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Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment

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  • Wang, Wei
  • Maio, Francesco Di
  • Zio, Enrico

Abstract

Multi-State Physics Modeling (MSPM) provides a physics-based semi-Markov modeling framework for a more detailed reliability assessment. In this work, a three-loop Monte Carlo (MC) simulation scheme is proposed to operationalize the MSPM approach, quantifying and controlling the uncertainty affecting the system reliability model. The proposed MC simulation scheme involves three steps: (i) the identification of the system components that deserve MSPM, (ii) the quantification of the uncertainties in the MSPM component models and their propagation onto the system-level model, and (iii) the selection of the most suitable modeling alternative that balances the computational demand for the system model solution and the robustness of the system reliability estimates.

Suggested Citation

  • Wang, Wei & Maio, Francesco Di & Zio, Enrico, 2017. "Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 276-289.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:276-289
    DOI: 10.1016/j.ress.2017.06.003
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    References listed on IDEAS

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    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. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
    5. Dong, Wenjie & Liu, Sifeng & Tao, Liangyan & Cao, Yingsai & Fang, Zhigeng, 2019. "Reliability variation of multi-state components with inertial effect of deteriorating output performances," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 176-185.
    6. Seyed Mohammad Asadzadeh & Hadi Maleki & Mehrab Tanhaeean, 0. "A resilience engineering-based approach to improving service reliability in maintenance organizations," 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. 0, pages 1-14.
    7. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Seyed Mohammad Asadzadeh & Hadi Maleki & Mehrab Tanhaeean, 2020. "A resilience engineering-based approach to improving service reliability in maintenance organizations," 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. 11(5), pages 909-922, October.
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    11. Liu, Jianqiao & Zou, Yanhua & Wang, Wei & Zio, Enrico & Yuan, Chengwei & Wang, Taorui & Jiang, Jianjun, 2022. "A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    12. Liu, Yu & Liu, Qinzhen & Xie, Chaoyang & Wei, Fayuan, 2019. "Reliability assessment for multi-state systems with state transition dependency," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 276-288.

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