van Khang Huynh

• Nguyen, Thien Phuoc; Eid, Mahmoud Sayed Mahmoud; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2026). Physics-Guided Cycle-Consistency Training for Stator Winding Fault Diagnosis in Synchronous Generators without Experimental Fault Data. IEEE Transactions on Transportation Electrification. ISSN 2577-4212. doi: 10.1109/TTE.2026.3727756. Full text in Research Archive Show summary

  Stator inter-turn short circuit faults in shipboard synchronous generators (SGs) can rapidly escalate from minor insulation degradation into catastrophic failures, threatening the operational continuity and safety of electrified vessels. Therefore, early fault severity estimation of SGs is essential to prevent extensive downtime and ensure the resilience of shipboard power systems. However, the conventional data-driven approaches typically require labeled faulty data, which are rarely obtainable in practical maritime operations. To address this limitation, this article proposes a machine learning framework that enables early fault severity estimation without the need for experimental fault samples for training. The proposed method couples two neural networks through a physics-guided cycle-consistency (PGCC) training strategy. This approach facilitates both high-fidelity synthetic data generation and domain adaptation from analytical simulations to experimental measurements. The framework is validated on an in-house 7.5 kW test bench, demonstrating that the proposed method achieves high-accuracy prediction of unseen experimental fault severities. Ablation studies confirm the contribution of each network component and the PGCC training strategy. Furthermore, a comparative study demonstrates that the proposed method outperforms the tested domain adaptation and generative adversarial networks-based methods, particularly under limited training data.

• Zakharov, Viacheslav; Huynh, Khang; Huynh, Khang Van & Minav, Tatiana (2026). AI-Based Hydraulic and Electrical Fault Identification in Direct-Driven Hydraulic Systems. IEEE Access. 14, p. 4940–4952. doi: 10.1109/access.2026.3651282. Full text in Research Archive
• Islam, Anfaj; Ha, Duy Hung; Nguyen, Trung Thang; Huynh, Van Van & Huynh, Khang (2025). A Two-Stage Framework for Fault and Cyber Attack Detection in Line Current Differential Relays of Power Transmission Network. IEEE Access. 13, p. 195257–195271. doi: 10.1109/ACCESS.2025.3632972. Full text in Research Archive Show summary

  The increasing integration of digital platforms in power systems has enhanced protection capabilities but also introduced new vulnerabilities, particularly in critical components like Line Current Differential Relays (LCDRs). LCDRs offer fast, selective, and sensitive protection for high-voltage transmission lines, but their reliance on synchronized current measurements, communicated over potentially insecure networks, exposes them to cyber threats. Attackers can exploit this dependency through sophisticated attacks, namely False Data Injection Attacks (FDIA) and Time Synchronization Attacks (TSA), manipulating relay decisions and leading to unnecessary tripping, load shedding, and system instability. To address this challenge, this study proposes a two-stage detection and classification framework to distinguish between grid faults and cyber-attacks. The first stage employs an unsupervised Long Short-Term Memory autoencoder, trained solely on normal operational data, to detect anomalous deviations without requiring labeled attack data. Once an anomaly is detected, the second stage, which is based on a supervised Random Forest classifier, categorizes the event as either a physical fault or a specific type of cyberattack. The proposed framework is implemented and validated on the IEEE 14-bus system using the OPAL-RT HYPERSIM platform. Results demonstrate that the proposed framework can accurately detect and classify both faults and cyber-attacks while preserving the essential protective functions of the LCDR.

• 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.

• Nguyen, Thien Phuoc; Ha, Duy Hung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2025). Harmonic-Based Analytical Model for Detecting Stator-Winding Short Circuit and Eccentricity Faults in Wound-Field Synchronous Generators. IEEE Access. 13, p. 195539–195551. doi: 10.1109/ACCESS.2025.3633506. Full text in Research Archive Show summary

  Conventional analytical models for fault detection in salient-pole synchronous generators often ignore the influence of higher-order force harmonics, limiting their diagnosis accuracy. This paper proposes a harmonic-based analytical model, integrating the Maxwell stress tensor and airgap flux density, and accounting for both radial and tangential components of force under various fault conditions to investigate the impact of inter-turn short circuit (ITSC) and rotor eccentricity (ECC) faults on the force density and vibration characteristics in wound-field synchronous generators (WFSMs). The effects of ITSC and ECC faults are analyzed through spatiotemporal harmonic analysis, showing significant shifts in the harmonic spectrum with additional spatial harmonics at higher frequencies. The proposed analytical model is numerically verified through finite element analysis and experimentally validated on an in-house testbed of healthy and faulty WFSMs. The results prove that the proposed model effectively detects fault-related variations, particularly through higher spatial and temporal harmonic components whereas existing models primarily focus on the fundamental frequency components.

• Eid, Mahmoud Sayed Mahmoud; Ali, Ahmed Mohamed Salem; Huynh, Khang & Robbersmyr, Kjell Gunnar (2025). Robust Self-augmented Open-circuit Fault Diagnosis of Three-level Inverters for EV Powertrains. IEEE Transactions on Transportation Electrification. ISSN 2577-4212. 11(4), p. 10250–10261. doi: 10.1109/TTE.2025.3554526. Full text in Research Archive Show summary

  With the rapid advancement of data collection systems, data-driven fault diagnosis approaches have significantly improved the detection of open-circuit (OC) faults in voltage source inverters (VSIs). However, the diverse operating conditions and noisy environments in electric vehicles (EVs) cause feature variations, rendering challenges in maintaining the high accuracy and robustness of existing methods. This article introduces a robust self-augmented deep learning (DL) method for detecting OC faults in VSIs working at various operation conditions. The proposed method automatically extracts the most relevant statistical features from the measured signals using an automatic feature extraction module (AFEM) based on an improved particle swarm optimization (PSO) algorithm. Then, these optimized features are utilized through an attention-based multi-input convolution neural network (A-MICNN) embedded with a multihead self-attention module (SAM) to effectively capture the local and global characteristics of different OC faults. The A-MICNN architecture effectively processes these multi-inputs by employing shared weights and feature extraction layers to fuse information from statistical features and time-frequency image data. The proposed method is validated using an in-house three-level (3L) F-type inverter setup, achieving a remarkable diagnosis accuracy of 99.74% with high robustness across various speeds, load conditions, and noise levels, providing an efficient solution for EV powertrain fault diagnosis.

