Adaptive Sparse Attention Wavelet Network for the Robust Open-Circuit Fault Diagnosis in PMSM Drives
Yilin Jiang, Longmiao Chen, Manyi Wang, Liuxuan Wei, Ming Li
Abstract
Inverter open-circuit faults can cause significant changes in the output current of permanent magnet synchronous motor (PMSM), so many fault diagnosis methods based on convolutional neural networks take the current as monitor signals. However, there are some shortcomings in the application of convolutional neural networks for fault diagnosis. On the one hand, most of the traditional pooling methods confuse the different frequency components of the feature map due to ignoring the classical sampling theorem, which decreases the accuracy of fault diagnosis. On the other hand, the generalization performance of the diagnostic model is not good enough when the data distribution changes due to variations in the operating conditions of the drive system. To address these issues, this paper proposes an open-circuit fault diagnosis method using an adaptive sparse attention wavelet network (ASAWN), where a novel pooling layer, termed as adaptive sparse attention wavelet pooling (ASAW-pooling) layer, is designed using discrete wavelet transform and adaptive sparse attention mechanism. The pooling layer improves the robustness of the diagnostic model by filtering out the high-frequency components of the current data and the redundant information of the attention mechanism. Experimental results show that the proposed method can effectively diagnose 22 types of open-circuit faults. Besides, a comparison with other models shows that the proposed model not only has high accuracy but also performs strong robustness.