Fault Diagnosis of Induction Motor Under Varying Operating Condition

Abstract

Induction Motor (IM) is the most important prime mover in the automotive industry and has great potential in Electric Vehicles (EVs). IM failure is one of the most prevalent reasons for EV failure. This paper presents a fault diagnosis method for IM to enhance the efficiency, performance, and availability of EV and to reduce its maintenance costs. Firstly, current and vibration signatures were acquired at varying speed conditions from four IMs having different fault conditions. The acquired signatures were decomposed using Hilbert Transform (HT) and further converted into the time-frequency domain using Constant-Q Transform (CQT). This time-frequency data were utilized for training the Machine Learning (ML) and Deep Learning (DL) model. A comparative analysis was done in terms of classification accuracies given by the ML and DL models. Eventually, the model performance was also studied for both current and vibration signatures. The experimental finding showed that the DL model has better potential than ML models for IM fault diagnosis under varying operating conditions.