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A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems
Author(s) -
Maio Francesco Di,
Baronchelli Samuele,
Zio Enrico
Publication year - 2015
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12251
Subject(s) - fault tree analysis , reliability (semiconductor) , implicant , discretization , computer science , prime (order theory) , identification (biology) , process (computing) , algorithm , event (particle physics) , state (computer science) , mathematical optimization , mathematics , reliability engineering , engineering , boolean function , mathematical analysis , power (physics) , boolean expression , physics , botany , quantum mechanics , combinatorics , biology , operating system
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.