A feature vector with insensitivity to the position of the outer race defect and its application in rolling bearing fault diagnosis

The fault diagnosis of rolling bearings is very important in industrial applications, which can avoid accidents and reduce operation and maintenance costs. Although the position of the bearing outer race defect has a significant impact on rolling bearing vibration response, most existing intelligent bearing fault diagnosis methods do not take this into account. In this paper, we establish a dynamic model of rolling bearing to clarify the influence of the outer race defect position on the dynamic response, and propose a feature vector that is insensitive to the outer race defect positions. First, the vibration characteristics of the outer race faults with different defect positions are analyzed, and the impact is evaluated using six indicators. Second, three indicators of insensitivity to the bearing outer race defect positions are constructed as the feature vector for bearing fault diagnosis. Finally, a bearing fault diagnosis method considering the positions of outer race defect is proposed based on the constructed feature vector and K nearest neighbor classifier. The diagnosis results of three datasets formed by experimental signals show that the constructed feature vector can separate different bearing states. Compared with the existing two diagnosis methods, the proposed diagnosis method obtains higher recognition accuracy, in the case of different outer race defect positions of the training set and the testing set. The above research results are expected to provide a reference for rolling bearing fault diagnosis, especially when considering the influence of the outer race defect positions.