Hoang Nam du Nguyen

• Nguyen, Hoang Nam Du; Huynh, Khang & Robbersmyr, Kjell Gunnar (2025). Domain Knowledge-Informed Few-Shot Fault Diagnosis of Multiple Low-Severity Faults in PMSGs Under Imbalanced Loads. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. p. 1–12. doi: 10.1109/TIE.2025.3629348. Full text in Research Archive Show summary

  Electrical experts can readily diagnose high-severity faults in permanent magnet synchronous generators (PMSGs) using a few samples of harmonic order data in the time-frequency domain, leveraging their prior knowledge of interturn short circuits, local demagnetization, and mixed faults. However, traditional harmonic analysis methods are often hindered by low signal amplitude, noise, and overlap with various operating conditions, which present significant challenges for intelligent fault detection and identification models. Moreover, the high similarity between healthy and faulty signatures, combined with the scarcity of faulty samples across diverse operating scenarios, leads to high misclassification rates in conventional FDI models. To address these challenges, this article proposes an explainable few-shot learning architecture that integrates symmetrical components and harmonic analysis to classify incipient faults using only a small number of samples under multiple operating conditions. A convolutional neural network–based attention mechanism is employed to extract physical features of PMSGs in both normal and abnormal states. The proposed model achieves 89% accuracy with 20 shots, outperforming the pretrained ResNet50 (43%) on a 4320 sample PMSG dataset. The precision, recall, and F1-score metrics are used for validating the predictions. Additionally, by incorporating physical domain knowledge, the model can generate interpretable predictions with visual explanations of its inference process, aligning with the transparency and accountability principles mandated by the EU AI Act for high-risk systems in the energy sector.

• Zhu, Mingda; Nguyen, Hoang Nam Du; Han, Peihua; Zhou, Jing & Huynh, van Khang (2025). A Semi-Supervised Variational Autoencoder for Fault Detection of Low-Severity Inter-Turn Short-Circuit in PMSMs, 2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis - WEMDCD. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331520748. doi: 10.1109/WEMDCD61816.2025.11014178. Full text in Research Archive Show summary

  Maintenance has been critical in energy and marine systems, ensuring safety, reliability and efficiency. Fault detection, a cornerstone of prognostics and health management systems, faces challenges due to limited faulty data. This study proposes a semi-supervised Variational Autoencoder (VAE) with Long Short-Term Memory Networks for fault detection. The method is demonstrated via an example of low-severity inter-turn short-circuit faults in permanent magnet synchronous machines. Trained only on healthy data, the model can generate anomaly scores based on the VAE's reconstruction error. The model is tested on time-series current data under varying conditions, achieving high detection accuracy. Comparative analysis shows its superiority over traditional methods such as One-Class Support Vector Machine, Isolation Forest and Autoencoder.

• Nguyen, Hoang Nam Du; Huynh, Khang & Robbersmyr, Kjell Gunnar (2025). Robust Multiple-Fault Diagnosis of PMSMs in Dynamic Operations under Imbalanced Datasets. IEEE Transactions on Transportation Electrification. ISSN 2577-4212. 11(5), p. 10714–10726. doi: 10.1109/TTE.2025.3566612. Full text in Research Archive Show summary

  Data-driven models for multiple-fault classifiers of permanent magnet synchronous machines (PMSMs) in electric power trains require historical data at faulty cases in a wide range of operations. However, data in healthy or steady-state operations are much more than the data in the faulty cases, resulting in imbalanced datasets and rendering challenges of expanding the robustness of the data-driven models in dynamic operations. To address these issues, this article proposes a robust two-stage learning scheme for detecting and classifying multiple incipient faults in PMSMs, namely, interturn short circuit (ITSC) local demagnetization (LD) and mixed faults (MFs), under dynamic operations. The first stage focuses on enhancing the effectiveness of anomaly detector in dealing with imbalanced datasets at low-severity faults by integrating prior domain knowledge into anomaly detectors and reducing false alarms using an extreme gradient boosting machine (XGBoost). In the second stage, a training algorithm based on a convolutional neural network (CNN) is introduced to classify multiple faults by studying the appearance of low-severity faults. The performance of the proposed anomaly detector is compared to conventional one-class models, including random forest, local outlier factor, and support vector machine. The accuracy and robustness of the suggested scheme are further evaluated by benchmarking pretrained classifiers such as MobileNetv2, ResNet50, and VGG16, using an imbalanced dataset at different operating conditions from an in-house test setup. A comparative study with a traditional CNN is conducted to explain how the model provides the prediction.

