Expert Opinion Fusion Framework Using Subjective Logic for Fault Diagnosis

• Published in: IEEE transactions on cybernetics Vol. 52; no. 6; pp. 4300 - 4311
• Main Authors: Xu, Peng, Cho, Jin-Hee, Salado, Alejandro
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes methods; Bayesian analysis; Expert clustering; Fault diagnosis; Knowledge engineering; Object oriented modeling; opinion fusion; subjective Bayesian network (SBN); subjective logic (SL); Troubleshooting; Uncertainty; Wind turbines
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2020.3025800

Fault diagnosis plays a critical role in maintaining and troubleshooting engineered systems. Various diagnosis models, such as Bayesian networks (BNs), have been proposed to deal with this kind of problem in the past. However, the diagnosis results may not be reliable if second-order uncertainty is involved. This article proposes a hierarchical system diagnosis fusion framework that considers the uncertainty based on a belief model, called subjective logic (SL), which explicitly deals with uncertainty representing a lack of evidence. The proposed system diagnosis fusion framework consists of three steps: 1) individual subjective BNs (SBNs) are designed to represent the knowledge architectures of individual experts; 2) experts are clustered as expert groups according to their similarity; and 3) after inferring expert opinions from respective SBNs, the one opinion fusion method was used to combine all opinions to reach a consensus based on the aggregated opinion for system diagnosis. Via extensive simulation experiments, we show that the proposed fusion framework, consisting of two operators, outperforms the state-of-the-art fusion operator counterparts and has stable performance under various scenarios. Our proposed fusion framework is promising for advancing state-of-the-art fault diagnosis of complex engineered systems.