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Construction of event-tree/fault-tree models from a Markov approach to dynamic system reliability

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  • Bucci, Paolo
  • Kirschenbaum, Jason
  • Mangan, L. Anthony
  • Aldemir, Tunc
  • Smith, Curtis
  • Wood, Ted

Abstract

While the event-tree (ET)/fault-tree (FT) methodology is the most popular approach to probability risk assessment (PRA), concerns have been raised in the literature regarding its potential limitations in the reliability modeling of dynamic systems. Markov reliability models have the ability to capture the statistical dependencies between failure events that can arise in complex dynamic systems. A methodology is presented that combines Markov modeling with the cell-to-cell mapping technique (CCMT) to construct dynamic ETs/FTs and addresses the concerns with the traditional ET/FT methodology. The approach is demonstrated using a simple water level control system. It is also shown how the generated ETs/FTs can be incorporated into an existing PRA so that only the (sub)systems requiring dynamic methods need to be analyzed using this approach while still leveraging the static model of the rest of the system.

Suggested Citation

  • Bucci, Paolo & Kirschenbaum, Jason & Mangan, L. Anthony & Aldemir, Tunc & Smith, Curtis & Wood, Ted, 2008. "Construction of event-tree/fault-tree models from a Markov approach to dynamic system reliability," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1616-1627.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:11:p:1616-1627
    DOI: 10.1016/j.ress.2008.01.008
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    References listed on IDEAS

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    1. David K. Backus & Bryan R. Routledge & Stanley E. Zin, 2005. "Exotic Preferences for Macroeconomists," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 319-414, National Bureau of Economic Research, Inc.
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    Cited by:

    1. KIM, Junyung & ZHAO, Xingang & SHAH, Asad Ullah Amin & KANG, Hyun Gook, 2021. "System risk quantification and decision making support using functional modeling and dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2012. "Dynamic risk analysis using bow-tie approach," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 36-44.
    3. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    4. Wu, Xingguang & Huang, Huirong & Xie, Jianyu & Lu, Meixing & Wang, Shaobo & Li, Wang & Huang, Yixuan & Yu, Weichao & Sun, Xiaobo, 2023. "A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Bjorkman, Kim, 2013. "Solving dynamic flowgraph methodology models using binary decision diagrams," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 206-216.
    6. Wu, Daohua & Zheng, Wei, 2018. "Formal model-based quantitative safety analysis using timed Coloured Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 62-79.
    7. Aldemir, T. & Guarro, S. & Mandelli, D. & Kirschenbaum, J. & Mangan, L.A. & Bucci, P. & Yau, M. & Ekici, E. & Miller, D.W. & Sun, X. & Arndt, S.A., 2010. "Probabilistic risk assessment modeling of digital instrumentation and control systems using two dynamic methodologies," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1011-1039.
    8. Lam, C.Y. & Cruz, A.M., 2019. "Risk analysis for consumer-level utility gas and liquefied petroleum gas incidents using probabilistic network modeling: A case study of gas incidents in Japan," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 198-212.
    9. Zengkai Liu & Yonghong Liu & Baoping Cai, 2014. "Reliability Analysis of the Electrical Control System of Subsea Blowout Preventers Using Markov Models," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    10. Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    11. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    12. Dhople, S.V. & DeVille, L. & Domínguez-García, A.D., 2014. "A Stochastic Hybrid Systems framework for analysis of Markov reward models," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 158-170.
    13. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    14. Guanquan, Chu & Jinhui, Wang, 2012. "Study on probability distribution of fire scenarios in risk assessment to emergency evacuation," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 24-32.
    15. Mandelli, Diego & Yilmaz, Alper & Aldemir, Tunc & Metzroth, Kyle & Denning, Richard, 2013. "Scenario clustering and dynamic probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 146-160.

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