Abstract: Early detection of motor faults (e.g., bearing defects, stator winding inter-turn shorts) is vital for preventing catastrophic failures. This article presents machine learning (ML) frameworks for condition monitoring, including vibration analysis, stator current signature analysis (MCSA), and acoustic emission sensing. We compare traditional signal processing (FFT, wavelet transforms) with deep learning models (convolutional neural networks, autoencoders) for fault classification. Real-world datasets from industrial motors validate the superiority of hybrid models in detecting incipient faults.
Content Highlights:
Data-Driven Prognostics: Remaining useful life (RUL) estimation using recurrent neural networks (RNNs).
Edge Computing Deployment: Lightweight ML models for real-time monitoring in resource-constrained environments.
Explainable AI (XAI): SHAP value analysis for interpreting fault diagnosis decisions in safety-critical systems.
Cyber-Physical Security: Addressing adversarial attacks on ML-based motor control systems.
