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A stochastic approach for the reliability evaluation of multi-state systems with dependent components

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  • Song, Xiaogang
  • Zhai, Zhengjun
  • Liu, Yidong
  • Han, Jie

Abstract

A multi-state system (MSS) employs more than two discrete states to indicate different performance rates. Methods using a universal generating function (UGF) and Monte Carlo (MC) simulation are primary approaches for the reliability analysis of an MSS. However, these approaches incur a large computational overhead because the number of system states increases significantly with the number of components in an MSS. In this paper, stochastic multi-valued (SMV) models are proposed for evaluating the reliability of an MSS with dependent multi-state components (MSCs). The performance rates and their corresponding probabilities of the MSCs are simultaneously encoded in multi-valued non-Bernoulli sequences using permutations of fixed numbers of 1 s and 0 s. The sequences are then processed by logic gates. The effectiveness of the proposed approach is demonstrated via a comparative evaluation of a multi-state system consisting of dependent components with steady and time-varying state probabilities.

Suggested Citation

  • Song, Xiaogang & Zhai, Zhengjun & Liu, Yidong & Han, Jie, 2018. "A stochastic approach for the reliability evaluation of multi-state systems with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 257-266.
  • Handle: RePEc:eee:reensy:v:170:y:2018:i:c:p:257-266
    DOI: 10.1016/j.ress.2017.10.015
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    References listed on IDEAS

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    5. Lisnianski, Anatoly & Ding, Yi, 2016. "Using inverse Lz-transform for obtaining compact stochastic model of complex power station for short-term risk evaluation," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 19-27.
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    Cited by:

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    2. Kozyra, Paweł Marcin, 2023. "The usefulness of (d,b)-MCs and (d,b)-MPs in network reliability evaluation under delivery or maintenance cost constraints," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Niu, Yi-Feng & Song, Yi-Fan & Xu, Xiu-Zhen & Zhao, Xia, 2022. "Efficient reliability computation of a multi-state flow network with cost constraint," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Eryilmaz, Serkan & Ozkut, Murat, 2020. "Optimization problems for a parallel system with multiple types of dependent components," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    5. Ossai, Chinedu I., 2019. "Remaining useful life estimation for repairable multi-state components subjected to multiple maintenance actions," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 142-151.
    6. Ralf Stetter, 2020. "Approaches for Modelling the Physical Behavior of Technical Systems on the Example of Wind Turbines," Energies, MDPI, vol. 13(8), pages 1-27, April.
    7. Yousefi, Nooshin & Coit, David W. & Song, Sanling, 2020. "Reliability analysis of systems considering clusters of dependent degrading components," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Xu, Xiu-Zhen & Niu, Yi-Feng & Song, Yi-Fan, 2021. "Computing the reliability of a stochastic distribution network subject to budget constraint," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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