A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks

This paper presents a novel method for diagnosing faults using fault tree analysis and Bayesian networks (BN) to optimize system diagnosis. All minimal cut sets were generated via qualitative analysis of fault tree using an efficient zero-suppressed binary decision diagram (ZBDD), while the diagnostic importance factor (DIF) of components and minimal cut sets were calculated by mapping fault tree into equivalent BN. Also, these analysis results such as minimal cut sets and DIF were updated after receiving the evidence data from sensors and used to develop an efficient diagnostic decision algorithm. Furthermore, a diagnostic decision tree (DDT) was generated to guide the maintenance personnel to repair the system. Finally, a real example is given to demonstrate the efficiency of this method.