Novel rolling bearing state classification method based on probabilistic Jensen–Shannon divergence and decision fusion

Abstract To improve the accuracy of bearing state classification in multi-source monitoring environments, a novel decision fusion method based on probabilistic Jensen–Shannon (PJS) divergence and D-S evidence theory is proposed. Firstly, the initial state probability of each bearing is determined by the bearing state classification score value, and the basic probability assignment (BPA) value of each bearing state is set by obtaining the global credibility of each sensor based on the pre-network test results. Secondly, the PJS divergence is proposed on the basis of the Kullback Leibler (KL) divergence to measure the conflict between the data collected by each sensor, and the weight value of the BPA value of each sensor is reset according to the PJS divergence value, which improves the accuracy of the BPA value. Finally, the D-S evidence theory is used to fuse the BPA value of each sensor and obtain the final bearing state classification result. The experimental part shows the effectiveness using the bearing signals collected in the laboratory, and the generality of this paper’s method is verified by using it in conjunction with other networks.