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A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems

机译：非相干动态系统中素数蕴含量识别的计算框架

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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 dis-cretized 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.
机译：动态可靠性方法旨在通过考虑系统动态行为及其与系统状态转换过程的相互作用来补充传统静态方法（例如，事件树[ET]和故障树[FT]）的功能。为此，此处通过时间相关的模型描述系统动力学，该模型包括具有随机过渡事件的相关性。在本文中，我们提出了一种用于动态可靠性分析的新颖计算框架，其目标是：i）考虑离散的随机过渡事件，以及ii）识别动态系统的主要隐含因素（PI）。该框架需要采用多值逻辑（MVL）来考虑离散时间的随机转换。然后，最初通过微分进化（DE）算法识别PI，该算法寻找针对MVL事故场景制定的覆盖问题的最佳MVL解决方案。为了测试框架的可行性，分析了由五个可能在离散时间失效的组件组成的动态非相干系统，显示了该框架在实际案例中的适用性。

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