Research on convolutional neural networks in motor fault diagnosis

The state of the motor greatly affects the overall performance of the transmission system, and implementing advanced motor fault diagnosis technology can prevent substantial financial losses. Deep learning algorithms, such as convolutional neural networks, are increasingly gaining popularity due to their ability to handle the prominent challenges posed by high feature dimensionality, large datasets, ambiguous labels, and sparse occurrence of motor fault types and causes. This paper extensively examines the usage of convolutional neural networks (CNN) and their different versions in different motor fault diagnosis problems by analyzing a wide range of international journal papers, leading to essential findings.