Abstract
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
Highlights
With the rapid development of science and technology, application of high dependability safeguard techniques have improved the performance of modern systems greatly on the one hand, but increased the complexity of these systems on the other hand, which significantly raises some challenges in fault diagnosis
High reliability makes it extremely difficult to obtain complete fault data because these systems may still be in the early life cycle, which results in the epistemic uncertainty
Duan et al proposed a new fault diagnosis for complex systems based on dynamic evidential network and multi-attribute decision making [11]
Summary
With the rapid development of science and technology, application of high dependability safeguard techniques have improved the performance of modern systems greatly on the one hand, but increased the complexity of these systems on the other hand, which significantly raises some challenges in fault diagnosis. Duan et al presented an efficient diagnostic algorithm which used DFT to establish a system failure model and calculated reliability parameters using a discrete time Bayesian network (DTBN) [8] This approach can avoid the state space explosion, and can incorporate sensor information to update reliability results. It is usually difficult to determine the corresponding membership function of each language value To this end, Duan et al proposed a new fault diagnosis for complex systems based on dynamic evidential network and multi-attribute decision making [11]. (1) Traditional fault diagnosis methods based on reliability analysis generally use a static fault tree or DFT to construct fault model and assume that the failure rates of all events are crisp values, which cannot deal with epistemic uncertainty. Some conclusions and future research recommendations are given in the final section
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