Abstract
Inter-turn short-circuit (ITSC) faults are among the most frequent stator winding faults in induction motors, often leading to irreversible damage. In this context, this work proposes a Machine Learning (ML) based framework for classifying ITSC faults using experimental stator current data comprising 13 categories. The framework employs Direct-Quadrature (dq) transformation, signal windowing, and feature extraction for data processing, followed by the training of multiple ML classifiers, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forests, XGBoost, and LightGBM. For benchmarking, a Convolutional Neural Network (CNN) was also trained directly on raw signals. The hyperparameters of all the models were tuned using Particle Swarm Optimization (PSO), Optuna, and random search. The results show that the proposed framework achieves high classification performance, with XGBoost tuned using random search reaching up to 99.60% accuracy across 13 classes. The CNN, relying on end-to-end learning, achieved lower performance compared to the developed ML classifiers, highlighting the importance of data representation for accurate fault classification under limited data. For hyperparameter tuning, random search achieved performance comparable to complex methods with a lower processing burden, making it a viable option for hyperparameter optimization. These findings confirm that careful data engineering is as important as model complexity and is key to achieving efficient ITSC fault diagnosis.