Demagnetization Fault Diagnosis Method for a Permanent Magnet Synchronous Motor Based on Limited Samples

Aiming at the demagnetization identification problem of permanent magnet synchronous motor due to the sparse sample data, low availability, weak feature and complex structure, this paper proposes a demagnetization fault diagnosis method combining sparse self-encoding and least squares generative countermeasure network. This method first collects the electromagnetic torque and magnetomotive force distribution data of the permanent magnet synchronous motor to form a limited sample set. Secondly, the least squares generative confrontation network is used to label and expand the sample while maintaining the same feature distribution, and finally use sparse The self-encoding network and Soft max classifier train and classify the samples to realize the diagnosis and identification of demagnetization faults. In the process of model training and fault identification, on the one hand, the parameters that affect learning efficiency such as the hidden nodes of the deep network, the training algorithm and the number of layers are rationally designed; on the other hand, the optimized network is trained and tested and verified to improve the fault diagnosis performance. After many tests, the effective diagnosis of permanent magnet synchronous motor demagnetization fault was finally realized