Inter-turn Short Circuit Fault Diagnosis of Induction Motors Using the Svm Optimized by Bare-bones Particle Swarm Optimization

In order to accurately recognize the stator winding inter-turn short circuit fault of induction motors, a novel method for fault diagnosis was proposed based on a bare-bones particle swarm optimization algorithm (BBPSO) and support vector machine (SVM); and feasible diagnostic steps were also introduced. In this method, the feature vector of induction motor in different conditions was extracted with wavelet packet, and was considered as the input vector of SVM. The SVM was used to solve the classification problem, and the parameter-free BBPSO and cross-validation were taken to optimize model parameters, which avoided the blindness of parameter selection. Finally, the experiment shows that the proposed method is effective to diagnose the stator winding inter-turn short circuit fault of induction motors.