A Real-Time Framework for Low-Load Fault Detection and Multi-Level Fault Severity Assessment Using an Energy-Modulated Transformer Autoencoder
Early fault detection and fault severity assessment are essential for ensuring the reliable operation of induction motors. While stator winding faults can often be identified through current-based monitoring techniques, reliable characterization of fault severity remains challenging, particularly for incipient faults under low-load operating conditions. To address this problem, this study proposes a low-latency fault diagnosis framework that combines healthy-only anomaly detection with multi-level fault severity assessment. At its core, an Energy-Modulated Transformer Autoencoder (EM-TransAE) is developed to model healthy operating behavior by incorporating instantaneous current energy into the self-attention mechanism. The framework adopts a two-stage learning strategy. First, EM-TransAE is trained exclusively on healthy data within a one-class learning paradigm, where fault detection is performed through reconstruction-error-based anomaly assessment. The proposed model achieves a reconstruction error of 0.000484, indicating high-fidelity representation of healthy operating conditions. Second, encoder-derived latent features and reconstruction error are combined to form a diagnostic feature set for XGBoost-based multi-level fault severity assessment, achieving an accuracy of 96.93%. To support practical deployment, the framework is integrated within an edge-based streaming architecture for real-time operation. Experimental results on stator inter-turn faults across multiple low-load operating conditions demonstrate superior reconstruction performance compared with representative recurrent, transformer-based, and graph-based autoencoder models while maintaining computational efficiency suitable for low-latency deployment.