Experimental results on stator inter-turn faults across multiple low-load operating conditions demonstrate superior reconstruction performance compared with representative recurrent, transformer-based, and graph-based autoencoder models while maintaining computational efficiency suitable for low-latency deployment.
Abstract
Early fault detection and fault severity assessment are essential for ensuring the reliable operation of induction motors. While stator winding faults can often be identified through current-based monitoring techniques, reliable characterization of fault severity remains challenging, particularly for incipient faults under low-load operating conditions. To address this problem, this study proposes a low-latency fault diagnosis framework that combines healthy-only anomaly detection with multi-level fault severity assessment. At its core, an Energy-Modulated Transformer Autoencoder (EM-TransAE) is developed to model healthy operating behavior by incorporating instantaneous current energy into the self-attention mechanism. The framework adopts a two-stage learning strategy. First, EM-TransAE is trained exclusively on healthy data within a one-class learning paradigm, where fault detection is performed through reconstruction-error-based anomaly assessment. The proposed model achieves a reconstruction error of 0.000484, indicating high-fidelity representation of healthy operating conditions. Second, encoder-derived latent features and reconstruction error are combined to form a diagnostic feature set for XGBoost-based multi-level fault severity assessment, achieving an accuracy of 96.93%. To support practical deployment, the framework is integrated within an edge-based streaming architecture for real-time operation. Experimental results on stator inter-turn faults across multiple low-load operating conditions demonstrate superior reconstruction performance compared with representative recurrent, transformer-based, and graph-based autoencoder models while maintaining computational efficiency suitable for low-latency deployment.
A hybrid two-stage machine learning pipeline that decouples detection from classification is proposed, and the direction of the zero-sequence signature is found to be system-dependent, motivating a learned decision boundary in place of a fixed relay threshold.
Sahil Manikshete, A. Gujarathi, Thanh Long Vu et al.· 0 citations
Measurement-level feature attribution shows that the DL-Xformer decision is driven by physically meaningful current and voltage channels at the attacked measurement location, which motivates a layered protection architecture for next-generation inverter-dominated smart grids.
Emad Abukhousa, Saman A. Zonouz, A. P. S. Meliopoulos· 0 citations
The results confirm that the proposed framework provides a comprehensive and efficient solution for real-time fault analysis by combining classification, localization, temporal analysis, and stability-aware decision support within a single model.
Nazmun Nahar Karima, M. Hazari, Shameem Ahmad 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
A progressive three-stage time–frequency learning framework that identifies series arc faults directly from normalized current waveforms and provides robust discrimination across unseen measurement sessions within the evaluated load categories and operating conditions is presented.
Seoyoung Jeon, Won-Kyu Choi, Sungsoo Kwon et al.· Italian National Conference...· 0 citations
Induction motors are central to industrial processes, yet their unexpected failure incurs significant production losses and maintenance costs. Motor Current Signature Analysis (MCSA) is a well-established non-intrusive technique for identifying electrical and electromechanical faults via frequency-domain analysis of the stator current. However, manual spectral interpretation remains challenging under low signal-to-noise conditions and variable operating regimes. This paper presents a complete, reproducible framework integrating physics-based simulation, interpretable feature engineering, and lightweight machine learning for real-time supply-fault diagnosis. A three-phase squirrel-cage induction motor is modelled in MATLAB/Simulink to validate MCSA sideband signatures under healthy, phase-loss, and voltage-imbalance conditions. A cost-effective test bench equipped with an ACS712 Hall-effect sensor and an Arduino UNO microcontroller acquires stator current data from 15 independent acquisition runs, yielding a balanced dataset of 1440 fixed-length windows (480 per class). All experiments are conducted under no-load conditions (slip $\approx \mathbf{0. 0 2})$, which represents a conservative lower bound on in-service performance since sideband energy grows with load-induced slip. A 1024-sample Hann-windowed FFT extracts an eight-dimensional feature vector combining frequency-domain indicators and time-domain statistics. Random Forest and XGBoost classifiers are evaluated under random-window and leakage-controlled group-aware protocols. Under the group-aware split, XGBoost achieves 91.3% (±1.4%) accuracy and a macro-F1 of $\mathbf{9 0. 7 \%}(\mathbf{\pm 1. 6 \%})$; Random Forest achieves $\mathbf{8 9. 6 \%}(\mathbf{\pm 1. 8 \%})$ accuracy and a macro-F1 of 88.4% (±2.1%). A McNemar test confirms the performance gap between classifiers is statistically significant $(\mathbf{p}<\mathbf{0. 0 5})$. An explicit offline-real-time benchmark reports end-to-end latency (280-295 ms), fault-to-alarm detection delay (~1.8 s), and false-alarm behaviour under a persistence-rule controller, bridging the gap between dataset-level accuracy and embedded deployment constraints.
Sara Sghiouri, H. Sabir, Mohamed Bezza et al.· IEEE International Conferenc...· 0 citations