Jul 2026· IEEE International Conference on Circuits and Systems for Communications· pp. 1-10· 0 citations· 12 references
Abstract
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.
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.· Engineering Research Express· 0 citations
The study investigates real-time problem diagnosis of induction motors (IMs) with digital signal processing (DSP) to improve monitoring. IMs are essential to industrial applications but can fail owing to mechanical, electrical, and thermal stressors. These defects must be detected quickly to prevent motor failure and production downtime. DSP is used to create a sophisticated real-time online condition monitoring system to diagnose three-phase IM issues. The suggested system was validated using MATLAB calculations and experimental investigations on a 415 V, 1 HP, 50 Hz, 1440 rpm, 4-pole IM. Disruptions in the stator windings, such as inter-turn short circuits or inter-phase faults, as well as problems with the rotor, such as broken bars or end rings, are identified in this investigation. Keeping an eye on negative sequence currents and analyzing fault frequencies with a fast Fourier transform (FFT). According to the results of the testing, current approaches are not very good at detecting stator inter-turn difficulties under light-load and no-load conditions. Under varying loads, the proposed DSP-based system identified stator inter-turn, inter-phase, and broken rotor bar problems. Results showed that the DSP-based online condition monitoring system was more accurate and better at detecting faults than earlier methods, making it a good fit for usage in industrial applications.
Mohan P. Thakre, Badal Kumar, Supriya Nilesh Thakur et al.· Bulletin of Electrical Engin...· 0 citations
Unplanned failures of induction motors impose serious operational and financial penalties on industrial facilities, yet the fault signatures that precede such failures are detectable well in advance through careful sensor instrumentation and data-driven analysis. This paper presents an end-to-end Internet-of-Things (IoT) predictive maintenance scheme based on two off-the-shelf sensors: a DS18B20 one-wire digital thermometer and 2 piezo vibration sensor modules, with an ESP32 edge device for running the full machine learning pipeline offline, independent from any cloud services. Four operating scenarios are considered: healthy condition, BPFO (bearing outer race fault), misaligned shaft, and rotor imbalance. Based on 1-second sampling intervals, 17 descriptors are derived, including statistics in the time domain, Fourier harmonic peaks, energy ratios between different frequency bands, and temperature gradients measured across all sensors. A dual-stage feature selection method using mutual information (MI) score and Random Forest mean decrease impurity (MDI) ranking reduces the number of features to the 10 most relevant descriptors, reducing the computational complexity by 41% at the expense of 5.6% F1-macro. On a balanced 600-sample synthetic dataset, the resulting Random Forest classifier attains 87.3% hold-out accuracy, 91.0±1.9% five-fold cross-validation accuracy, and a macro area-under-the-ROC-curve of 0.980. End-to-end inference takes just 39 ms on the ESP32, easily meeting the 200 ms requirement for real-time alerting.
Akash Mastud, Dhiraj Vaidya, Azaroddin Sayyed et al.· International Conference on...· 0 citations
Permanent magnet synchronous motors (PMSMs) are widely utilized in electric vehicle and industrial drive applications due to their exceptional efficiency and power density. Nevertheless, the irreversible demagnetization of permanent magnets poses a significant degradation mechanism that negatively impacts torque capability, efficiency, and long-term reliability. A physics-guided hybrid diagnostic architecture has been developed for assessing demagnetization in PMSM. This architecture integrates statistical process control (SPC), Isolation Forest for anomaly validation, and K-Nearest Neighbors (KNNs) for severity estimation, thereby forming a unified sequential monitoring framework. Instead of depending solely on an isolated algorithm, this proposed methodology establishes a structured, hierarchical diagnostic pipeline. This pipeline systematically combines initial statistical screening with subsequent unsupervised anomaly confirmation, ultimately leading to a quantitative degradation assessment, all designed for continuous, real-time monitoring of motor health. Integrated multi-sensor data, encompassing temperature, magnetic flux density, stator currents, and rotor speed, forms the basis for facilitating non-invasive, real-time health monitoring. The analytical process involves three distinct methods, which are executed in a strict sequential pipeline. Initially, SPC is applied to continuously monitor magnetic flux density using both Shewhart and Exponentially Weighted Moving Average control charts. This step identifies observations that exceed the three-sigma control limits, classifying them as statistically deviant. These flagged observations, along with all incoming multi-feature vectors, are subsequently transferred to the Isolation Forest algorithm. This algorithm assigns an anomaly score to each sample by measuring the mean path length necessary to isolate it within an ensemble of randomized trees. Samples with scores above a predefined contamination threshold are then designated as anomalies. Finally, only these labeled anomalous samples are directed to the KNN regression model. The KNN model retrieves the k most similar historical degradation records in the feature space, utilizing Euclidean distance, and then calculates the predicted demagnetization percentage as the average of their respective target values. Validation of the approach was performed on an experimental 1 kW PMSM setup exposed to varying thermal and electrical loading in order to simulate sensor-based degradation markers associated with the early stages of demagnetization-related degradation. The proposed framework was validated using experimentally acquired multi-sensor degradation indicators rather than direct measurements of irreversible permanent magnet remanence. The ability to consistently identify the degradation pattern and agree with the physics-guided demagnetization indicators computed using multi-sensor data is evident from this result. Severity estimation is based on experimentally obtained degradation indicators compared with other indicators, not on the actual magnet degradation levels themselves. The proposed framework provides a scalable, interpretable, and cost-effective solution for predictive maintenance and real-time fault diagnosis of PMSMs.
Jayant N Ramesh, Alex V, Sureshkumar P et al.· Engineering Research Express· 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
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