Research on Demagnetization Fault Diagnosis Method of Mine Cutting Permanent Magnet Synchronous Motor

• Published in: International journal of rotating machinery 2024, Vol.2024, p.1-16
• Main Authors: Ye, Guo, Lu, Yanbin, Ju, Jinyong, Sheng, Lianchao
• Format: Article
• Language: English
• Subjects: Classification; Demagnetization; Fault diagnosis; Faults; Finite element analysis; Finite element method; Fourier transforms; Kalman filters; Magnetic fields; Neural networks; Permanent magnets; Phase current; Principal components analysis; Reduction; Simulation; Simulation models; Software; Spectrum analysis; Support vector machines; Synchronous motors; Wavelet analysis
• ISSN: 1023-621X; 1542-3034
• DOI: 10.1155/2024/6648925

To give timely and accurate diagnosis in the early stage of demagnetization failure for effective control and treatment, based on wavelet packet analysis, principal component analysis (PCA) dimensionality reduction, and least squares support vector machine(LSSVM), the extraction of features and the classification of demagnetization faults are completed. Since it is difficult to collect real data sets of demagnetization faults in practice, a two-dimensional finite element simulation model of permanent magnet synchronous motor (PMSM) under uniform demagnetization and partial demagnetization faults is established based on the Maxwell simulation platform. The wavelet packet analysis is used to extract the demagnetization feature of the A-phase current of the PMSM. Based on PCA dimensionality reduction, the dimensionality reduction of fault features is realized. The LSSVM is used to identify the fault and complete the fault classification. The simulation results show that the method has a high classification accuracy rate for demagnetization faults.