A comprehensive approach to convolutional neural networks‐based condition monitoring of permanent magnet synchronous motor drives

The increasing complexity of modern industrial systems calls for automatic and innovative predictive maintenance techniques. As suggested by the Industry 4.0 process, this demand translates in the need of more-intelligent drives. Herein, the use of a special kind of neural networks to interpret the data from motor currents for diagnostic purposes is described. The early detection of possible faults in the electrical motor allows programmed maintenance and reduces the risk of unplanned shutdowns. The innovation is in the overall approach to the neural network training, which does not call anymore for a large set of faulty motors. A large training dataset generated using a combination of tuned motor models and some data augmentation techniques is proposed. The result is a comprehensive and effective motor condition monitoring algorithm, whose hearth is a convolutionary neural network trained by a safe and cheap simulation-based dataset. The details of the design are fully reported here. The method has been implemented in the laboratory and fully tested on both healthy and faulty permanent magnet synchronous motors. The generality of the proposed method also paves the way for the detection of other failures and the application to different electrical motors.