• Nguyen, Hoang Nam Du; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2024). Training Scheme for Convolutional Neural Network Based Multiple Fault Classifier of Permanent Magnet Synchronous Motors Under Variable Speed and Load Conditions. Proceedings (International Conference on Electrical Machines) (ICEM). ISSN 2381-4802. doi: 10.1109/ICEM60801.2024.10700238. Full text in Research Archive
• Nguyen, Hoang Nam Du; Huynh, Khang & Robbersmyr, Kjell Gunnar (2024). Classification of Multiple Incipient Faults in Permanent Magnet Synchronous Motors under Variable Speed Conditions Using Transfer Learning. In NN, NN (Eds.), 2024 Tenth International Conference on Communications and Electronics (ICCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350379792. p. 637–642. doi: 10.1109/ICCE62051.2024.10634627. Full text in Research Archive
• Nguyen, Nam Du Hoang; Huynh, Khang & Robbersmyr, Kjell Gunnar (2023). Current and Stray Flux Sensors for Anomaly Detection in PMSM Drive based on Gradient Boosting Machine. Proceedings of IEEE Sensors. ISSN 1930-0395. doi: 10.1109/SENSORS56945.2023.10325076. Full text in Research Archive
• Nguyen, Hoang Nam Du; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2023). Early detection of rolling bearing faults using long short-term memory, 2023 6th International Conference on Control, Robotics and Informatics. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350323283. p. 46–51. doi: 10.1109/ICCRI58865.2023.00016. Full text in Research Archive

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• Nguyen, Hoang Nam Du; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2023). Current and stray flux sensors for anomaly detection in PMSM drive based on gradient boosting machine. Full text in Research Archive
• Nguyen, Hoang Nam Du (2026). Domain Knowledge – Informed Low-Severity Fault Diagnosis for Permanent Magnet Synchronous Machines. Universitetet i Agder. ISSN 9788284273303. Full text in Research Archive Show summary

  Energy demand has increased significantly following the pandemic and the geopolitical instability worldwide. According to the IEA report, the growth of renewable sources reached 34.5% by the end of 2025. Although remarkable efforts are being made to develop and deploy renewable energy sources to meet the demand, they remain well short of the annual growth target of 8%. Moreover, to accelerate annual energy growth, permanent magnet synchronous machines (PMSMs) are central to the sustainable development of large-scale energy production, energy transition, electrification, autonomous vehicles, and the aerospace industry. PMSMs are now becoming the preferred choice for large -scale wind turbines due to their high efficiency, precise control, torque density, and ease of maintenance. Despite the rapid growth of diagnostic technologies enabled by artificial intelligence, PMSMs are subject to electrical faults and mechanical and thermal stresses under dynamic operating conditions. Additionally, the scarcity of historical fault data in practical applications limits the effectiveness of data-driven approaches, which typically require large amounts of labeled data. Therefore, robust fault detection and identification (FDI) based on data-driven approaches are not only key to deploying reliable, safe energy systems but also to reducing labor and maintenance costs. This dissertation addresses these challenges through three key contributions. First, it improves FDI performance under low-severity faults and dynamic operations. Second, a framework is developed to integrate domain knowledge with data-driven approaches to enable explainable decision making across different time scales in predictive maintenance. Third, an autonomous online fault diagnosis framework is proposed to provide traceable decision trajectories and physical reasoning during dynamic operations. The proposed methods are described theoretically and validated through experimental setups. The results demonstrate that the models achieve robustness and explainability for each prediction in fault diagnosis, making them well-suited for deployment in predictive maintenance strategies for PMSMs. A brief summary of the models and their findings is presented in this dissertation, while detailed explanations and results are provided in the appendices.

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