Machine Learning-Based Detection of Inter-Turn Short-Circuit Faults in Synchronous Machines Using Stray Flux Time-Domain Analysis

This paper deals with an inter-turn short-circuit fault detection approach for synchronous machines using timedomain analysis of the external magnetic field. We compare single-sensor and dual-sensor configurations, extracting 22 timeseries features using the Catch22 algorithm to classify healthy and faulty states. Machine learning classifiers are employed; they are initially trained on simulated data and later refined using a mixed dataset incorporating experimental signals to improve generalization. Results show that logistic regression struggles with nonlinear patterns, while random forest demonstrates superior robustness. The dual-sensor configuration further enhances detection, confirming it as the most effective strategy for accurate and reliable fault classification.