Rotor Faults Diagnosis in PMSMs Based on Branch Current Analysis and Machine Learning

• Published in: Actuators 2023-04, Vol.12 (4), p.145
• Main Authors: Yu, Yinquan, Gao, Haixi, Zhou, Shaowei, Pan, Yue, Zhang, Kunpeng, Liu, Peng, Yang, Hui, Zhao, Zhao, Madyira, Daniel Makundwaneyi
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial intelligence; branch current analysis; Classification; Data mining; Deep learning; Discriminant analysis; Electric properties; Fault diagnosis; Faults; GA-SVM; Genetic algorithms; Identification methods; Machine learning; Magnets, Permanent; Methods; Neural networks; Optimization; permanent magnet synchronous motor (PMSM); Permanent magnets; Rotors; support vector machine (SVM); Support vector machines; Synchronous motors; Time domain analysis; Wavelet transforms
• ISSN: 2076-0825; 2076-0825
• DOI: 10.3390/act12040145

To solve the problem that it is difficult to accurately identify the rotor eccentric fault, demagnetization fault and hybrid fault of a permanent magnet synchronous motor (PMSM) with a slot pole ratio of 3/2 and several times of it, this paper proposes a method to identify the rotor fault based on the combination of branch current analysis and a machine learning algorithm. First, the analysis of the electrical signal of the PMSM after various types of rotor faults shows that there are differences in the time domain of the electrical signal of the PMSM after three types of rotor faults. Considering the symmetry of the structure of the PMSM with a slot-pole ratio of 3/2 and its integer multiples, the changes in the time domain of the phase currents cancel each other after the fault, and the time domain fluctuations of the stator branch currents that do not cancel each other are chosen as the characteristics of the fault classification in this paper. Secondly, after signal preprocessing, feature factors are extracted and several fault feature factors with large differences are selected to construct feature vectors. Finally, a genetic algorithm is used to optimize the parameters of a support vector machine (SVM), and the GA-SVM model is constructed as a classifier for multifault classification of permanent magnet synchronous motors to classify these three types of faults. The fault classification results show that the proposed method using branch current signals combined with GA-SVM can effectively diagnose faulty PMSM.