Journal
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
Volume 9, Issue 3, Pages 396-415Publisher
ATLANTIS PRESS
DOI: 10.1080/18756891.2016.1175808
Keywords
Fault diagnosis; interval-valued belief structures; Dempster-Shafer evidence theory; evidence updating; alarm monitoring
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Funding
- NSFC [61374123, 61433001, 61573076, 61573275]
- Zhejiang Open Foundation of the Most Important Subjects
- Zhejiang Province Research Program Project of Commonweal Technology Application [2016C31071]
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Information fusion methods based on Dempster-Shafer evidence theory (DST) have been widely used in fault diagnosis. In DST-based methods, the monitoring information collected from sensors is modeled as multiple pieces of diagnosis evidence in the form of basic belief assignment (BBA), and Dempster's rule is then used to combine these BBAs to obtain the fused BBA for diagnosis decision making. However, the belief structure with crisp singlevalued belief degrees in BBA may be too coarse to truthfully represent detailed fault information. Moreover, Dempster's rule only uses a static combination process, which is unsuitable for dynamically fusing information collected at different time steps. In order to address these issues, the paper proposes a dynamic diagnosis method based on interval-valued evidential updating. First of all, the diagnosis evidence is constructed as an interval-valued belief structure (IBS), which provides a more informative scheme than BBA to model fault information. Secondly, the proposed evidential updating strategy can generate updated IBS as global diagnosis evidence by updating the previous evidence with the new incoming evidence recursively. Thirdly, the reliability and sensitivity indices are designed to evaluate and compare the performance of the proposed updating strategy with other commonly used strategies. Finally, the effectiveness of the proposed evidential updating strategy is demonstrated through some typical fault experiments of a machine rotor.
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