Electrical faults classification in permanent magnet synchronous motor using ResNet neural network

The predictive maintenance of permeant magnet synchronous motor is highly required as this kind of motor has been commonly employed in electric vehicles, industrial systems, and other applications owing to its high power density output, as well as the regenerative operation characteristics during braking and deceleration driving conditions. One of the most important causes of PMSM failure is the stator short and drive switches failure. These problems have attracted more attention in the field of deep learning for fault detection purposes in the early stages, to avoid any system breakdown, and to decrease the risk and price of maintenance. In this paper, we investigate the possibility of detecting the electrical faults in PMSM by generating our data which includes current signals that have been analyzed and preprocessed by applying Continuous Wavelet Transform (CWT) to select the reliable features this conversion will be used to train ResNet 50. The evaluation metrics have shown that ResNet 50 achieves an accuracy of 100% for the classification of faults.