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An algorithm for the computationally efficient deductive implementation of the Markov/Cell-to-Cell-Mapping Technique for risk significant scenario identification

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  • Yang, Jun
  • Aldemir, Tunc

Abstract

A backtracking algorithm is proposed for the computationally efficient diagnostic/deductive implementation of the Markov/Cell-to-Cell-Mapping Technique (CCMT). Using a probabilistic mapping of the discretized system space onto itself in discrete time that can account for both epistemic and aleatory uncertainties on a phenomenologically consistent platform, Markov/CCMT allows quantification of probabilistic system evolution in time, as well as tracing of fault propagation throughout the system. The algorithm is illustrated using an example level control system and by identifying possible sequential pathways and risk significant scenarios for a given failure mode of the system. The algorithm allows incremental verification of the fidelity of the model used to represent the physics without increased memory requirements. The results show that the algorithm is scalable to larger systems.

Suggested Citation

  • Yang, Jun & Aldemir, Tunc, 2016. "An algorithm for the computationally efficient deductive implementation of the Markov/Cell-to-Cell-Mapping Technique for risk significant scenario identification," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 1-8.
    • Handle: RePEc:eee:reensy:v:145:y:2016:i:c:p:1-8
    DOI: 10.1016/j.ress.2015.08.013
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    References listed on IDEAS

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    1. 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.
    2. de Saporta, Benoîte & Zhang, Huilong, 2013. "Predictive maintenance for the heated hold-up tank," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 82-90.
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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. 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).
    3. Liang, Zhenglin & Parlikad, Ajith Kumar & Srinivasan, Rengarajan & Rasmekomen, Nipat, 2017. "On fault propagation in deterioration of multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 72-80.
    4. 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).
    5. Yang, Jun & Zou, Bowen & Yang, Ming, 2019. "Bidirectional implementation of Markov/CCMT for dynamic reliability analysis with application to digital I&C systems," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 278-290.

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