Rolling bearing fault diagnosis based on multi-source data fusion

Abstract In this paper, a fault diagnosis method is proposed based on multi-source data fusion to address the inaccurate results caused by single evidence used for decision-making. First, empirical mode decomposition is performed using sensor data to extract fault features, and a fault feature matrix and a diagnostic matrix are established. Then the deviation vector is defined to obtain the difference between the diagnostic sample and the fault sample, and then the basic probability assignment is obtained for each diagnostic sample. Finally, the Dempster combination rule is used for fusion. The effectiveness and accuracy of this method in fault diagnosis of rolling bearings have been verified through examples of rolling bearings.