PMSM Stator Winding Fault Detection and Classification Based on Bispectrum Analysis and Convolutional Neural Network

The diagnosis of permanent magnet synchronous motor (PMSM) faults has been the subject of much research in recent years, due to the growing reliability and safety requirements for drive systems. This article concerns PMSM stator winding fault detection and classification. A novel intelligent diagnosis approach is proposed, based on the bispectrum analysis of a stator phase current and the convolutional neural network (CNN). Rather than using raw phase current signals, bispectrum is applied for symptom extraction and utilized as the input for a pretrained CNN model. The CNN model is used for automatic inference on the winding condition of the PMSM stator. Experimental results are presented to validate the proposed algorithm. The classification effectiveness of the developed CNN is as high as 99.4%. This article also presents the possibility of improving the accuracy of the CNN model and reducing the training time by properly tuning the training parameters. The CNN model learning time is only about one minute. The fault classifier model is developed in Python programming language, avoiding the cost of purchasing additional software.

Automated summary

This article proposes a novel approach for detecting and classifying faults in PMSM stator windings, using bispectrum analysis and convolutional neural network (CNN). Experimental results show that the proposed method achieves a classification effectiveness of 99.4%. Furthermore, the accuracy of the CNN model can be improved and the training time can be reduced by properly tuning the training parameters.