Recent Advances in Fault Diagnosis Methods for Electrical Motors- A Comprehensive Review with Emphasis on Deep Learning

Document Type : Review Article

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School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

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 techniques 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 architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, 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. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners seeking to enhance the fault diagnosis process of electrical motors using deep learning techniques.

The advantages of each method are stated individually, however, an overall analysis is provided as a guide for future studies. Based on the findings Deep learning-based technologies are replacing manual expert involvement as the new norms in this field. Additionally, methods are getting more standard and official benchmarks are getting created.

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