• 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, Thien Phuoc; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2025). Analytical Vibration Model of Stator Inter-Turn Short Circuit and Eccentricity in a Synchronous Generator at Different Loading Conditions, 2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis - WEMDCD. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331520748. doi: 10.1109/WEMDCD61816.2025.11014134. Full text in Research Archive Show summary

  Low-severity stator inter-turn short circuit (ITSC) and orbital eccentricity in synchronous generators (SMs) might not result in clear features or fault characteristics using vibration signals. Further, the effect of load profiles is unavoidable in fault detection of generators. This paper models the effect of the mentioned faults in a SM in various load profiles on the vibration signals on the SM stator surface. The impact of the stator ITSC and eccentricity on vibration harmonics are analyzed using the suggested analytic model based on Fourier expression of magnetic force density. Finally, the results from the suggested analytic model are experimentally validated on an in-house experimental setup.

• Laursen, Mikal; Huynh, Van-Van; Ha, Duy-Hung; Eid, Mahmoud Sayed Mahmoud & Huynh, van Khang (2025). Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions. IEEE Access. 13, p. 115217–115225. doi: 10.1109/ACCESS.2025.3585518. Full text in Research Archive Show summary

  Detection and isolation of multiple low-severity faults in permanent magnet synchronous machines (PMSMs) under dynamics and noisy conditions are very important to enhance the system’s reliability, lifetime, and service availability. This study investigates a robust fault classification scheme for PMSMs under low-severity, multiple-fault conditions in noisy and variable-speed scenarios. Two supervised learning models, namely Extra Trees (ET) and Support Vector Machine (SVM), are developed and compared using time-domain (TD) and frequency-domain (FD) features. A hybrid feature extraction method is adopted to balance the computational burden and classification accuracy. Hyperparameters and frequency-domain (FD) features are optimized based on prediction confidence, prediction times, and accuracy. Furthermore, the robustness of the data-driven models is evaluated at different Signal-to-Noise (SNR) ratios under mixed faults and variable speeds in an in-house PMSM, combining an inter-turn short circuit fault of 2.2% in one phase and a 30% demagnetization of one rotor magnet. A comparative study was conducted, confirming the proposed ET model’s superior performance in terms of prediction accuracy and robustness.

• Eid, Mahmoud Sayed Mahmoud; Ali, Ahmed Mohamed Salem; Huynh, Khang; Robbersmyr, Kjell Gunnar & Pomarnacki, Raimondas (2024). Highly Accurate Diagnosis Scheme of Open-Circuit Faults in a Three-Level Inverter Using Optimized Multi-Classifier Decision Fusion, 2023 24th International Middle East Power System Conference (MEPCON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350358469. doi: 10.1109/MEPCON58725.2023.10462369. Full text in Research Archive
• 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
• Eid, Mahmoud Sayed Mahmoud; Ali, Ahmed Mohamed Salem; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2024). A Few–Shot Open-Circuit Fault Diagnosis of F-Type Inverters Using CGAN-Based Vision Transformer. IEEE Journal of Emerging and Selected Topics in Power Electronics. ISSN 2168-6777. 13. doi: 10.1109/JESTPE.2024.3478378. Full text in Research Archive Show summary

  Multilevel inverters (MLIs) are widely adopted in various industries due to their distinctive features. However, they are susceptible to higher failure rates due to the increased number of components. Deep learning (DL) models are widely used for accurately diagnosing faults in inverters because they effectively extract features automatically. These models work on the hypothesis of the availability of a sufficient number of samples to train the diagnostic models. However, obtaining enough sample data in engineering practice is difficult, especially in fault cases. Therefore, this article proposes a fault diagnosis scheme combining a conditional generative adversarial network (CGAN) and a vision transformer (ViT) for diagnosing opencircuit (OC) faults with few fault samples (few shots). First, the measured signals are converted to time–frequency images. Afterward, CGAN generates new 2-D sample images with data distributions similar to real samples. Finally, the improved ViT uses the original and generated samples to learn and extract local and global features with a multihead self-attention mechanism and classify the faults. The proposed scheme is validated using an experimental setup of the F-type inverter, and the results show that the suggested scheme outperforms the other conventional DL methods with an accuracy of 98.46% using only six samples per class.

• Jankauskas, Mindaugas; Serackis, Artūras; Paulauskas, Nerijus; Pomarnacki, Raimondas & Huynh, Khang (2024). The Impact of the Weather Forecast Model on Improving AI-Based Power Generation Predictions through BiLSTM Networks. Electronics (Basel). 13(17). doi: 10.3390/electronics13173472. 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
• Asad, Bilal; Raja, Hadi Ashraf; Vaimann, Toomas; Kallaste, Ants; Pomarnacki, Raimondas & Huynh, Khang (2023). A Current Spectrum-Based Algorithm for Fault Detection of Electrical Machines Using Low-Power Data Acquisition Devices. Electronics (Basel). 12(7). doi: 10.3390/electronics12071746. Full text in Research Archive Show summary

  An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented.

• Suzdalenko, Alexander; Zakis, Janis; Huynh, Khang & Suskis, Pavels (2023). 9-Level Active Neutral Point Clamped Multilevel Converter with Cascaded H-Bridges Fed by Flying Capacitors and T-Type Interconnections. In IEEE, . (Eds.), IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350331820. doi: 10.1109/IECON51785.2023.10312062. Full text in Research Archive
• Eid, Mahmoud Sayed Mahmoud; Huynh, Khang; Senanayaka, Jagath Sri Lal; Robbersmyr, Kjell Gunnar & Panadero, Ruben (2023). A two-stage Fault Diagnosis Scheme of Partial Demagnetization Faults in PMSMs with Low Labelled Rates, 2023 IEEE International Electric Machines & Drives Conference (IEMDC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350398991. doi: 10.1109/IEMDC55163.2023.10238950. Full text in Research Archive
• Khan, Muhammad Amir; Asad, Bilal; Vaimann, Toomas; Kallaste, Ants; Pomarnacki, Raimondas & Huynh, Khang (2023). Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms. Machines. 11(10). doi: 10.3390/machines11100963. Full text in Research Archive Show summary

  The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.

