Rotor inter-turn short-circuit (ITSC) faults in synchronous generators introduce electromagnetic asymmetries that can lead to torque ripple, unbalanced magnetic pull, and progressive mechanical degradation. While most existing studies focus on binary classification and severe fault conditions, the assessment of incipient rotor ITSC severity using displacement-sensitive vibration measurements remains relatively underexplored. This paper proposes a vibration-based diagnostic framework for multi-class severity classification of rotor ITSC by integrating an eddy-current displacement sensor with physically motivated feature extraction. An 18-dimensional hybrid feature set is designed to characterize electromechanical modulations induced by rotor electromagnetic asymmetry. Using an XGBoost classifier with leave-one-out cross-validation, the proposed approach achieved 90.56% overall accuracy, including 99% recall for healthy operation and 87% recall for mild fault conditions. The results suggest that displacement-sensitive vibration analysis enables effective severity-aware diagnosis of rotor ITSC with minimal sensor requirements.
The problem of fault diagnosis in electrical motors has an important impact on the supervision of dynamic systems, and model-based methods are efficient tools for this purpose. In this regard, this work presents a novel method for detecting inter-turn short circuits (ITSCs) in the stator windings of permanent magnet synchronous motors (PMSMs). The approach is based on algebraic identification to process the motor voltage signals, estimating the offsets, amplitudes, and phases of the fundamental and third-harmonic components. Fault detection is performed in two steps: first, a voltage imbalance index is evaluated to determine the presence of abnormal operating conditions. Subsequently, characteristic patterns in the estimated parameters are analyzed to identify both the fault type and the affected phase(s). The experimental results show that single-phase ITSC faults produce a reduction in the offset of the faulted phase together with an increase in its third-harmonic amplitude, whereas phase-to-phase ITSC faults lead to an increase in the offsets of the affected phases and nearly identical third-harmonic amplitudes between them. In both cases, only minor variations are observed in the estimated phase angles. The effectiveness of the proposed methodology is supported through theoretical analysis and validated experimentally using voltage measurements acquired from a PMSM test bench. The results demonstrate that the proposed technique can accurately identify fault conditions through voltage imbalance and harmonic-pattern analysis, providing a practical and computationally efficient methodology for PMSM stator winding fault diagnosis.
David Marcos-Andrade, Francisco Beltrán-Carbajal, I. Rivas-Cambero et al.· Mathematics· 0 citations
The outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
H. Zaimen, T. Thelaidjia, Makhlouf Chouki et al.· International Journal of Ele...· 0 citations
Rotor faults are a prevalent failure mode in induction motors. In the stator current of induction motors, the fault characteristic frequency component is in close spectral proximity to the fundamental component, yet possesses an extremely low amplitude. This makes it prone to being overwhelmed by the leakage of the fundamental component and ambient noise. Accurate extraction of the fault characteristic component from the stator current is of great significance for the quantitative assessment of rotor faults. This paper proposes a detection method that leverages two-stage correlation analysis integrated with phase difference correction to suppress the fundamental component and noise, allowing for the precise extraction of the rotor fault frequency component. Results from simulations and experiments indicate that the proposed method effectively mitigates the impact of fundamental component leakage and retains robust detection accuracy under low signal-to-noise ratios.
Wenbiao Hu, Zhaorui Lv· International Conference on...· 0 citations
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.
Y. Azzoug, M. Boukhnifer, R. Pusca· International Conference on...· 0 citations
Tank vibrations of a power transformer, originating primarily from winding vibration and core vibration through mechanical coupling and fluid–structure interaction, are regarded as essential carrier signals for assessing the integrity of the winding. To improve the diagnostic accuracy of winding condition, this paper presents a vibration centroid-based diagnostic model that integrates feature fusion from vibration signals. According to the frequency spectrum of vibration signals obtained using Zoom-FFT, a set of new spatial vibration feature vectors—namely vibration centroid coordinates and Boyce-Clark shape index—were defined. This approach converts spatially distributed vibration signals into compact and discriminate indicators. A diagnostic model was subsequently constructed by integrating the grey wolf optimization (GWO) algorithm with the least squares support vector machine (LSSVM), ensuring that optimal classification performance was achieved. No-load, short-circuit, and load tests were made on a 35 kV-rated oil-immersed transformer. During the experiments, the transformer winding was divided into four categories: healthy condition, winding looseness, axial deformation, and radial deformation. The proposed GWO-LSSVM-based classifier was trained and tested using the defined vibration feature vectors. The results indicate that the proposed method achieves superior performance, with a recognition rate of 98.44%, and offers high efficiency.
Bo Ren, Peidong Gao, Fenghua Wang et al.· Energies· 0 citations
Early and reliable diagnosis of inter-turn short-circuit (ITSC) faults is critical to maintaining the reliability, availability, and safe operation of doubly fed induction generators (DFIGs) used in wind energy conversion systems (WECSs). Incipient winding faults are particularly challenging to identify because their electrical signatures can be masked by the inherent spectral complexity of DFIG operation and variations in wind and operating conditions. This study proposes a hybrid Fast Fourier Transform-Adaptive Neuro-Fuzzy Inference System (FFT–ANFIS) diagnostic framework for the detection, localization, and severity assessment of ITSC faults in both stator and rotor windings. The proposed approach employs the FFT method to extract fault-sensitive harmonic components from stator-current signals, which are subsequently used as diagnostic features by an Adaptive Neuro-Fuzzy Inference System (ANFIS). By integrating spectral feature extraction with nonlinear neuro-fuzzy classification, the proposed framework provides an efficient and interpretable mechanism for distinguishing healthy and faulty operating conditions and assessing fault severity. The methodology is evaluated using MATLAB/Simulink simulations under healthy and multiple ITSC fault conditions with different fault locations and severity levels. The results demonstrate 100% classification accuracy for stator faults, rotor faults, and multiple short-circuit (MSC) fault conditions, together with near-zero prediction error in fault-severity estimation. These results confirm the high discriminative capability of the selected FFT-based spectral features and the effectiveness of ANFIS in establishing the nonlinear relationship between fault signatures and fault conditions. In addition, the proposed framework maintains low computational complexity and is therefore suitable for real-time condition-monitoring applications. Compared with existing diagnostic approaches, the proposed method provides a unified framework for multi-fault diagnosis while combining high diagnostic accuracy, computational efficiency, and interpretable decision-making. The proposed FFT–ANFIS framework consequently offers a practical approach for early fault detection and condition-based maintenance of DFIG-based wind turbines, with the potential to reduce unplanned downtime, maintenance requirements, and energy-production losses.
M. Abid, S. Laribi, M'hamed Larbi et al.· Algorithms· 0 citations