基于动态贝叶斯网络的电源系统可靠性分析与故障诊断

动态系统的可靠性分析与故障诊断一直是可靠性领域的热点及难点问题，作为该领域热门的分析工具之一，动态贝叶斯网络(DBN)得到了充分的应用与开发。但是，现有的DBN算法受限于系统的失效分布类型，且建模难度也随着系统复杂度的增加而呈指数增长。针对以上问题，该文提出一种改进的动态贝叶斯网络概率表建模方法，在连续任务时间的条件下，实现动态系统的可靠性分析。然后，结合DBN双向推理算法，求解系统失效时部件失效的后验概率，并将计算结果应用于系统故障诊断及薄弱部件定位。最后，结合某电源系统的可靠性分析与故障诊断，验证了该方法的实用性。

Reliability Analysis and Fault Diagnosis for Power System via Dynamic Bayesian Network

Reliability analysis and fault diagnosis for dynamic systems have always been hot topics in this field. As one of the popular reliability analysis methods, dynamic bayesian network (DBN) has been fully studied. However, the existing DBN algorithm has no general inference engines, and the modeling difficulty increases exponentially with the system complexity. This paper proposes a general probability table modeling method, which can also be applied on the dynamic reliability analysis of the system under the continuous mission time. Additionally, via the Bayesian inference algorithm, the posterior probability of component failure can be obtained, which can also be applied on system fault diagnosis. Finally, the validation of proposed method is verified by the reliability analysis and fault diagnosis of the power system.