Short-Time Fourier-Transform–CNN–LSTM-Based Eccentricity Fault Diagnosis System for Three-Phase Permanent-Magnet-Synchronous Motors (PMSMs)

Accurate discrimination among static eccentricity fault (SEFs), dynamic eccentricity fault (DEFs), and mixed eccentricity fault (MEFs) in permanent-magnet-synchronous motors remains difficult because these conditions produce similar stator-current patterns. This paper proposes an STFT–CNN–LSTM-based Eccentricity Fault Diagnosis System (STFT-CL-EFDS) for classifying Normal, SEF, DEF, and MEF conditions using only three-phase stator currents. Six signal transformations were initially compared, after which the three leading representations—STFT, DWT, and CWT—were evaluated with seven neural-network architectures. Sensitivity and ablation analyses selected a 500-sample observation window and a compact log-magnitude STFT representation over the nominal 0–1 kHz band. Following full-schedule retraining, the proposed model achieved 99.57% accuracy and a 99.57% weighted F1-score, with recalls of 100.00%, 98.66%, 100.00%, and 99.53% for Normal, SEF, DEF, and MEF, respectively. It exceeded the strongest machine-learning benchmark, STFT–MLP, by 8.42 percentage points in accuracy and 8.52 percentage points in weighted F1-score. On an independent unseen test bench, the proposed model provided the most balanced response across the three fault types and ultimately converged to the correct class in every case, although temporary DEF–MEF confusion remained. These results demonstrate the effectiveness of STFT-CL-EFDS for current-only multiclass PMSM eccentricity diagnosis. The main contributions of this study are as follows: (1) a systematic comparison of six signal-transformation methods, namely Fast Fourier Transform (FFT), STFT, Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), and Variational Mode Decomposition (VMD), to determine their suitability for eccentricity fault diagnosis; (2) a comparative evaluation of seven neural-network architectures, including CNN, LSTM, CNN–LSTM, DNN, TCN, ModernTCN, and TimesNet, using the three best-performing transformation methods, namely STFT, CWT, and DWT; and (3) the development of a unified current-only STFT-CL-EFDS that combines STFT-based time–frequency representation with convolutional feature extraction and temporal-sequence learning for the classification of Normal, SEF, DEF, and MEF conditions.