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首页> 外文期刊>IEEE Transactions on Reliability >An algorithm to find minimal cuts of coherent fault-trees with event-classes, using a decision tree
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An algorithm to find minimal cuts of coherent fault-trees with event-classes, using a decision tree

机译:使用决策树找到具有事件类的相干故障树的最小割的算法

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A new algorithm, the Carrasco-Sune minimal cuts (CS-MC) algorithm for computing the minimal cuts of s-coherent fault trees is presented. Input events of the fault tree are assumed classified into classes, where events of the same class are indistinguishable. This allows capturing some symmetries which some systems exhibit. CS-MC uses a decision tree. The search implemented by the decision tree is guided by heuristics which try to make the CS-MC algorithm as efficient as possible. In addition, an irrelevance test on the inputs of the fault tree is used to prune the search. The performance of CS-MC is illustrated and compared with the basic top-down and bottom-up algorithms using a set of fault trees, some of which are very difficult. The CS-MC performs very well even in the difficult examples, and the memory requirements of CS-MC are small.
机译:提出了一种新的算法,即Carrasco-Sune最小割(CS-MC)算法,用于计算s相干故障树的最小割。假定故障树的输入事件被分类为多个类别,其中相同类别的事件是无法区分的。这允许捕获某些系统表现出的某些对称性。 CS-MC使用决策树。由决策树执行的搜索是在启发式技术的指导下进行的,这些启发式技术试图使CS-MC算法尽可能高效。另外,使用对故障树的输入的不相关性测试来修剪搜索。阐述了CS-MC的性能,并与使用一组故障树的基本自上而下和自下而上的算法进行了比较,其中有些非常困难。即使在困难的示例中,CS-MC的性能也非常好,并且CS-MC的内存需求很小。

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