Comparative Analysis of Decision Tree (DT) & Deep Neural Network (DNN) Techniques to Diagnose the Stator Winding Fault in the Permanent Magnet Synchronous Motor (PMSM)

Abstract

Every industry is moving towards automation, and Electrical motors like DC and AC play a significant role in the overall automation scheme. Electrical motors, however, are prone to multiple manufacturing defects, and untimed wear and tear. Automated detection of faults, while the motor is in operations, can prevent automation breakdowns, and unforeseen accidents. The majority of faults in electrical motors are because of overheating of windings, bearing misalignment, shaft mismatch, vibrations, noise, etc. A few hours of Electric Motor running can generate a lot of sensor data measuring temperature, torque etc.. Manual analysis of such data can be extremely time consuming. Machine Learning can be used to perform automated analysis of data and real-time fault detection as suggested by many experts. In this research, we have used a few machine learning techniques to train the model on top of sensor data and use the model to diagnose the electric motor faults, occurring because of heat in the stator winding part of the permanent magnet synchronous motor (pmsm). We have specifically compared Decision Tree (DT) and Deep Neural Network (DNN) techniques on real-life datasets, and compared their accuracy. As per the final results, Decision Tree (DT) and Deep Neural Network (DNN) have 93.82% & 97% accuracy respectively.