A Hybrid Deep Learning Framework for Multi-Sensor PMSM Fault Diagnosis Based on SVMD Denoising and Spatiotemporal...

With the increasing application of inverter-fed permanent magnet synchronous, motors (PMSMs) in industrial and intelligent energy systems, reliable fault detection and diagnosis (FDD) has become increasingly important for ensuring operational safety and system reliability. However, conventional single-sensor-based approaches usually exhibit limited robustness under varying operating conditions due to measurement noise, load fluctuations, and incomplete fault information. Therefore, multi-sensor information fusion has attracted increasing attention in PMSM fault diagnosis because it can provide complementary information from different sensing sources. This paper proposes a hybrid deep learning framework for multi-class PMSM fault diagnosis, integrating successive variational mode decomposition (SVMD)-based signal denoising, parallel temporal and spatial feature extraction using temporal convolutional network (TCN) and convolutional neural network (CNN), and BiLSTM with attention-based feature enhancement. First, SVMD is employed to adaptively decompose multi-sensor signals, and components with low Pearson correlation coefficients are removed as noise-dominated components. The remaining components are reconstructed to obtain denoised signals with improved quality. Subsequently, parallel TCN and CNN branches are constructed to extract temporal and spatial features, respectively, enabling comprehensive representation of spatiotemporal characteristics from multi-channel signals. Finally, a BiLSTM combined with an attention mechanism is utilized to model long-term dependencies and emphasize discriminative features for accurate fault classification. The proposed method is evaluated on a public PMSM dataset containing eight sensor channels and nine operating states. Experimental results demonstrate that the proposed framework achieves an accuracy of 98.5%, outperforming several existing representative models.