A new modeling and inference approach for the belief rule base with attribute reliability
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A belief rule-based (BRB) model with attribute reliability (BRB-r) has been developed recently, where the systematic uncertainty is regarded as attribute reliability by extending the traditional BRB model. The BRB-r model provides a framework to deal with the systematic uncertainty, but the drawbacks in modeling and inference reduces the accuracy of it. This paper proposed a new modeling and inference approach to improve the effectiveness of the BRB-r. This approach is constituted by two parts: data processing and BRB inference. In the data processing, the attribute reliability is calculated based on the auto regressive model, while the parameters of BRB-r are optimized using the differential evolution algorithm. In the BRB inference, a new attribute reliability fusion algorithm is proposed, which can effectively integrate attribute reliability into the BRB model and ensure the rationality in different situations. A benchmark case about pipeline leak detection and a practical case about condition monitoring are studied to demonstrate the rationality and feasibility of the proposed approach to the BRB-r model.
KeywordsBelief rule-based model with attribute reliability (BRB-r) Attribute reliability Systematic uncertainty Auto regressive (AR) model
This work was supported in part by the National Key R&D Program of China under Grant No. SQ2017YFSF070185 and the National Natural Science Foundation of China under Grant 71901212 and Grant 71690233 and Grant 71671186, and in part by the Research Project of National University of Defense Technology.
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