Motor fault diagnosis based on teaching and learning gray wolf algorithm optimized support vector machine

Abstract

To quickly and accurately identify motor faults, this paper proposes the gray wolf algorithm based on teaching and learning improvement to adaptively optimize the parameters of the multi-class support vector machine. Finally, based on the improved algorithm, the seven types of fault data such as the air gap eccentricity of the motor, the broken bar of the rotor, the bearing seat damage, and the bearing wear were carried out fault diagnosis experiments, and a comprehensive comparison and analysis were carried out with the widely used algorithm. The results show that the motor fault diagnosis accuracy rate of this method is 97.88%, which is better than other methods in classification accuracy.