A Fault Diagnosis Method Based on an Improved Deep Q-Network for the Interturn Short Circuits of a Permanent Magnet Synchronous Motor

With the rapid development of artificial intelligence, deep learning has become a widely used method to monitor the state of permanent magnet synchronous motors (PMSMs). However, to achieve accurate condition detection, datasets with large quantities of labeled data must be used for model training. Accuracy decreases considerably when the quantity of labeled data is insufficient. To address this problem, a fault diagnosis method based on an improved deep Q-network (DQN) is designed in this study to detect interturn short circuit (ITSC) faults. In this method, first, a 1-D group convolutional neural network (CNN)-based conditional generative adversarial network (CGAN) is used to expand the collected dataset. Subsequently, a DQN-based fault diagnosis method is applied. A 1-D deep residual shrinkage network and the prioritized experience replay (PER) strategy are introduced into the original network structure, and the sampling strategy of the original network is optimized. Finally, the fault diagnosis capability of the proposed method was evaluated in several experiments. The proposed method considerably outperformed competing algorithms: it had a diagnosis rate of 98.49% for the ITSC faults of PMSMs and a diagnosis rate of 99.50% on the bearing dataset of Case Western University with certain generalizations. The proposed method is promising for use in circuit fault detection.