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A Multiple-Valued Decision-Diagram-Based Approach to Solve Dynamic Fault Trees

机译:基于多值决策图的动态故障树求解方法

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Dynamic fault trees (DFTs) have been used for many years because they can easily provide a concise representation of the dynamic failure behaviors of general non-repairable fault tolerant systems. However, when repeated failure events appear in real-life DFT models, the traditional modularization-based DFT analysis process can still generate large dynamic subtrees, the modeling of which can lead to a state explosion problem. Examples of these kinds of large dynamic subtrees abound in models of real-world dynamic software and embedded computing systems integrating with various multi-function components. This paper proposes an efficient, multiple-valued decision-diagram (MDD)-based DFT analysis approach for computing the reliability of large dynamic subtrees. Unlike the traditional modularization methods where the whole dynamic subtree must be solved using state-space methods, the proposed approach restricts the state-space method only to components associated with dynamic failure behaviors within the dynamic subtree. By using multiple-valued variables to encode the dynamic gates, a single compact MDD can be generated to model the failure behavior of the overall system. The combination of MDD and state-space methods applied at the component or gate level helps relieve the state explosion problem of the traditional modularization method, for the problems we explore. Applications and advantages of the proposed approach are illustrated through detailed analyses of an example DFT, and through two case studies.
机译:动态故障树(DFT)已经使用了很多年,因为它们可以轻松地提供一般不可修复的容错系统的动态故障行为的简明表示。但是,当现实生活中的DFT模型中出现重复的故障事件时,传统的基于模块化的DFT分析过程仍会生成大型动态子树,对其进行建模会导致状态爆炸问题。此类大型动态子树的示例在集成了各种多功能组件的实际动态软件和嵌入式计算系统模型中比比皆是。本文提出了一种有效的,基于多值决策图(MDD)的DFT分析方法,用于计算大型动态子树的可靠性。与必须使用状态空间方法解决整个动态子树的传统模块化方法不同,所提出的方法仅将状态空间方法限制为与动态子树中的动态故障行为相关的组件。通过使用多值变量对动态门进行编码,可以生成单个紧凑的MDD来对整个系统的故障行为进行建模。对于我们探讨的问题,在组件或门级应用MDD和状态空间方法的组合有助于缓解传统模块化方法的状态爆炸问题。通过对示例DFT的详细分析以及两个案例研究,说明了该方法的应用和优点。

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