• Suskis, Pavels; Zakis, Janis; Suzdalenko, Alexander; Huynh, Khang & Pomarnacki, Raimondas (2023). A Study on Electrolytic Capacitor Aging in Power Converters and Parameter Change Over the Lifespan, 2023 IEEE 10th Jubilee Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350311785. doi: 10.1109/AIEEE58915.2023.10135053. Full text in Research Archive Show summary

  Electrolytic capacitor is a very common element used in modern power electronics. Besides the undoubtable advantages as high capacity per volume and low price per capacity unit, they are relatively low reliability and limited lifetime. This paper describes the aging mechanisms, change of parameters over time and process of artificial ageing of electrolytic capacitors. The accelerated aging of these elements helps to discover the boundaries of ESR growth and degradation of capacitance in comparison to initial values, how changes the bode diagram of impedance over aging. The obtained data is useful for development of diagnostic algorithms for power converters, design considerations for high reliability designs and better understanding of lifetime estimation of electronic devices. The paper contains the data obtained from real tests made with electrolytic capacitors of small values as an example. Capacitor accelerated ageing is made using overtemperature and overvoltage, the process is alternated with tests of parameters to fully illustrate the processes expected in future designs.

• 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
• Jiya, Immanuel Ninma; Huynh, Khang; Gunawardena, Pasan; Kishor, Nand & Li, Yunwei Ryan (2023). Novel Isolated Multiport DC Converter with Natural Bipolar Symmetry for Renewable Energy Source Integration to DC Grids. IEEE Access. 11, p. 117729–117740. doi: 10.1109/ACCESS.2023.3326752. Full text in Research Archive Show summary

  Due to their greater reliability, efficiency, and resilience as compared to unipolar dc grid systems, bipolar dc grid systems are swiftly gaining popularity for the integration of renewable energy sources. However, development of multiport converters for bipolar microgrid systems is still progressing slowly in terms of reducing costs or improving power density and compact designs. This paper proposes a multiport isolated dc-dc converter with naturally symmetric bipolar outputs (MIBDC). With respect to the number of input ports, voltage gain, and output symmetry that the proposed converter naturally possesses, it outperforms its few competitors. Additionally, the proposed MIBDC significantly reduces component count and control complexity by employing a fixed transformer with only one primary and secondary winding for any number of inputs. The suggested converter’s performance in both open and closed loops is evaluated quantitatively in simulation and experimentally using OPAL-OP5700 RT’s hardware-in-the-loop (HIL) platform under various situations.

• Jiya, Immanuel Ninma; Gunawardena, Pasan; Huynh, Khang; Kishor, Nand & Li, Yunwei (2023). Multiport DC-DC Converter for Integrating Energy Systems in All-Electric Vehicles, 2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350346893. doi: 10.1109/ESARS-ITEC57127.2023.10114856. Full text in Research Archive
• Salata, Gianluca; Cenkeramaddi, Linga Reddy; Huynh, Khang; Robbersmyr, Kjell Gunnar & Jha, Ajit (2023). Time-Frequency Analysis and Fault Prediction of Motor Bearings Using Millimeter-Wave Radar. IEEE Sensors Journal. ISSN 1530-437X. 23(16), p. 18718–18728. doi: 10.1109/JSEN.2023.3292899. Full text in Research Archive Show summary

  Industrial motors are ubiquitous and used extensively in various industrial applications. The aim of increasing productivity, efficiency, lowering down, and mean time between failures while maintaining safety necessitates the continuous condition monitoring of abnormalities, faults in motors, and even predict them as early as possible to maintain their smooth functioning. In this article, we propose and experimentally demonstrate the use of millimeter-wave (mmW) radar for noncontact, nondestructive detection, characterization, and finally prediction of the probability of failure by interrogating its vibrating shaft at a given set of bearing conditions. Precisely, we obtain experimental raw 1-D time series data from mmW radar and transform them into time–frequency representations using wavelet transformation. Then, the features are extracted from the time–frequency representations, specifically the instantaneous velocity trajectory that distinguishes the vibrating shaft due to faulty bearing from healthy bearing. Furthermore, using maximum likelihood estimation (MLE), the velocity profile of the vibrating shaft follows Gaussian distribution from collected data at different motor shaft rotations per second (rps) (5, 15, and 25 Hz) with healthy and faulty bearings. While the mean of distribution for all the cases remains constant, the variance of latter increases with the increase in rps. Finally, the probabilistic approach and the model parameters are exploited to quantify the probability of motor failure in terms of complementary cumulative distribution function (CCDF). It is observed that the CCDF for the faulty case is greater than that of healthy case for all velocities and rps.

• Ibrahim, Mahmoud; Rassõlkin, Anton; Vaimann, Toomas; Kallaste, Ants; Zakis, Janis & Huynh, Khang [Show all 7 contributors for this article] (2023). Digital Twin as a Virtual Sensor for Wind Turbine Applications. Energies. 16(17). doi: 10.3390/en16176246. Full text in Research Archive Show summary

  Digital twins (DTs) have been implemented in various applications, including wind turbine generators (WTGs). They are used to create virtual replicas of physical turbines, which can be used to monitor and optimize their performance. By simulating the behavior of physical turbines in real time, DTs enable operators to predict potential failures and optimize maintenance schedules, resulting in increased reliability, safety, and efficiency. WTGs rely on accurate wind speed measurements for safe and efficient operation. However, physical wind speed sensors are prone to inaccuracies and failures due to environmental factors or inherent issues, resulting in partial or missing measurements that can affect the turbine’s performance. This paper proposes a DT-based sensing methodology to overcome these limitations by augmenting the physical sensor platform with virtual sensor arrays. A test bench of a direct drive WTG based on a permanent magnet synchronous generator (PMSG) was prepared, and its mathematical model was derived. MATLAB/Simulink was used to develop the WTG virtual model based on its mathematical model. A data acquisition system (DAS) equipped with an ActiveX server was used to facilitate real-time data exchange between the virtual and physical models. The virtual sensor was then validated and tuned using real sensory data from the physical turbine model. The results from the developed DT model showed the power of the DT as a virtual sensor in estimating wind speed according to the generated power.

• Jiya, Immanuel Ninma; Ali, Ahmed Mohamed Salem; Huynh, Khang & Pomarnacki, Raimondas (2023). Integrated Multiport DC-DC and Multilevel Converters for Multiple Renewable Energy Source Integration. IEEE Access. 11, p. 132483–132495. doi: 10.1109/ACCESS.2023.3336567. Full text in Research Archive Show summary

  This research introduces an innovative converter system which has been developed to seamlessly incorporate multiple sources of renewable energy into both DC and AC grids. The integration of multiple energy sources is made possible through the utilization of a unique converter system that combines a multiport DC converter topology with a multilevel inverter topology, while maintaining a low component count and hence increased overall power density and efficiency for the proposed converter system. Unlike previous literature, where each energy source needs a separate DC converter and a two-level converter is used for DC to AC conversion, this converter system features buck-boost operation, superior power quality characteristics, and fewer components. The system suggests the implementation of a DC link voltage balancing technique that relies on an auxiliary circuit to achieve equilibrium in the voltage on the DC link, as a possible substitute to the more complicated control-based balancing scheme. The operational integrity of the converter system is assessed through rigorous numerical simulations and experimentally validated using OPAL-RT’s OP5700 hardware-in-the-loop (HIL) implementation platform as well as an in-house experimental test setup.

