A Data-Driven Smart Fault Diagnosis Method for Electric Motor

The electric motor is the elementary device of modern industry system, and its timely fault diagnosis leads to reduce the maintenance costs and downtime, and improve system reliability. This paper deals with the problem of fault diagnosis of electric motor based on power signal, and a data-driven fault diagnosis method based on genetic algorithm (GA) optimized support vector machine (SVM) is presented. The feature presentation, feature selection and feature extraction are applied as data preprocessing methods to reduce data dimensions, that is, we implement feature representation by the time domain analysis method and the range analysis method, and the fisher discriminant analysis is used for feature selection, and the locally linear embedding (LLE) is used for feature extraction. Then the GA is used to optimize the SVM classifier for fault classification after data preprocessing. Our method can obtain good fault classification effect, and the experimental results show that the classification accuracy of the proposed method is better than that of probabilistic neural network, and the feasibility and effectiveness of this proposed method in fault diagnosis of electric motor are proved.