A machine learning-based classification method for SynRM faults.

Synchronous reluctance motors (SynRMs) are increasingly critical in industrial and traction applications due to their high efficiency, magnet-free construction, and thermal robustness. However, fault diagnosis frameworks specifically tailored to SynRMs remain scarce, with existing literature predominantly focusing on induction or permanent-magnet machines, isolated fault scenarios, or simulation-only validations. This paper presents a comprehensive multi-fault diagnosis framework that addresses critical gaps in SynRM condition monitoring through rigorous experimental validation and reproducible methodology. Inter-turn short-circuit faults (5% severity, 12/240 turns) and inner-race bearing defects were experimentally induced on a 2.2 kW laboratory SynRM under varying load conditions (no-load, 50%, and 100% rated load), while static/dynamic eccentricity faults were modelled via ANSYS Maxwell FEA and statistically aligned with experimental distributions through noise injection and domain adaptation. Discrete Wavelet Transform (Daubechies 4, 5-level decomposition) was employed to extract time-frequency features from stator currents, yielding a 12-dimensional feature space capturing harmonic signatures from 0 Hz to 5 kHz. Eight machine-learning classifiers were evaluated under standardized protocols: stratified 80/20 train-test splitting (group-based to prevent data leakage), 5-fold cross-validation, and systematic hyperparameter optimization via Grid Search. Results demonstrate that ensemble tree-based methods significantly outperform linear models (McNemar's test, p < 0.05). Random Forest achieved 99.975% accuracy with 100% recall for inter-turn fault detection and 99.975% accuracy with 100% recall for bearing faults, prioritizing zero false negatives essential for protection relaying. For eccentricity classification, AdaBoost and XGBoost attained 100% accuracy with O(NlogN) training complexity, avoiding the prohibitive O(N