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A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems

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  • Francesco Di Maio
  • Samuele Baronchelli
  • Enrico Zio

Abstract

Dynamic reliability methods aim at complementing the capability of traditional static approaches (e.g., event trees [ETs] and fault trees [FTs]) by accounting for the system dynamic behavior and its interactions with the system state transition process. For this, the system dynamics is here described by a time‐dependent model that includes the dependencies with the stochastic transition events. In this article, we present a novel computational framework for dynamic reliability analysis whose objectives are i) accounting for discrete stochastic transition events and ii) identifying the prime implicants (PIs) of the dynamic system. The framework entails adopting a multiple‐valued logic (MVL) to consider stochastic transitions at discretized times. Then, PIs are originally identified by a differential evolution (DE) algorithm that looks for the optimal MVL solution of a covering problem formulated for MVL accident scenarios. For testing the feasibility of the framework, a dynamic noncoherent system composed of five components that can fail at discretized times has been analyzed, showing the applicability of the framework to practical cases.

Suggested Citation

  • Francesco Di Maio & Samuele Baronchelli & Enrico Zio, 2015. "A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 142-156, January.
    • Handle: RePEc:wly:riskan:v:35:y:2015:i:1:p:142-156
    DOI: 10.1111/risa.12251
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    References listed on IDEAS

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    Cited by:

    1. Bellaera, R. & Bonifetto, R. & Di Maio, F. & Pedroni, N. & Savoldi, L. & Zanino, R. & Zio, E., 2020. "Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    2. Nicolae Brînzei & Jean-François Aubry, 2018. "Graphs models and algorithms for reliability assessment of coherent and non-coherent systems," Journal of Risk and Reliability, , vol. 232(2), pages 201-215, April.
    3. Tosoni, E. & Salo, A. & Govaerts, J. & Zio, E., 2019. "Comprehensiveness of scenarios in the safety assessment of nuclear waste repositories," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 561-573.
    4. Pietro Turati & Nicola Pedroni & Enrico Zio, 2017. "An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems," Risk Analysis, John Wiley & Sons, vol. 37(1), pages 147-159, January.
    5. Edoardo Tosoni & Ahti Salo & Enrico Zio, 2018. "Scenario Analysis for the Safety Assessment of Nuclear Waste Repositories: A Critical Review," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 755-776, April.

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