A new modeling and inference approach for the belief rule base with attribute reliability

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.