采用动态故障树分析诊断系统故障的信息融合法

Information Fusion Method for System Fault Diagnosis Based on Dynamic Fault Tree Analysis

为提高系统故障诊断效率,提出了一种利用动态故障树分析诊断系统故障的信息融合方法,该方法充分发挥动态故障树建模和贝叶斯网络推理各自优势,通过集成系统结构信息和传感器信息来诊断系统故障.采用高效的零压缩二元决策图生成系统所有最小割集,并采用贝叶斯网络方法计算部件和最小割集的诊断重要度;根据传感器证据信息对系统特征函数化简,同时对部件和证据条件下割集的诊断重要度进行更新;综合考虑部件和割集诊断重要度设计了系统诊断决策算法,生成诊断决策树以指导维修人员恢复系统故障;最后通过实例验证了该故障诊断方法的有效性.

An information fusion method was proposed to diagnose system faults with dynamic fault tree（DFT） analysis to improve the efficiency of system diagnosis,which made full use of the advantages of both DFT for modeling and Bayesian networks（BN） for the inference ability and incorporated system structure information as well as sensors data into fault diagnosis.All minimal cut sets were generated via an efficient zero-suppressed binary decision diagram,while the diagnostic importance factor of components and minimal cut sets were calculated using BN.Furthermore,these reliability analysis results together with the characteristic function of the system were updated after receiving the evidence data from sensors and used to develop diagnostic decision algorithm to optimize system diagnosis.Then,a diagnostic decision tree was generated to guide the maintenance crew to recover a system.Finally,an example was given to illustrate the efficiency of this method.