• Eid, Mahmoud Sayed Mahmoud; Huynh, Khang; Senanayaka, Jagath Sri Lal & Robbersmyr, Kjell Gunnar (2023). Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). 4, p. 762–772. doi: 10.1109/OJIES.2024.3350443. Full text in Research Archive Show summary

  With the rapid development of industrial applications using permanent magnet synchronous motors (PMSMs) and the Internet of Things, the demand for using robust fault diagnosis methods working in noisy conditions has increased significantly. The current data-driven methods depend mainly on deep learning (DL) models due to the effectiveness of automated feature extraction. However, these models have shallow depths compared with benchmark convolution neural networks, limiting their accuracy in final predictions, and they are established based on the hypothesis that the measured data are noiseless. Despite this, electric machinery is subjected to various noise sources that interfere with measurements during operation. This article proposes a new scheme combining a transfer-learned pretrained residual neural network (ResNet) and supervised machine learning (S-ML) to enhance the performance of DL models in noisy industrial environments. The effectiveness of the proposed scheme is validated using an in-house setup of a PMSM drive with demagnetization and intern-short circuit faults at variant operating conditions. The results show that the proposed method significantly reduced the computational burden by tenfold on average while improving the average accuracy to 96.84% across all the datasets compared with other DL and S-ML methods, with high robustness in noisy working conditions.

• Suskis, Pavels; Zakis, Janis; Suzdalenko, Alexander; Huynh, Khang & Pomarnacki, Raimondas (2023). Diagnostics of Pre-Fault Conditions Using The Impact of Electrolytic Capacitor Aging on Power Supply Dynamics, 2023 IEEE 10th Jubilee Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350311785. doi: 10.1109/AIEEE58915.2023.10135051. Full text in Research Archive Show summary

  The modern power electronics is indistinctive to questions on a reliability. The electrolytic capacitors despites their excessive properties in terms of affordability, power density to volume and weight, remains an element that can cause device fail. The paper is dedicated to a solution to diagnostics, monitoring and prediction of remain lifetime of electrolytic capacitors. The main idea is that ESR (Equivalent Series Resistance) of capacitor grows while capacitor is getting to the end of the lifespan. ESR in combination with the capacitance is forming a Left Half-Plane zero and thus change in listed parameters is affecting overall transfer function. This paper uses buck converter as an example. The data for capacitors models is obtained from real accelerated aging experimental results. Modelling at different lifespan periods shows change in response to disturbance. The paper proposes to use band-pass filter to get signal that is easy to process further. The paper also has a little discussion on potential uses of the signal and their advantages and disadvantages.

• Jankauskas, Mindaugas; Serackis, Artūras; Šapurov, Martynas; Pomarnacki, Raimondas; Baskys, Algirdas & Huynh, Khang [Show all 8 contributors for this article] (2023). Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique. Sensors. 23(12). doi: 10.3390/s23125695. Full text in Research Archive Show summary

  : The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection.

• Jiya, Immanuel Ninma; Ali, Ahmed Mohamed Salem & Huynh, Khang (2022). Integrated Multiport DC-DC and Multilevel Converters for Energy Sources, 2022 IEEE Industry Applications Society Annual Meeting (IAS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665478151. doi: 10.1109/IAS54023.2022.9939764. Full text in Research Archive Show summary

  This paper presents a novel converter system for integrating multiple renewable energy sources for both dc and ac grids. The proposed converter system is formed by integrating a novel multiport dc converter topology with a multilevel inverter topology, aiming to achieve multiple source integration with low component count and higher efficiency on the multiport converter section and efficient dc to ac conversion on the multilevel inverter section. As compared to counterparts in literature, where each energy source requires its own dc converter and the dc to ac conversion is achieved using a two-level converter, the converter system proposed in this paper has more attractive features of buck-boost operation, better power quality characteristics and low part counts. Within the framework, an auxiliary circuit-based dc link voltage balancing technique is proposed to balance the voltage on the dc link as compared to the more complex control-based balancing scheme. Open and closed loop operations of the converter system are numerically verified using simulations and validated by a high-fidelity hardware-in-the-loop implementation platform.

• Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2022). RMS Based Health Indicators for Remaining Useful Lifetime Estimation of Bearings. MIC Journal: Modeling, Identification and Control. ISSN 0332-7353. 43(1), p. 21–38. doi: 10.4173/mic.2022.1.3. Full text in Research Archive Show summary

  Estimating the remaining useful life (RUL) of bearings from healthy to faulty is important for predictive maintenance. The bearing fault severity can be estimated based on the energy or root mean square (RMS) of vibration signals, and a stopping criterion can be set based on a threshold given by an ISO standard. However, the vibration RMS is often not monotonically increasing with damage, which renders a challenge for predicting the RUL. This study proposes a novel method for splitting the vibration signal into multiple frequency bands before RMS calculations to generate multiple health indicators. Monotonic health indicators are identified using the Spearman coefficient, and the RUL is afterward estimated for each indicator using a suitable model and parameter update scheme. Historical failure data is not required to set any parameters. The proposed method is tested with the Paris' law, where parameters are updated by particle filters. Experimental results from two test rigs validate the performance of the proposed method.

