Bearing fault diagnosis in induction motor using continuous wavelet transform and convolutional neural networks

Abstract

Induction motors are widely used in various industries due to their high efficiency, reliability and low cost. However, faults in these motors can lead to serious problems, such as unexpected shutdowns, decreased efficiency, and even damage to other parts of the system. Monitoring and diagnosing these faults are necessary. In this study, we propose a new approach for diagnosing bearing faults using convolutional neural network (CNN) and continuous wavelet transform (CWT). The suggested approach uses Scalograms with various CWT types as the network's input and utilizes many epochs and various batch sizes (Multi Ep-Batch) throughout the bearing fault classification training and testing phases. To assess our method, we implemented an extension of the Squeeze Net pre-trained model (transfer learning). The results show that the proposed method outperforms traditional methods in terms of accuracy and computational efficiency in detecting bearing faults. These results are based on publicly available MFPT data, and the proposed approach is compared to traditional methods. This work opens new research avenues in the field of bearing fault diagnosis and provides a promising solution for real-world applications.