarding to the reliability criteria and/or the financial issues. A numerical example is introduced to illustrate the use and the advantages of the proposed importance measures.

Suggested Citation

  • Do, Phuc & Bérenguer, Christophe, 2020. "Conditional reliability-based importance measures," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:reensy:v:193:y:2020:i:c:s0951832019302121
    DOI: 10.1016/j.ress.2019.106633
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    Cited by:

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    3. Dui, Hongyan & Wei, Xuan & Xing, Liudong, 2023. "A new multi-criteria importance measure and its applications to risk reduction and safety enhancement," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Zhu, Xiaoyan & Chen, Zhiqiang & Borgonovo, Emanuele, 2021. "Remaining-useful-lifetime and system-remaining-profit based importance measures for decisions on preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Zhang, Chengjie & Qi, Faqun & Zhang, Ning & Li, Yong & Huang, Hongzhong, 2022. "Maintenance policy optimization for multi-component systems considering dynamic importance of components," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    6. Zhang, Mimi, 2020. "A heuristic policy for maintaining multiple multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    7. Dinh, Duc-Hanh & Do, Phuc & Iung, Benoit, 2022. "Multi-level opportunistic predictive maintenance for multi-component systems with economic dependence and assembly/disassembly impacts," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
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    9. Phuc Do & Christophe Bérenguer, 2022. "Residual life-based importance measures for predictive maintenance decision-making," Journal of Risk and Reliability, , vol. 236(1), pages 98-113, February.
    10. Lyu, Dong & Si, Shubin, 2021. "Importance measure for K-out-of-n: G systems under dynamic random load considering strength degradation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. Vaisman, Radislav & Sun, Yuting, 2021. "Reliability and importance measure analysis of networks with shared risk link groups," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    12. Dui, Hongyan & Wu, Shaomin & Zhao, Jiangbin, 2021. "Some extensions of the component maintenance priority," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    13. Wang, Yukun & Li, Xiaopeng & Chen, Junyan & Liu, Yiliu, 2022. "A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    14. Oakley, Jordan L. & Wilson, Kevin J. & Philipson, Pete, 2022. "A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    15. Liu, Mingli & Wang, Dan & Zhao, Jiangbin & Si, Shubin, 2022. "Importance measure construction and solving algorithm oriented to the cost-constrained reliability optimization model," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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