Journal of Engineering Research

As motors are widely applied in various fields, fault diagnosis has become crucial. Traditional methods have limitations, while deep learning, with its powerful feature learning capabilities, is emerging as a promising approach. By constructing appropriate neural network models and collecting multi-source data from motor operation, such as vibration and current, through data preprocessing and model training optimization, it can accurately identify various fault modes of motors. This effectively enhances diagnostic accuracy and efficiency, providing strong technical support for ensuring the reliable operation of motors.

Deep learning; Motor fault; Data acquisition; Model training; Fault diagnosis