Fault Detection in Soft-started Induction Motors using Convolutional Neural Network Enhanced by Data Augmentation Techniques

Stray flux analysis is an interesting source of information for the diagnosis of Induction Motors (IMs). The widespread use of these motors in industry leads to a necessity of additional tools and methods for their predictive maintenance. On the other hand, soft-starters are increasingly used to reduce the high consumption of IMs at start-up. In this work, AI techniques based on convolutional neural networks are applied to detect rotor faults in soft-started motors. The objective is the automatic early detection of broken bars, avoiding the necessity of user intervention to interpret the obtained results. This work proves the potential of the methodology, including a successful set of experimental results.