• Jiya, Immanuel Ninma; Huynh, Van Khang; Kishor, Nand & Ciric, Rade (2022). Novel High Gain Multiport Isolated DC-DC Converter with Bipolar Symmetric Outputs, IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665480253. doi: 10.1109/IECON49645.2022.9968834. Full text in Research Archive
• Eid, Mahmoud Sayed Mahmoud; Huynh, Van Khang; Senanayaka, Jagath Sri Lal & Panadero, Ruben (2022). Fault Diagnosis of Low-severity Demagnetization in Permanent Magnet Synchronous Motors Using Numerical Data, 978-1-7281-6117-4. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728161174. doi: 10.1109/ICEMS56177.2022.9983411. Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Huynh, Van Khang; Rassõlkin, Anton; Vaimann, Toomas; Zakis, Jānis & Pomarnacki, Raimondas (2022). Fault-Tolerant Control of a Grid-connected Bipolar DC Microgrid with High Penetration of Intermittent Renewable Energy, IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665480253. doi: 10.1109/IECON49645.2022.9968572. Full text in Research Archive
• Attestog, Sveinung; Senanayaka, Jagath Sri Lal; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2022). Robust Active Learning Multiple Fault Diagnosis of PMSM Drives with Sensorless Control under Dynamic Operations and Imbalanced Datasets. IEEE Transactions on Industrial Informatics. ISSN 1551-3203. 19(9), p. 9291–9301. doi: 10.1109/TII.2022.3227628. Full text in Research Archive Show summary

  This paper proposes an active learning scheme to detect multiple faults in permanent magnet synchronous motors in dynamic operations without using historical labelled faulty training data. The proposed method combines the self-supervised anomaly detector based on a local outlier factor (LOF) and a deep Q-network (DQN) supervised reinforcement learner to classify interturn short-circuit, local demagnetisation and mixed faults. The first fault, which is detected by LOF and verified by an expert during maintenance, is used as training data for the DQN classifier. From that point onward, the LOF anomaly detector and DQN fault classifiers are working in tandem in the identification of new faults, which require expert intervention when either of them identifies a fault. The robustness of the scheme against dynamic operations, mixed fault and imbalanced training datasets is validated via a comparative study using stray flux data from an inhouse test setup.

• Raja, Hadi; Kudelina, Karolina; Asad, Bilal; Vaimann, Toomas; Kallaste, Ants & Rassõlkin, Anton [Show all 7 contributors for this article] (2022). Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines. Energies. 15(24). doi: 10.3390/en15249507. Full text in Research Archive Show summary

  Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.

• Maskeliūnas, Rytis; Pomarnacki, Raimondas; Huynh, Van Khang; Damaševičius, Robertas & Plonis, Darius (2022). Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network. Remote Sensing. 15(1). doi: 10.3390/rs15010194. Full text in Research Archive Show summary

  To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for analyzing and monitoring power line integrity. The results of experiments performed over 32 km long power line under different scenarios are presented. The proposed framework may be useful for monitoring traditional power lines as well as alternative energy source parks and large users like industries. We discovered that the quantity of data transferred changes based on the problem and the size of the planned data packet. When all phases were absent from all meters, we noted a significant decrease in the amount of data collected from the power line of interest. This implies that there is a power outage during the monitoring. When even one phase is reconnected, we only obtain a portion of the information and a solution to interpret this was necessary. Our Q-network was able to identify and classify simulated 190 entire power outages and 700 single phase outages. The mean square error (MSE) did not exceed 0.10% of the total number of instances, and the MSE of the smart meters for a complete disturbance was only 0.20%, resulting in an average number of conceivable cases of errors and disturbances of 0.12% for the whole operation.

• Attestog, Sveinung; Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2022). Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors. Sensors. 22(3). doi: 10.3390/s22031216. Full text in Research Archive Show summary

  This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor through an optimisation problem. This eliminates the need for the position sensors, allowing for the fault classification of sensorless PMSM drives using only two external stray flux sensors alone. Both SVM and FNN algorithms could identify a single fault of the magnet defect with an accuracy higher than 95% in transient states. For mixed faults, the FNN-based algorithm could identify ITSC in parallel-strands stator winding and local partial demagnetisation with an accuracy of 87.1%.

• Suskis, Pavels; Zakis, Janis; Suzdalenko, Alexander; Huynh, Van Khang; Rassolkin, Anton & Vaimann, Toomas [Show all 7 contributors for this article] (2022). Converter State-Space Model Estimation Using Dynamic Mode Decomposition, 2022 IEEE 7th International Energy Conference (ENERGYCON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665479820. doi: 10.1109/ENERGYCON53164.2022.9830201. Full text in Research Archive Show summary

  Power electronic reliability is an important topic for modern industrial electronics. Each component of a power converter has its lifespan, which is affected by many factors like ambient temperature, humidity, load, and thermal cycles of the system. This study focuses on modeling and identifying a state-space model of a power converter under the degradation of passive components, namely the capacitor and inductor. A state-space model of a buck converter is estimated within the framework by dynamic mode decomposition. The algorithm requires 10 to 16 long historical samples of the state variables and the control signal. The numerical results prove that the suggested algorithm can track the system change in time.

• Bastida-Molina, Paula; Jiya, Immanuel Ninma & Huynh, Van Khang (2022). Modelling a Hybrid Renewable Energy System for Recharging Electric Vehicles, 2022 IEEE 7th International Energy Conference (ENERGYCON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665479820. doi: 10.1109/ENERGYCON53164.2022.9830232. Full text in Research Archive Show summary

  Abstract—Cold countries with low irradiance values have typically used utility grid to support solar PV systems. However, the extended option is facing new challenges: the electricity tariffs increase and the expected high penetration of EVs, which can increase the pressure on the grid. In this research, a prototype simulation model of a solar PV-batteries-grid HRES to cover recharge of EVs was developed. The system was simulated in MATLAB®, with a control algorithm based on power balance between EVs load demand-solar PV generation, and the state of charge (SOC) of the batteries. The results indicate the suitability of the simulation model. It can select the appropriate support system according to the control algorithm. Moreover, the two scenarios presented for the case study reflect the importance of the initial SOC, since it will directly affect the availability of batteries for covering electricity shortfalls.

• Ebrahimi, Saeed Hasan; Choux, Martin Marie Hubert & Huynh, Khang (2022). Real-Time Detection of Incipient Inter-Turn Short Circuit and Sensor Faults in Permanent Magnet Synchronous Motor Drives Based on Generalized Likelihood Ratio Test and Structural Analysis. Sensors. 22(9). doi: 10.3390/s22093407. Full text in Research Archive Show summary

  This paper presents a robust model-based technique to detect multiple faults in permanent magnet synchronous motors (PMSMs), namely inter-turn short circuit (ITSC) and encoder faults. The proposed model is based on a structural analysis, which uses the dynamic mathematical model of a PMSM in an abc frame to evaluate the system’s structural model in matrix form. The just-determined and over-determined parts of the system are separated by a Dulmage–Mendelsohn decomposition tool. Subsequently, the analytical redundant relations obtained using the over-determined part of the system are used to form smaller redundant testable sub-models based on the number of defined fault terms. Furthermore, four structured residuals are designed based on the acquired redundant sub-models to detect measurement faults in the encoder and ITSC faults, which are applied in different levels of each phase winding. The effectiveness of the proposed detection method is validated by an in-house test setup of an inverter-fed PMSM, where ITSC and encoder faults are applied to the system in different time intervals using controllable relays. Finally, a statistical detector, namely a generalized likelihood ratio test algorithm, is implemented in the decision-making diagnostic system resulting in the ability to detect ITSC faults as small as one single short-circuited turn out of 102, i.e., when less than 1% of the PMSM phase winding is short-circuited.

