A physics-guided hybrid diagnostic architecture has been developed for assessing demagnetization in PMSM that provides a scalable, interpretable, and cost-effective solution for predictive maintenance and real-time fault diagnosis of PMSMs.
Abstract
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.
Accurate demagnetization-grade diagnosis of permanent magnet synchronous motors is difficult when operating conditions change and only a small number of independent fault records are available. This paper presents a condition-calibrated dual-modal workflow that combines complex Morlet continuous wavelet transform (CWT) order features of external radial stray flux with angular-domain current order-amplitude ratios. A healthy D0 record at the same operating point is used for calibration; feature selection, support vector classifier tuning, probability calibration, and fusion-weight selection are performed only within the outer training conditions. The dataset contains 54 simulated records with six demagnetization grades, three speeds, and three loads, and nested leave-one-speed-load-condition-out validation is used. The fusion model gives 96.30% accuracy and 96.37% macro-F1, compared with 83.33% and 82.00% for the Bronly model; grade MAE decreases from 0.389 to 0.037. The Br branch provides the stronger stand-alone basis, while current probabilities correct complementary boundary errors; their fusion improves cross-condition diagnosis.
Yuanxiang Meng, Shijie Sun, P. Su· 2026 5th International Confe...· 0 citations
Permanent magnet synchronous motors (PMSMs) are widely used in new energy vehicles, electric drive systems, and industrial servo applications. Excessive permanent magnet temperature may lead to magnetic performance degradation or even irreversible demagnetization; therefore, accurate estimation of permanent magnet temperature is of considerable importance. However, existing data-driven methods generally rely heavily on high-frequency measurements, and their prediction accuracy tends to deteriorate under low-frequency sampling conditions. Moreover, purely data-driven models lack explicit physical constraints, which limits their interpretability and generalization capability. To address these issues, this study proposes a physics-informed long short-term memory model for permanent magnet temperature prediction. A physics-based loss function is formulated using the PMSM d–q-axis voltage balance equations, while the d- and q-axis inductances are treated as trainable parameters during network optimization. This design enables the temperature prediction task and the electromagnetic constraints to be optimized jointly. Multi-operating-condition experiments are conducted using a publicly available electric motor temperature dataset, and the proposed model is compared with CNN, GRU, MLP-PINN and TNN models. In addition, experiments involving different downsampling ratios, errors in the high-temperature region, parameter sensitivity, physical parameter identification, and input-feature effects are performed to comprehensively evaluate the proposed model. The results show that the PINN-LSTM model achieves the best overall prediction performance, with an MAE of 1.6048 °C, an RMSE of 2.1890 °C, and an R2 of 0.9861, outperforming all comparison models. The model also maintains high prediction accuracy in the high-temperature region, with an MAE of 1.363 °C and an RMSE of 1.896 °C. Furthermore, the parameters learned by the model can effectively reconstruct the variation trends of the d- and q-axis voltages under the test operating conditions. Sensitivity analysis of the temperature coefficients further demonstrates that the model is robust to deviations in key physical parameters. These results indicate that the proposed method can achieve accurate and robust permanent magnet temperature prediction under low-frequency sampling conditions, providing an effective solution for motor thermal-state monitoring and health management.
Linxin Yu, Jianye Liang, Jing Ou et al.· Energies· 0 citations
Permanent magnet synchronous motors (PMSMs) are broadly used in diverse applications due to their inherent advantages. Open-circuit faults (OCFs) are among the major fault classifications in PMSMs, posing significant concerns due to their contribution to torque ripples, vibrations, and efficiency degradation. Therefore, accurate and real-time OCF diagnosis is essential for reliable operation and predictive maintenance practices. This underscores the importance of a robust diagnostic framework that enables early fault detection and localization, supports embedded integration, and requires no additional dedicated sensors. However, existing studies rarely address these requirements together. To overcome these limitations, this article proposes a novel OCF diagnostic framework that fuses features derived from multiple strategies, including wavelet energy-based features, frequency-domain features extracted from current waveforms, and speed measurement data. The extracted feature vector is used as input to a lightweight deep neural network. The proposed approach enhances interpretability and enables seamless embedded integration compared to conventional raw-data-driven machine learning models. In addition, an extended refinement layer is incorporated to enable integrated fault detection and classification for OCF while enhancing diagnostic transparency. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulations using the PLECS Blockset and further validated in real-time with an RTBox-based hardware-in-the-loop setup using a C2000 launchpad. Furthermore, experimental validation is conducted using a domain-adaptation strategy based on transfer learning. Performance evaluation confirms diagnostic accuracy exceeding 99% across varying operating conditions. The validation process achieves fault detection within 22% of a fundamental electrical cycle, with fault localization occurring within 40% of an average, demonstrating the robustness and adaptability of the proposed method. A sensitivity analysis of the proposed algorithm’s feature vector validates the effectiveness of high-frequency features. Furthermore, the risk distribution matrix provides insights supporting informed maintenance decisions.
