Skip to content
Log in

Author

Soukayna Tantani

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

Simulation-Driven Diagnosis of Stator Phase-Current Imbalance in Wind Turbine Induction Generators Using Multi-Signal Features and Explainable Machine Learning

Wind turbine reliability depends on timely identification of electromechanical faults, especially in generator-related subsystems under variable mechanical loads. This study presents a simulation-based, multi-signal, and physically interpretable diagnostic workflow for wind turbine electrical systems. It combines multiphysics simulation, FFT feature extraction, and explainable machine learning, emphasising the integration of existing methods rather than developing new AI models. A COMSOL Multiphysics (2D electromagnetic with 3D multibody dynamics) model of an induction machine simulated both healthy and imbalanced operating conditions with increasing stator phase-A current imbalance (parameter ε). The verified fault mechanism was incorporated into the model. From these simulations, a multisignal dataset was built using electromagnetic torque, rotor speed, electromagnetic force, and foundation force responses across 9 configurations, resulting in 54 samples (each with 100 features) classified into healthy, minor, and major imbalance groups. We tested Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms. Repeated stratified cross-validation showed Random Forest performed best, with an average accuracy of 92.3% (±4.1%) and macro-F1 of 0.764 (±0.146). A leave-one-configuration-out test, where no data from the same configuration appear in both training and testing, produced more conservative results: 46.3% accuracy and 0.317 macro-F1, with no healthy-condition samples correctly classified, because only one independent healthy configuration was available. SHAP analysis identified foundation-force spectral energy in the 50-150 Hz range as the most important predictor, suggesting imbalance severity at the configuration level. Since foundation-force features are fixed within each configuration, this indicator should be considered a configuration-level marker rather than an individual sample marker. Overall, the sample-level results are promising, indicating that the multi-signal, physics-based feature set and interpretability are useful. However, the configuration-level results suggest that the current 9-configuration simulation setup is not yet sufficient for definitive diagnostic accuracy.

Sara Sghiouri, H. Sabir, Mohamed Bezza et al. · 0 citations
Conference Jul 2026

Real-Time Fault Diagnosis of Induction Motors Based on Stator Current Analysis and Embedded Machine Learning on a Cost-Effective Testing Platform

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. · 0 citations