Leveraging Deep Learning for Fault Detection and Classification of Induction Machines: A Review

The management of incipient faults in induction machines (IMs) is crucial for ensuring reliability and efficiency in diverse industrial applications, including power grids, electric vehicles, and manufacturing processes. This review explores advanced fault detection and diagnosis (FDD) strategies, emphasizing deep learning (DL) methods such as convolutional neural networks (CNN), recurrent neural networks (RNN), and autoencoders for fault detection and classification. Traditional machine learning (ML) approaches are also discussed, highlighting their integration with signal processing techniques like wavelet transforms and Fourier transforms to enhance FDD accuracy. Additionally, the potential of physics-informed neural networks (PINNs) is examined, demonstrating how incorporating physical knowledge into data-driven models can improve diagnostic precision. The paper presents an analysis of recent publications, identifies current research gaps, and proposes future directions, including the development of robust AI-based FDD systems and the consideration of stochastic industrial data for more accurate predictive maintenance. By offering a comprehensive overview of FDD techniques and highlighting key research areas, this review aims to advance the reliability and performance of IMs.