A lightweight deep learning framework for motor fault diagnosis using time-frequency current signatures

This study proposes a motor fault diagnosis method that uses the measurement and analysis of alternating current (AC) signals. This approach enables assessment of motor operational health by classifying it as either normal or exhibiting one of three specific faults: inner race defect, outer race defect, or misalignment. The diagnostic process starts with the motor current signal, which is transformed using a continuous wavelet transform to create a time-frequency representation. Subsequently, a deep learning framework is developed, comprising two main sub-networks: a feature extractor with an attention mechanism and a fault-classification neural network specifically tailored to identify common AC-motor faults. The proposed method outperformed the other methods in terms of diagnostic accuracy across various operating conditions on two public benchmark datasets. Furthermore, experimental evaluation on public datasets confirmed the framework’s effectiveness for both three-phase induction motors and permanent-magnet synchronous motors. The results show that the proposed model achieves higher accuracy while remaining lightweight, making it suitable for deployment on edge-computing devices in industrial environments.