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Generalized Boolean logic Driven Markov Processes: A powerful modeling framework for Model-Based Safety Analysis of dynamic repairable and reconfigurable systems

机译：广义布尔逻辑驱动的马尔可夫过程：一个强大的建模框架，用于基于模型的动态可修复和可重构系统的安全性分析

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This paper presents a modeling framework that permits to describe in an integrated manner the structure of the critical system to analyze, by using an enriched fault tree, the dysfunctional behavior of its components, by means of Markov processes, and the reconfiguration strategies that have been planned to ensure safety and availability, with Moore machines. This framework has been developed from BDMP (Boolean logic Driven Markov Processes), a previous framework for dynamic repairable systems. First, the contribution is motivated by pinpointing the limitations of BDMP to model complex reconfiguration strategies and the failures of the control of these strategies. The syntax and semantics of GBDMP (Generalized Boolean logic Driven Markov Processes) are then formally defined; in particular, an algorithm to analyze the dynamic behavior of a GBDMP model is developed. The modeling capabilities of this framework are illustrated on three representative examples. Last, qualitative and quantitative analysis of GDBMP models highlight the benefits of the approach.
机译：本文提供了一个建模框架，该框架允许以综合的方式描述关键系统的结构，以通过使用丰富的故障树，通过马尔可夫过程和其重构策略来分析其组件的功能失常行为。计划使用摩尔机器确保安全性和可用性。该框架是根据BDMP（布尔逻辑驱动的马尔可夫过程）开发的，后者是动态可修复系统的先前框架。首先，通过精确指出BDMP对复杂的重新配置策略进行建模的局限性以及这些策略的控制失败来激发这一贡献。然后正式定义GBDMP（广义布尔逻辑驱动的马尔可夫过程）的语法和语义；特别是，开发了一种用于分析GBDMP模型动态行为的算法。在三个有代表性的示例中说明了此框架的建模功能。最后，GDBMP模型的定性和定量分析突出了该方法的好处。

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