基于Vesely故障率的大型故障树可靠性分析

S. Amari, J. Akers
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引用次数: 16

摘要

故障树提供了一种简洁、图形化、直观的系统可靠性分析方法。然而,组合故障树分析方法,如二元决策图,不能用于发现具有可修部件的系统的可靠性。在这种情况下,分析人员应该显式地使用马尔可夫模型,或者使用自动转换算法从故障树生成马尔可夫模型。这个过程很繁琐,即使对于中等大小的故障树也会产生巨大的马尔可夫模型。本文演示了利用Vesely故障率作为系统实际故障率的近似值来寻找大型故障树的基于可靠性的度量。该方法的主要优点是使用组合方法(如二元决策图)计算可修系统的可靠性。通过与其他几种近似的比较,证明了该近似的有效性,并提供了系统可靠性的各种界限。该方法在寻找其他可靠性度量(如MTBF、MTTR、MTTF和MTTFF)方面的有效性得到了证明。最后,将该方法扩展到包含静态和动态模块的复杂故障树分析,以及由其他建模工具表示的事件分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliability analysis of large fault trees using the Vesely failure rate
Fault trees provide a compact, graphical, intuitive method to analyze system reliability. However, combinatorial fault tree analysis methods, such as binary decision diagrams, cannot be used to find the reliability of systems with repairable components. In such cases, the analyst should use either Markov models explicitly or generate Markov models from fault trees using automatic conversion algorithms. This process is tedious and generates huge Markov models even for moderately sized fault trees. In this paper, the use of the Vesely failure rate as an approximation to the actual failure rate of the system to find the reliability-based measures of large fault trees is demonstrated. The main advantage of this method is that it calculates the reliability of a repairable system using combinatorial methods such as binary decision diagrams. The efficiency of this approximation is demonstrated by comparing it with several other approximations and provide various bounds for system reliability. The usefulness of this method in finding the other reliability measures such as MTBF, MTTR, MTTF, and MTTFF is shown. Finally, extending this method to analyze complex fault trees containing static and dynamic modules as well as events represented by other modeling tools.
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