Nimesh Jayasena, Battur Batkhishig, B. Nahid-Mobarakeh et al.· IEEE Open Journal of Industr...· 0 citations
Incipient stator interturn short-circuit faults in permanent magnet synchronous motors produce only weak changes in the terminal currents, which limits the sensitivity of conventional amplitude- and unbalance-based indicators. This paper proposes a phase-wise diagnostic framework that combines multiscale sample entropy (MSE), topological data analysis (TDA), and a Gaussian mixture model (GMM). For each three-period current window, ten scale-dependent sample-entropy components and two persistent-entropy components are concatenated into a 12-dimensional feature vector. A separate GMM is trained for each phase using healthy data only. The resulting likelihood-based health scores are used for fault detection and faulty-phase localization, while physically defined score boundaries calibrated from measured short-circuit-current groups are used for severity assessment. Experiments on a 1.5 kW, 8-pole, 12-slot PMSM demonstrate class-wise recalls of 96.50–100% and an overall accuracy of 97.50% under the investigated operating conditions. The results show that the combined temporal and topological representation can reveal weak current changes that are difficult to distinguish using conventional terminal-current indicators.
Zhao-Yu Mao, Jien Ma, Shangke Li et al.· Energies· 0 citations
Feature selection plays a critical role in designing efficient and interpretable condition monitoring frameworks for electrical drives. In this paper, a correlation analysis of statistical and spectral features is performed for Permanent Magnet Synchronous Motor (PMSM) fault detection in naval windlass systems. Using both simulated data from a MATLAB/Simulink model and real shipboard current signals acquired from five Nigerian Navy vessels over one-month monitoring periods, higher-order statistical moments (Mean, Variance, Standard Deviation, Skewness, Kurtosis) and the Fault Severity Index (FSI) were computed alongside Total Harmonic Distortion (THD). Pearson correlation coefficients were employed to quantify feature relationships under healthy and faulty operating modes, while scatter-plot clustering was used to visualize feature separability across six fault classes. The dataset comprised 2,880 observation windows per vessel (2 kHz sampling, 60-minute windows with 30-minute overlap), yielding a total analytical corpus of 14,400 windows from ship data and 12,000 high-resolution windows from simulation. Results demonstrated strong correlations between variance, standard deviation, and FSI (r ≥ 0.80–0.99), confirming their redundancy. Kurtosis and skewness exhibited weaker correlations with other first- and second-order features (r < 0.60) but showed a moderate inter-correlation of r = 0.88 with each other, indicating shared higher-order sensitivity. THD demonstrated weak correlations with all time-domain statistical moments (r < 0.65), confirming its role as an independent spectral indicator. All reported coefficients were statistically significant (p < 0.001, df = n − 2). The study highlights the potential for dimensionality reduction in PMSM diagnostic frameworks without compromising detection accuracy, with practical guidance for real-time embedded naval monitoring systems.
Ibrahim Muhammad, B. Akinloye· Mansoura Engineering Journal· 0 citations
An ensemble subspace k-nearest neighbour model for fault detection and classification of switch open-circuit and short-circuit faults and indicates that the proposed model provides an effective and computationally efficient solution for reliable fault diagnosis in electric vehicle motor drive systems.
Masadi Prashanth Kumar, Srikanth Velpula, Chidurala Saiprakash· Transactions of the Institut...· 0 citations
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