基于贝叶斯网络的复杂系统级联故障评估

Nuo Jia, Hongzhang Jin, Yanli Zhang, A. Zou
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引用次数: 1

摘要

为了提高复杂系统的可靠性,揭示系统、子系统和部件之间的级联失效纵向关系,提出了一种基于贝叶斯网络的级联失效评估方法。利用由故障树(FT)变换而来的BN的条件概率给出了级联故障的概率指标。之后,本文采用结点树推理算法进行双向推理,定量评估子系统或部件故障对系统故障的影响,以及系统故障情况下部件故障的可能性。最后,将该方法应用于船舶湿式喷水灭火系统双总线自动报警子系统的级联故障评估,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cascading failure assessment of complex systems based on Bayesian networks
A cascading failure assessment method based on Bayesian network (BN) is proposed in order to improve the reliability of complex system and show cascading failure longitudinal relationship among system, subsystems and components. The probability index of cascading failure is given by using conditional probability of BN which is transformed from fault tree (FT). After that, junction tree inference algorithm is adopted here to carry out bidirectional reasoning to exhibit quantitative assessment of influence on system failure due to subsystem or component failure and possibility of component failure under the condition of system failure. Finally, the method is applied to cascading failure assessment of 2-bus automatic alarm subsystem in ship wet sprinkler system to demonstrate its effectiveness.
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