Abstract: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 zerosuppressed 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.
