recent advances in fault diagnosis methods for electrical motors- a comprehensive review with emphasis on deep learning

This paper provides a review of deep learning-based methods for fault diagnosis of electrical motors. electrical motors are crucial components in various industrial applications, and their efficient operation is essential for maintaining productivity and minimizing downtime. traditional fault diagnosis methods have limitations in accurately detecting and classifying motor faults. deep learning, a subset of machine learning, has emerged as a promising approach for improving fault diagnosis accuracy. this review discusses various deep learning methods, such as convolutional neural networks, recurrent neural networks, autoencoders, transfer learning, and transformers that have been utilized for motor fault diagnosis. additionally, it examines different datasets and features used in these methods, highlighting their advantages and limitations. the paper also discusses challenges and future research directions in this field, such as data augmentation, transfer learning, and interpretability of deep learning models. based on the findings, it is concluded that deep learning-based technologies are replacing manual expert involvement as the new norms in this field. besides, methods are getting more standard, and official benchmarks are being created. a summarized table is provided at the end of the paper and numerous methods have been reported.