Research on fault diagnosis of electric motor rolling bearings based on CMFSE-SVM

Source abstract

Rolling bearings are key components of rotating machinery such as electric motors, and their health status directly affects the reliability and safety of the equipment. In order to improve the fault classification accuracy of electric motor rolling bearing, this paper proposes a diagnostic method based on CMFSE-SVM. Firstly, the composite multi-scale fuzzy slope entropy (CMFSE) method proposed in this paper is used to extract the characteristics of the vibration signal of the motor rolling bearings. Finally, the obtained feature vectors are sent to the support vector machine (SVM) for fault classification. This paper verifies the classification accuracy of the method proposed in this paper on two publicly available datasets of electric motor rolling bearing faults. The experimental results show that the method proposed in this paper achieves average classification accuracies of 100 % and 99.6 % respectively on all working conditions corresponding to these two datasets. And the classification accuracies were 2.4 % and 2.8 % higher respectively than those of the compared methods.