Diagnosis of ITSC Faults in Stator Windings of Controlled PMSM: Entropy-Based Feature Extraction and MLP Classifier
This paper investigates the diagnosis of Inter-Turn Short-Circuit (ITSC) faults in Permanent Magnet Synchronous Machines (PMSM) under controlled experimental conditions. The study is based on stator current signals acquired for different speeds, load levels, and fault severities. Since time-domain waveforms under variable speed drive do not clearly reveal the fault, Shannon entropy is used to capture variations in signal complexity and extract a discriminative feature. This feature is then used as input to a Multi-Layer Perceptron (MLP) classifier. The experimental results show that the proposed approach can effectively distinguish healthy and faulty conditions, achieving mean values of 92.8% accuracy, 94% precision, 96.6% recall, and 95.2% F1-score. These results demonstrate the potential of Shannon entropy combined with MLP for reliable ITSC fault diagnosis in PMSMs.