Fault tree analysis (FTA) is a prominent reliability analysis method, which is widely used in safety-critical industries. Computing the minimal cut sets (MCSs) of a fault tree, i.e., finding all the smallest combinations of the basic events that cause system failures, is a fundamental step in FTA. Since coherent fault trees are the most common in industrial systems in practice, they are the focus of this article. Computing MCSs is a computationally hard problem. Classical methods have been proposed based on manipulation of Boolean expressions and binary decision diagrams. However, given the inherent intractability of computing MCSs in practice, there are still limitations on time and memory in these methods. Therefore, developing new methods over different paradigms remains to be an interesting research direction. In this article, motivated by recent progress on modern Boolean satisfiability problem (SAT) solvers, we present a new method for computing MCSs based on SAT, namely SATMCS . Specifically, given a fault tree, we iteratively search for a cut set based on the conflict-driven clause learning framework. By exploiting local propagation graph, which characterizes the partial failure propagation based on the cut set, we provide efficient algorithms for extracting an MCS. The new MCS is learned as a block clause for SAT solving, and the conflict clauses in iterations are incrementally recorded, which helps to prune search space and ensures completeness of the results. Moreover, we adopt a jump-chronological backtracking strategy to prepare the next iteration, which allows for reusing the same search steps in SAT solving. We compare SATMCS with state-of-the-art commercial tools on practical fault trees. Although SATMCS is only a prototype, it shows comparable performance in time consumption with one tool ( XFTA ), and in various cases, it outperforms the others ( FaultTree + and Commander ). Besides, SATMCS exhibits much better performance on memory usage than these tools. Specifically, SATMCS consumes about one order of magnitude less memory usage in most instances.

中文翻译：

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卫星通信系统：一种高效的基于SAT的算法及其计算最小割集的改进

故障树分析（FTA）是一种突出的可靠性分析方法，广泛应用于安全关键行业。计算故障树的最小割集 (MCS)，即找到导致系统故障的基本事件的所有最小组合，是 FTA 的基本步骤。由于相干故障树在实践中是工业系统中最常见的，因此它们是本文的重点。计算 MCS 是一个计算困难的问题。已经提出了基于布尔表达式和二元决策图的操作的经典方法。然而，考虑到在实践中计算 MCS 固有的难处理性，这些方法在时间和内存上仍然存在限制。因此，开发不同范式的新方法仍然是一个有趣的研究方向。在本文中，卫星通信系统 . 具体来说，给定故障树，我们基于冲突驱动的子句学习框架迭代搜索割集。通过利用局部传播图来表征基于割集的部分故障传播，我们提供了用于提取 MCS 的有效算法。新的MCS作为SAT求解的块子句学习，迭代中的冲突子句被增量记录，这有助于修剪搜索空间并确保结果的完整性。此外，我们采用跳跃时间顺序回溯策略来准备下一次迭代，这允许在 SAT 求解中重用相同的搜索步骤。我们比较卫星通信系统使用有关实际故障树的最先进的商业工具。虽然卫星通信系统 只是一个原型，它在时间消耗方面表现出与一个工具相当的性能（ XFTA )，并且在各种情况下，它的表现优于其他 ( 故障树 + 和 指挥官 ）。除了，卫星通信系统与这些工具相比，在内存使用方面表现出更好的性能。具体来说，卫星通信系统 在大多数情况下，消耗大约一个数量级的内存使用量。