• Husebø, Arild Bergesen; Huynh, Khang; Robbersmyr, Kjell Gunnar & Klausen, Andreas (2022). Classification of Mechanical Fault-Excited Events Based on Frequency. In Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yayilgan, Sule Yildirim & Bajwa, Imran Sarwar (Ed.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 9783031105241. p. 380–392. doi: 10.1007/978-3-031-10525-8_30. Full text in Research Archive Show summary

  We propose a method for classifying periodic events generated at one or multiple frequencies on any one-dimensional space. This is very useful in problems where you need to find the type of event based on observations of location, e.g. in time. For each frequency, all events are mapped into periodic axes, which are represented independently of each other. Using an expectation-maximization algorithm, we can fit distributions to the events and classify them using maximum likelihood. The proposed method is applied to two mechanical faulty cases: a defect rolling-element bearing, and a gearbox with defect teeth. We show very good classification results in cases of multiple event types of similar frequency, multiple event types of different frequencies, and combinations of the two for artificially generated events.

• Ali, Ahmed Mohamed Salem; Huynh, Van Khang; Jiya, Immanuel Ninma & Robbersmyr, Kjell Gunnar (2022). Hybrid Three-Phase Transformer-Based Multilevel Inverter with Reduced Component Count. IEEE Access. 10, p. 47754–47763. doi: 10.1109/ACCESS.2022.3171849. Full text in Research Archive Show summary

  The topology of the static synchronous compensator of reactive power for a low-voltage three-phase utility grid capable of asymmetric reactive power compensation in grid phases has been proposed and analysed. It is implemented using separate, independent cascaded H-bridge multilevel inverters for each phase. Every inverter includes two H-bridge cascades. The first cascade operating at grid frequency is implemented using thyristors, and the second one—operating at high frequency is based on the high-speed MOSFET transistors. The investigation shows that the proposed compensator is able to compensate the reactive power in a low-voltage three-phase grid when phases are loaded by highly asymmetrical reactive loads and provides up to three times lower power losses in the compensator as compared with the situation when the compensator is based on the conventional three-level inverters implemented using IGBT transistors.

• Jiya, Immanuel Ninma; Huynh, Van Khang; Kishor, Nand & Ciric, Rade (2022). Novel Family of High-Gain Nonisolated Multiport Converters with Bipolar Symmetric Outputs for DC Microgrids. IEEE transactions on power electronics. ISSN 0885-8993. 37(10), p. 12151–12166. doi: 10.1109/TPEL.2022.3176688. Full text in Research Archive
• Šapurov, Martynas; Baskys, Algirdas; Pomarnacki, Raimondas; Serackis, Artūras; Jankauskas, Mindaugas & Huynh, Van Khang [Show all 12 contributors for this article] (2022). Cascaded Multilevel Inverter-Based Asymmetric Static Synchronous Compensator of Reactive Power. Symmetry. 14(3). doi: 10.3390/sym14030483. Full text in Research Archive Show summary

  The topology of the static synchronous compensator of reactive power for a low-voltage three-phase utility grid capable of asymmetric reactive power compensation in grid phases has been proposed and analysed. It is implemented using separate, independent cascaded H-bridge multilevel inverters for each phase. Every inverter includes two H-bridge cascades. The first cascade operating at grid frequency is implemented using thyristors, and the second one—operating at high frequency is based on the high-speed MOSFET transistors. The investigation shows that the proposed compensator is able to compensate the reactive power in a low-voltage three-phase grid when phases are loaded by highly asymmetrical reactive loads and provides up to three times lower power losses in the compensator as compared with the situation when the compensator is based on the conventional three-level inverters implemented using IGBT transistors.

• Salem, Ahmed; Huynh, Khang & Robbersmyr, Kjell Gunnar (2021). Four-level three-phase inverter with reduced component count for low and medium voltage applications. IEEE Access. 9, p. 35151–35163. doi: 10.1109/ACCESS.2021.3062110. Full text in Research Archive
• Husebø, Arild Bergesen; Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2021). A Simple Time Domain-Based Method for Estimating the Resonance Frequency of a Bearing, 2021 24th International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9788986510218. doi: 10.23919/ICEMS52562.2021.9634452. Full text in Research Archive
• Jiya, Immanuel Ninma; Ali, Ahmed Mohamed Salem; Huynh, Van Khang; Kishor, Nand & Ciric, Rade (2021). Novel Multisource DC-DC Converter for All-Electric Hybrid Energy Systems. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. 69(12), p. 12934–12945. doi: 10.1109/TIE.2021.3131871. Full text in Research Archive
• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2021). Field reconstruction for modeling multiple faults in permanent magnet synchronous motors in transient states. IEEE Access. 9, p. 127131–127140. doi: 10.1109/ACCESS.2021.3112224. Full text in Research Archive
• Kudelina, Karolina; Vaimann, Toomas; Rassolkin, Anton; Kallaste, Ants & Huynh, Khang (2021). Heat Pump Induction Motor Faults Caused by Soft Starter Topology — Case Study, International Power Electronics and Motion Control Conference (EPE-PEMC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728156606. p. 454–459. doi: 10.1109/PEMC48073.2021.9432506. Full text in Research Archive
• Suzdalenko, Alexander; Sribniya, Victoria; Zakis, Janis & Huynh, Khang (2021). Continuous Monitoring of Parasitic Elements in Boost Converter Circuit, 2021 IEEE Energy Conversion Congress and Exposition – Asia (ECCE-Asia 2021). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728163444. p. 1829–1832. doi: 10.1109/ECCE-Asia49820.2021.9479093. Full text in Research Archive
• Ebrahimi, Saeed Hasan; Choux, Martin Marie Hubert & Huynh, Khang (2021). Detection and Discrimination of Inter-Turn Short Circuit and Demagnetization Faults in PMSMs Based on Structural Analysis, 22nd IEEE International Conference on Industrial Technology (ICIT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728157306. p. 184–189. doi: 10.1109/ICIT46573.2021.9453557. Full text in Research Archive Show summary

  This paper presents a fault diagnosis method based on structural analysis of permanent magnet synchronous motors (PMSMs), focusing on detecting and discriminating two of the most common faults in PMSMs, namely demagnetization and inter-turn short circuit faults. The structural analysis technique uses the dynamic mathematical model of the PMSM in matrix form to evaluate the system’s structural model. After obtaining the analytical redundancy using the over-determined part of the system, it is divided into redundant testable sub-models. Four structured residuals are designed to detect and isolate the investigated faults, which are applied to the system in different time intervals. Finally, the proposed diagnostic approach is numerically verified through a simulation of an inverter-fed PMSM and white Gaussian noise are added to the measured signals from the motor to verify its diagnosis performances.

