Machine Learning-Based Detection of Inter-Turn Short Circuits in the Stator Windings of 220 V, Three-Phase Induction Motor

Abstract

Induction motors play a vital role in industrial operations; however, stator inter-turn short-circuits faults remain a common and critical source of failure. This paper presents a machine learning-based diagnostic approach for detecting stator inter-turn short-circuit faults in three-phase induction motors operating at 50 Hz. Secondary data from a controlled test bench were processed using Power Spectral Density to determine energy distribution and guide the design of a Butterworth bandpass filter (20–350 Hz). The filtered signals were then analyzed using the Hilbert Transform to extract statistical features, which were ranked using the Minimum Redundancy Maximum Relevance algorithm to identify the most discriminative parameters. Two supervised classifiers, Support Vector Machine and Random Forest, were developed and validated using MATLAB’s Classification learner app with 5-fold cross-validation. The Support Vector Machine achieved an accuracy of 94.19%, while the Random Forest model achieved 99.51% with macro and F1-scores of 0.9951 and near-perfect area under the curve values. The results confirm that the Random Forest classifier provides superior generalization, sensitivity, and robustness in fault detection compared to Support Vector Machine. This study successfully demonstrates that combining Power Spectral Density, Hilbert Transform, Minimum Redundancy Maximum Relevance, and ensemble learning yields a highly effective framework for predictive maintenance and reliable fault diagnosis in industrial motor applications.