Running State Monitoring of Induction Motors Based on KPCA_RBF and Stator Current Characteristics

Abstract

Induction motors are important equipment in industrial production processes. To solve the problem of characteristic harmonics overlapping when there is deterioration in different parts of the induction motor and diagnose the deterioration degree of internal components such as bearings, stator winding insulation, and air gap balance, a diagnostic method based on kernel principal component analysis (KPCA) and radial basis function neural network (RBF) is proposed. Firstly, through the experimental analysis, it is found that the 2nd, 3rd, 4th, and 5th order characteristic frequency harmonics in the current can reflect the deterioration of the motor. Secondly, KPCA is used to determine the correlation degrees between characteristic frequency harmonic contents and the deterioration of the corresponding parts of the motor. Finally, the products of characteristic frequency harmonic contents and corresponding correlation degrees are taken as the input vectors of radial basis neural network, and the deterioration degrees are taken as the output vectors to diagnose the deterioration of the motor. Through the diagnostic analysis of the experimental unit and the comparison of the actual deterioration degree of the motor after disassembly, it is proved that the proposed method can accurately diagnose the deterioration of the motor. By the proposed method, the correlation between motor deterioration and characteristic frequency harmonics has been identified, and the degree of deterioration of each part of the motor has been quantified. It can monitor the degree of motor deterioration in real-time, grasp the trend of motor deterioration, and detect early signs of motor deterioration.

Keywords

• Induction motor
• diagnosis
• harmonic
• kernel principal component
• radial basis function neural network