This paper presents an information fusion method to diagnose system fault based on dynamic fault tree (DFT) analysis and
dynamic evidential network (DEN). In the proposed method, firstly, it uses a DFT to describe the dynamic fault characteristics
and evaluates the failure rate of components using interval numbers to deal with the epistemic uncertainty. Secondly, qualitative
analysis of a DFT is to generate the characteristic function via a traditional zero-suppressed binary decision diagram, while
quantitative analysis is to calculate some importance measures by mapping a DFT into a DEN. Thirdly, these reliability results are
updated according to sensors data and used to design a novel diagnostic algorithm to optimize system diagnosis. Furthermore, a
diagnostic decision tree (DDT) is obtained to guide the maintenance workers to recover the system. Finally, the performance of the
proposed method is evaluated by applying it to a train-ground wireless communication system. The results of simulation analysis
show the feasibility and effectiveness of this methodology
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