A fully Bayesian approach for combining multilevel failure information in fault tree quantification and optimal follow-on resource allocation
A fully Bayesian approach for combining multilevel failure information in fault tree quantification and optimal follow-on resource allocation
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DOI：
10.1016/j.ress.2004.02.001
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发表时间：
2004-12
期刊：
影响因子：
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通讯作者：
M. Hamada;H. Martz;C. S. Reese;T. Graves;Val Johnson;Alyson G. Wilson
中科院分区：
文献类型：
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作者：
M. Hamada;H. Martz;C. S. Reese;T. Graves;Val Johnson;Alyson G. Wilson
本文提出了一种完全贝叶斯方法，同时结合不重叠（在时间上）的基本事件和更高级别的事件故障数据的故障树量化。这种更高级别的数据通常对应于列车、子系统或系统故障事件。完全贝叶斯方法还自动将最高级别的数据传播到故障树中的较低级别。一个简单的例子说明了我们的方法。还提出了从不同级别事件的选择中收集额外数据的资源的最佳分配。使用遗传算法实现优化。
This paper presents a fully Bayesian approach that simultaneously combines non-overlapping (in time) basic event and higher-level event failure data in fault tree quantification. Such higher-level data often correspond to train, subsystem or system failure events. The fully Bayesian approach also automatically propagates the highest-level data to lower levels in the fault tree. A simple example illustrates our approach. The optimal allocation of resources for collecting additional data from a choice of different level events is also presented. The optimization is achieved using a genetic algorithm.