• Jiya, Immanuel Ninma; Huynh, Khang; Kishor, Nand & Ciric, Rade (2021). Four Quadrant Switch Based Multiple-Input DC-DC Converter, 2021 IEEE Energy Conversion Congress and Exposition – Asia (ECCE-Asia 2021). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728163444. p. 2199–2204. doi: 10.1109/ECCE-Asia49820.2021.9479432. Full text in Research Archive
• Jiya, Immanuel Ninma; Huynh, Van Khang; Ali, Ahmed Mohamed Salem; Kishor, Nand & Ciric, Rade (2021). Novel Isolated Multiple-Input Buck-Boost DC-DC Converter for Renewable Energy Sources, IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665435543. doi: 10.1109/IECON48115.2021.9589538. Full text in Research Archive
• Kudelina, Karolina; Asad, Bilal; Vaimann, Toomas; Rassõlkin, Anton; Kallaste, A & Huynh, Khang (2021). Methods of condition monitoring and fault detection for electrical machines. Energies. 14(22). doi: 10.3390/en14227459. Full text in Research Archive
• Ali, Ahmed Mohamed Salem; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2021). Novel Transformer-Based Six-Level Inverter with Low Component Count. In Bossche, Alex (Eds.), 2021 23rd European Conference on Power Electronics and Applications (EPE'21 ECCE Europe). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9789075815375. Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2021). CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features, IEEE 30th International Symposium on Industrial Electronics (ISIE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728156354. doi: 10.1109/ISIE45552.2021.9576257. Full text in Research Archive
• Suzdalenko, Alexander; Huynh, Khang; Zakis, Janis & Sribniya, Victoria (2021). Nine-Level Active Neutral Point Clamped Converter with Cascading H-Bridges Fed by Flying Capacitors, 2021 11th International Conference on Power and Energy Systems (ICPES 2021). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665413251. p. 42–46. doi: 10.1109/ICPES53652.2021.9683987. Full text in Research Archive
• Asad, Bilal; Vaimann, Toomas; Belahcen, Anouar; Kallaste, Ants; Rassolkin, Anton & Huynh, Khang [Show all 9 contributors for this article] (2021). The modeling and investigation of slot skews and supply imbalance on the development of principal slotting harmonics in squirrel cage induction machines. IEEE Access. 9, p. 165932–165946. doi: 10.1109/ACCESS.2021.3134331. Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Huynh, Khang; Vaimann, Toomas; Rassolkin, Anton; Zakis, Janis & Pomarnacki, Raimondas (2021). Fault Tolerant Control of a Grid-Connected Microgrid with High Penetration of Renewable Energy, 2021 24th International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9788986510218. doi: 10.23919/ICEMS52562.2021.9634364. Full text in Research Archive
• Ebrahimi, Saeed Hasan; Choux, Martin Marie Hubert & Huynh, Khang (2021). Diagnosis of Sensor Faults in PMSM and Drive System Based on Structural Analysis, 2021 IEEE International Conference on Mechatronics (ICM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728144436. doi: 10.1109/ICM46511.2021.9385663. Full text in Research Archive Show summary

  Virtual reality promises to be a tool that can improve higher education. Immersive virtual environments offer the chance to enrich courses with experiential learning experiences. The technological possibilities evolve rapidly and more and more researchers report on adopting virtual reality for learning – albeit such work often has a more or less experimental character. However, the base of knowledge on using virtual reality in higher education is growing; educators who want to employ virtual reality to amend courses, to extend the curriculum with experiential learning, or who want to offer new content enabled through virtual reality, find increasingly rich advice. With this article, we contribute to this advice by providing insights from three research cases. Although these were experimental, their embedding into a larger project enables us to propose recommendations for educators. The ultimate aim of our work is the routine use of virtual reality in higher education.

• Salem, Ahmed; Huynh, Khang; Robbersmyr, Kjell Gunnar; Norambuena, Margarita & Rodriguez, Jose (2020). Novel Three-Phase Multilevel Inverter with Reduced Components for Low- and High-Voltage Applications. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. doi: 10.1109/TIE.2020.2998752. Full text in Research Archive
• Golzar, Milad; Kandukuri, Surya Teja; Huynh, Khang; Choux, Martin Marie Hubert & Versland, Alf Magne (2020). A Novel Fault Indicator for Local Demagnetization in Fractional-Slot Permanent Magnet Synchronous Motor using Winding Function Theory. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). Full text in Research Archive Show summary

  Local demagnetization is an irreparable failure mode in permanent magnet synchronous motors (PMSMs), resulting in reduced motor efficiency and high cogging torque. These effects are particularly disadvantageous to fractional-slot wound PMSMs used in industrial applications, such as robotics and automations, that require high power density and precise operation. In this article, a diagnostic indicator is developed for detecting local demagnetization fault in fractional-slot wound PMSMs based on winding function theory, utilizing back-electromotive force (back-EMF) as a medium for fault detection. Further, the developed indicator is validated in a commercial fractional-slot wound PMSM of an in-house experimental setup.

• Hemmer, Martin; Klausen, Andreas; Huynh, Khang; Robbersmyr, Kjell Gunnar & Waag, Tor Inge (2020). Health Indicator for Low-speed Axial Bearings using Variational Autoencoders. IEEE Access. 8, p. 35842–35852. doi: 10.1109/ACCESS.2020.2974942. Full text in Research Archive Show summary

  This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10 -6 , and the method is most effective at lower shaft speeds.

