RESEARCH PAPER
Fault diagnosis for complex systems based on reliability analysis and sensors data considering epistemic uncertainty
1
School of Information Engineering Nanchang University, 999 Xuefu Rd., Honggutan new district Nanchang, Jiangxi, China
Publication date: 2018-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2018;20(4):558-566
KEYWORDS
dynamic fault treedynamic evidential networkinterval numberssensors datadiagnostic importance factor
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
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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