Macroeconomics

Quantum mechanics

Magnetic field

Control (management)

Engineering

Magnetization

Magnetic flux leakage

Leakage (economics)

Synchronous motor

Economics

Computer science

Digital signal processing

Fault (geology)

Seismology

Demagnetizing field

Geology

Signal processing

Physics

Pattern recognition (psychology)

Electronic engineering

Artificial intelligence

Control theory (sociology)

Magnet

Wavelet

Electrical engineering

IEEE Transactions on Industrial Informatics

In most industrial applications, it is difficult to obtain complete demagnetization fault signals of all conditions with labels for permanent magnet synchronous motor (PMSM), and motors are not allowed to be disassembled, so non-contact diagnostic methods are essential. A non-contact fault diagnosis method using magnetic leakage signal based on wavelet scattering convolution network (WSCN) and semi-supervised deep rule-based (SSDRB) classifier is proposed. Through magnetic equivalent circuit model analysis, the magnetic leakage signal on motor surface is selected as fault signal. To avoid complex signal processing, the symmetrized dot pattern method is introduced to convert fault signals into two-dimensional images. Then, WSCN is applied to extract features from images, and SSDRB classifier is adopted to diagnose demagnetization fault. Finally, faulty motor prototypes are manufactured for experiment. By comparing with other methods, the superiority and effectiveness of the proposed method using a small number of labeled samples under different conditions are verified.

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Using Magnetic Leakage Signals