A fault diagnosis method of turn-to-turn short circuit of Permanent Magnet Synchronous Motor (PMSM) based on deep learning

The fault diagnosis of turn-to-turn short circuit of permanent magnet synchronous motor (PMSM) often results in low diagnostic accuracy due to insufficient sample size. A fault diagnosis method based on deep learning PMSM inter-turn short circuit is proposed. According to the shortcomings of commonly used Generative Adversarial Networks (GAN), fault samples are expanded by using an optimized GAN to build a robust training set. Using stack sparse autoencoder (SSAE) combined with classifier to construct SSAE neural network can effectively solve the shortcomings of SAE’s limited learning ability and poor feature learning effect. In this paper, the PMSM three-phase stator current and zero-sequence voltage signals are used as the feature combination for synthetic fault diagnosis. We have conducted extensive experiments on the basis of the above method. The results show that the diagnostic accuracy of this method is as high as 99.4%. Outperforms traditional PMSM turn-to-turn short-circuit fault diagnosis methods.