A comparative study of deep learning efficiency in the classification of electrical faults of permanent magnet synchronous motor

Abstract Permanent Magnet Synchronous Motors (PMSMs) are widely used in modern industrial applications due to their high efficiency, reliability, and compact size. However, faults in PMSMs, such as stator winding failures, can lead to significant performance degradation and operational failures. Traditional fault detection methods often rely on signal processing and manual analysis, which may be time-consuming and lacking in accuracy. This study explores the application of deep learning techniques for automated fault detection in PMSMs. The deep learning models based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are employed to classify electrical faults in the motor data, which includes the scalogram images of stator current signal allowing models to learn fault patterns. The performance of the used networks has been compared, in order to choose the reliable one for classification purposes and hence to be utilized for developing the prediction system. The experimental results show that the ResNet50 has better capability to classify the variation of data used where it could achieve 100% of accuracy, recall, precision, and F1 score as compared to other techniques.