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Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures

Author

Listed:
  • Francesco Di Maio

    (POLIMI - Politecnico di Milano [Milan])

  • Chiara Pettorossi

    (POLIMI - Politecnico di Milano [Milan])

  • Enrico Zio

    (POLIMI - Politecnico di Milano [Milan], CRC - Centre de recherche sur les Risques et les Crises - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres)

Abstract

The reliability of critical infrastructures, such as power distribution networks, is of key importance for modern societies. The reliability of such complex systems can, in principle, be assessed by Monte Carlo simulation. However, the size and complexity of these systems, and the rarity of the failure events, can make the calculations quite demanding. Survival signature can help to address this issue, as it allows modelling the structure of the system separately from the probabilistic modelling for the reliability assessment. However, the survival signature calculation of complex, multi-component systems for their reliability assessment suffers from the curse of dimensionality, and both analytical calculation and Monte Carlo Simulation (MCS) are not feasible in practice. Then, in this work, we propose a novel approach to approximate the survival signature of a system, which stands on the use of entropy to drive the sampling by MCS towards non-trivial system structure configurations, so as to save computational cost. The approach is exemplified by calculating the reliability of a generic synthetic multi-component network and the feasibility of its application is shown on a real-world network.

Suggested Citation

  • Francesco Di Maio & Chiara Pettorossi & Enrico Zio, 2023. "Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures," Post-Print hal-04103855, HAL.
    • Handle: RePEc:hal:journl:hal-04103855
    DOI: 10.1016/j.ress.2022.108982
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    Cited by:

    1. Wang, Shaoxuan & Yao, Yuantao & Ge, Daochuan & Lin, Zhixian & Wu, Jie & Yu, Jie, 2023. "Reliability evaluation of standby redundant systems based on the survival signatures methods," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Ahmed, Shoaib & Li, Tie & Yi, Ping & Chen, Run, 2023. "Environmental impact assessment of green ammonia-powered very large tanker ship for decarbonized future shipping operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

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