考虑共因失效的新型连续时间动态贝叶斯网络可靠性分析方法

现代系统失效行为复杂，动态性与相关性并存。首先为直观准确地刻画分析系统中的动态失效行为，提出新型连续时间动态贝叶斯网络分析方法，利用节点时序条件概率表刻画事件关系，进而提出基于节点时序条件概率表规则执行度与冲激函数抽样性质的子节点故障概率、根节点后验概率及重要度的计算方法；进一步，针对共因失效引起的系统相关性失效行为，提出考虑共因失效的新型连续时间动态贝叶斯网络分析方法，解决系统失效逻辑动态性和相关性的重叠问题。通过与贝叶斯网络、离散时间动态贝叶斯网络分析方法、Markov链、Monte Carlo法对比，验证所提方法的可行性与优越性。最后，对动态失效相关系统进行可靠性分析，结果表明，本文方法能够直观有效地刻画动态性与相关性失效行为，得到准确的系统可靠性指标，考虑共因失效相比于忽略共因失效，在任务时间为5×10~6 h时能够提高系统29%的可靠性分析精度，更加符合实际。

A novel continuous-time dynamic Bayesian network reliability analysismethod considering common cause failure

The failure behaviour of modern systems is complex, with both dynamics and correlation. First, in order to describe thedynamic failure behaviour intuitively and accurately, a novel continuous-time dynamic Bayesian network analysis method is proposed,which uses node sequence conditional probability table(CPT) to describe the event relationship. Then, the calculation method of childnode failure probability, posteriori probability and importance measures of root node based on the rule execution degree of node sequenceCPT and the sampling property of impulse function is proposed. Further, aiming at the system correlation failure behaviour caused bycommon cause failure(CCF), a novel continuous-time dynamic Bayesian network analysis method considering CCF is proposed to solvethe overlapping problem of system failure logic dynamics and correlation. Compared with the Bayesian network, discrete-time dynamicBayesian network analysis method, Markov chain and Monte Carlo method, the feasibility and superiority of the proposed method areverified. Finally, the reliability of dynamic failure related systems is evaluated, the results show that the proposed method can directlyand effectively describe the dynamic and correlation failure behavior, obtain the accurate system reliability index, compared with ignoringCCF, considering CCF can improve the reliability analysis accuracy of the system by 29% when the task time is 5×106h, which is morepractical.