Abstract
Induction motors (IMs) are vital in industrial applications. Although all motor faults can disrupt its operation significantly, stator turn to turn faults (ITFs) are the most challenging one due to their detection difficulties. This paper introduces an AI-based approach to detect ITFs and assess their severity. A simulation based on an accurate mathematical model of the IM under ITFs is employed to generate the training data. Recognizing that ITFs directly affect the motor's current balance, complex current unbalance coefficient is identified and used as the key feature for detecting ITFs. Since unbalanced supply voltage (USV) can also disrupt current balance, the AI models are trained to account for USV by incorporating complex voltage unbalance coefficient that helps to distinguish between ITF-induced and voltage-induced imbalances. After feature extraction, the AI models are trained and validated with simulation data. The approach's effectiveness is further tested using an experimental setup, where measurements from motors under various fault conditions, including USV scenarios, are considered. The results indicate that the gradient boosting model outperforms other ML models in detecting ITFs in IMs and assessing their severity. In the pursuit of achieving highest possible performance, DNN is tested and compared with ML models. The study reveals that DNN demonstrates superior performance in all tested scenarios including USV making DNN the top performer that to be used in the proposed approach. The proposed AI-based approach based on DNN offers high accuracy in fault detection and can effectively distinguish between ITFs and USV-induced anomalies, maintaining low estimation errors and robust performance across different operational conditions.