Behavioral Fault Diagnosis in Inverter-Driven PMSM Systems Using a Hybrid CNN–BiLSTM–Attention Deep Learning Framework with SHAP-Based Interpretability

Reliable fault detection and diagnosis (FDD) plays a key role in inverter-driven permanent magnet synchronous motor (PMSM) systems, especially in applications where operational continuity cannot be compromised. In this work, a hybrid deep learning framework is developed by combining one-dimensional convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and a multi-head self-attention mechanism. The model targets multi-class fault classification in a three-phase PMSM inverter system. Its effectiveness is evaluated on a publicly available experimental dataset consisting of 10,892 multi-sensor samples collected under nine operating conditions, including normal operation, open-circuit faults, short-circuit faults, and half-bridge overheating scenarios. To avoid temporal data leakage, a block-aware chronological splitting strategy is applied. Model hyperparameters are determined through a validation process involving 24 different configurations. The proposed CNN–BiLSTM–Attention model achieves a macro F1-score of 0.9681 ± 0.0195, accuracy of 0.9810 ± 0.0102, Matthews correlation coefficient (MCC) of 0.9757 ± 0.0130, and ROC-AUC of 0.9996 ± 0.0003 over five independent runs, achieving the highest accuracy and MCC among all evaluated models; although the Random Forest baseline attains a marginally higher macro F1 score (0.9747) by operating on temporally aggregated features without temporal modelling, the proposed model provides superior discrimination across the full confusion matrix structure alongside end-to-end temporal interpretability via SHAP. Model interpretability is provided through SHAP (SHapley Additive exPlanations) GradientExplainer analysis, revealing that temperature-related features dominate fault discrimination, particularly for over-heating conditions, while current imbalance features are critical for distinguishing open- and short-circuit faults.