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A subset simulation analysis framework for rapid reliability evaluation of series-parallel cold standby systems

Author

Listed:
  • Lin, Zhixian
  • Tao, Longlong
  • Wang, Shaoxuan
  • Yong, Nuo
  • Xia, Dongqin
  • Wang, Jianye
  • Ge, Daochuan

Abstract

Due to its capacity to increase system reliability, cold standby redundancy design has attracted considerable attention and has been applied in crucial-safety engineering systems. Cold standby systems are often featured with small failure probability and components having more mixed time-to-failure distribution types. It is significant to evaluate the reliability of cold standby systems. But the existing analysis methods still have some limitations. The sequential binary decision diagram (SBDD) method is time-consuming particularly for those with more mixed time-to-failure distribution types. The conventional Monte Carlo simulation (MCS) method is ineffective for rare events. In this contribution, to address these issues, a hybrid subset simulation method paired with limit state equations for series-parallel cold standby systems is proposed. Firstly, the limit state equations for series-parallel cold standby systems are established; secondly, to efficiently produce conditional time-to-failure sample points for components, the adaptive Markov Chain Monte Carlo technique with an optimal scaling technique is introduced; thirdly, an integrated framework for performing the reliability assessment of series-parallel cold standby systems is created. To show the effectiveness and efficiency of the proposed method, three numerical case studies are implemented. The results demonstrated that the proposed method is more effective than the SBDD and MCS methods for small probability events.

Suggested Citation

  • Lin, Zhixian & Tao, Longlong & Wang, Shaoxuan & Yong, Nuo & Xia, Dongqin & Wang, Jianye & Ge, Daochuan, 2024. "A subset simulation analysis framework for rapid reliability evaluation of series-parallel cold standby systems," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023006208
    DOI: 10.1016/j.ress.2023.109706
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    References listed on IDEAS

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