Treatment of general dependencies in system fault-tree and risk analysis
Treatment of general dependencies in system fault-tree and risk analysis
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DOI：
10.1109/tr.2002.801848
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发表时间：
2002-11
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影响因子：
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通讯作者：
J. Vaurio
中科院分区：
文献类型：
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作者：
J. Vaurio
隐式和显式方法描述了系统的可靠性和风险分析的相关或相关的基本事件。一般规则的建模任何一组的n个相互s依赖的事件与2/supn/-1 s-独立的事件。这些虚拟事件的概率基于原始s相关事件的联合概率来确定，通常通过s相关或条件概率已知。变换保持所有项的值（例如，最小割集），独立于系统成功标准。这有利于一般使用的普通故障树的计算机代码，假设基本事件是s-独立的。当n个冗余部件（1 /spl les/ n /spl les/ 4）之间的测试事件的调度和同步以及故障率的统计变化引起s-依赖时，得到了计算备用安全系统按需故障概率的显式基本事件概率。在这个练习中遇到了有趣的“负概率”，主要是由于交错测试的组件不可用性之间的负s相关性。当重复错误的条件概率大于单个孤立任务中错误的概率时，人为错误事件的结果是有用的。得到了明确的结果与时间相关的一般多故障率建模的共因故障的系统。包括测试间隔和测试交错的影响。交错测试是最佳的ETR（额外测试规则），虽然ETR是不重要的1-out-of-n：G系统。一个经济模型提供了洞察各种参数的影响：最佳的测试间隔随着冗余和测试成本的增加而增加，它随着事故成本和启动事件率的增加而减少。使用ETR的交错测试允许最长的最佳测试间隔。规则改变的s-依赖概率时，一些组件是已知的失败。目前的故障树量化工具不适合使用隐式方法，尽管它会简化故障树的构造，减少割集的数量，并允许不同类型的依赖关系或相关性的分析。建议计算隐式方法或将其作为当前代码的选项。它只需要一个联合概率的数据表，以及在一个项（或一个截集）中出现两个或更多个s相关事件时从该表中提取数据的能力。
Implicit and explicit methods are described for reliability and risk analysis of systems with dependent or correlated basic events. General rules are presented for modeling any group of n mutually s-dependent events with 2/sup n/-1 s-independent events. The probabilities of these virtual events are determined based on the joint probabilities of the original s-dependent events, typically known by s-correlation or conditional probabilities. The transformations preserve the values of all terms (e.g., minimal cut sets), independent of system success criteria. This facilitates general use of ordinary fault-tree computer codes that assume basic events to be s-independent. Explicit basic event probabilities are obtained for calculating the probability of failure on demand of standby safety systems when the s-dependency is caused by scheduling and synchronization of test episodes between n redundant components (1 /spl les/ n /spl les/ 4), and by statistical variation of failure rates. Interesting "negative probabilities" are encountered in this exercise, mainly due to negative s-correlation between the component unavailabilities with staggered testing. Results obtained for human-error events are useful when the conditional probability to repeat an error is larger than the probability of an error in a single isolated task. Explicit results are obtained for systems with time-related common-cause failures modeled by general multiple failure rates. The impacts of test intervals and test staggering are included. Staggered testing is optimal with an ETR (extra-testing rule), although ETR is not important for 1-out-of-n:G systems. An economic model provides insights into the impacts of various parameters: the optimal test interval increases with increasing redundancy and testing cost, and it decreases with increasing accident cost and initiating event rate. Staggered testing with ETR allows for the longest optimal test intervals. Rules are presented for changing s-dependency probabilities when some component is known to be failed. Current fault-tree quantification tools are not well geared to use the implicit method in spite of the fact that it would simplify the fault-tree construction, reduce the number of cut sets, and allow different types of dependencies or correlations in the analysis. A recommendation is to computerize the implicit method or include it as an option to current codes. It would need only a data table for joint probabilities and the ability to pick-up data from this table whenever two or more of the s-dependent events appear in a term (or a cut set).