• Husebø, Arild Bergesen; Kandukuri, Surya Teja; Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2020). Rapid Diagnosis of Induction Motor Electrical Faults using Convolutional Autoencoder Feature Extraction. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 5(1). Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2020). Towards Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains. IEEE Transactions on Industrial Informatics. ISSN 1551-3203. doi: 10.1109/TII.2020.3014422. Full text in Research Archive Show summary

  This paper proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labelled training data. The proposed method combines one-class support vector machine (SVM)-based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the online diagnosis scheme to learn features based on the latest data. The effectiveness of the scheme is validated via a comparison study using experimental data from an in-house test setup. Finally, the online implementation of the proposed scheme on the test setup is briefly introduced.

• Salem, Ahmed; Huynh, Khang; Robbersmyr, Kjell Gunnar; Norambuena, Margarita & Rodríguez, José (2020). Voltage Source Multilevel Inverters with Reduced Device Count: Topological Review and Novel Comparative Factors. IEEE transactions on power electronics. ISSN 0885-8993. 36(3), p. 2720–2747. doi: 10.1109/TPEL.2020.3011908. Full text in Research Archive Show summary

  Multilevel inverters (MLIs) have gained increasing interest for advanced energy-conversion systems due to their features of high-quality produced waveforms, modularity, transformerless operation, voltage, and current scalability, and fault-tolerant operation. However, these merits usually come with the cost of a high number of components. Over the past few years, proposing new MLIs with a lower component count has been one of the most active topics in power electronics. The first aim of this article is to update and summarize the recently developed multilevel topologies with a reduced component count, based on their advantages, disadvantages, construction, and specific applications. Within the framework, both single-phase and three-phase topologies with symmetrical and asymmetrical operations are taken into consideration via a detailed comparison in terms of the used component count and type. The second objective is to propose a comparative method with novel factors to take component ratings into account. The effectiveness of the proposed method is verified by a comparative study.

• Ebrahimi, Saeed Hasan; Choux, Martin Marie Hubert & Huynh, Khang (2020). Modeling Stator Winding Inter-Turn Short Circuit Faults in PMSMs including Cross Effects. In Liu, Yujing (Eds.), Proc. 2020 International Conference on Electrical Machines (ICEM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728199450. doi: 10.1109/ICEM49940.2020.9270890. Full text in Research Archive Show summary

  This paper presents a detailed analysis of stator winding inter-turn Short Circuit (ITSC) faults, taking the cross effects in the three phases of a permanent magnet synchronous motor (PMSM) into account by considering insulation degradation resistances. A PMSM with series coils in each phase winding is selected as a case study.The ITSC in one coil of each phase winding is modelled based on deformed fluxes or inductance variations caused by flux linkages, depending on the distribution of the coils in the same phase winding. Different fault ratios are investigated to evaluate different fault severity and scenarios. Therefore, three-phase faulty coils within three-phase winding analysis dynamics will constitute sixth-order assessments. The proposed faulty PMSM model is verified by a 2-D finite element analysis (FEA), showing show a good agreement between the proposed model and FEA.

• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2020). Improved Quadratic Time-frequency Distributions for Detecting Inter-turn Short Circuits of PMSMs in Transient States. In Liu, Yujing (Eds.), Proc. 2020 International Conference on Electrical Machines (ICEM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728199450. p. 1461–1467. doi: 10.1109/ICEM49940.2020.9271050. Full text in Research Archive
• Vaimann, Toomas; Rassolkin, Anton; Kallaste, Ants; Pomarnacki, Raimondas; Belahcen, Anouar & Huynh, Khang (2020). Artificial Intelligence in Monitoring and Diagnostics of Electrical Energy Conversion Systems, 2020 27th International Workshop on Electric Drives: MPEI Department of Electric Drives 90th Anniversary (IWED). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728141589. Full text in Research Archive
• Golzar, Milad; Huynh, Khang; Choux, Martin Marie Hubert & Versland, Alf Magne Midtbø (2019). Experimental Investigation of Efficiency Map for an Inverter-Fed Surface-Mount Permanent Magnet Synchronous Motor, 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728135878. doi: 10.1109/ICRERA47325.2019.8996549. Full text in Research Archive
• Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Multi-band identification for enhancing bearing fault detection in variable speed conditions. Mechanical systems and signal processing. ISSN 0888-3270. 139. doi: 10.1016/j.ymssp.2019.106422. Full text in Research Archive
• Hemmer, Martin; Klausen, Andreas; Huynh, Khang; Robbersmyr, Kjell Gunnar & Waag, Tor Inge (2019). Simulation-driven Deep Classification of Bearing Faults from Raw Vibration Data. International Journal of Prognostics and Health Management. 10(Special Issue on Deep Learning and Emerging Analytics). Full text in Research Archive Show summary

  The industry is moving towards maintenance strategies that consider component health, which require extensive collection and analysis of data. Condition monitoring methods that require manual feature extraction and analysis, become infeasible on an industrial scale. Machine learning algorithms can be used to automatically detect and classify faults, however, obtaining sufficient data for training is required for deep learning and other data-driven classification approaches. Data from healthy machine operation is generally available in abundance, while data from representative fault- and operating conditions is limited. This limits both development and deployment of deep learning-based CM systems on an industrial scale. This paper addresses both the challenges of automated analysis and lack of training data. A deep learning classifier architecture utilizing 1-dimensional dilated convolutions is proposed. Dilation of the convolution kernel allows for analysis of raw vibration signals while simultaneously maintaining the receptive field of the classifier enough to capture temporal patterns. The proposed method performs classification in time domain on signal segments of 1 second or shorter. With knowledge of the bearing specification, artificial vibration signals with similar characteristics as an actual bearing fault can be created. In this work, generated fault signals are combined with healthy operational data to obtain training data for a deep classifier. Parameters of the vibration model is chosen as distributions rather than fixed values. By using a range parameters in the vibration model, the classifier learns to recognize temporal features from the training data that generalize to unseen data. The effectiveness of the proposed method is demonstrated by training classifiers on generated data and testing on real signals from faulty bearings at both low and high speed. One dataset containing seeded faults and three run-to-failure tests are used for the demonstration.

• Husebø, Arild Bergesen; Huynh, Khang & Pawlus, Witold (2019). Diagnosis of Incipient Bearing Faults using Convolutional Neural Networks, 2019 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538681077. p. 143–149. doi: 10.1109/WEMDCD.2019.8887785. Full text in Research Archive
• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Detecting Eccentricity and Demagnetization Fault of Permanent Magnet Synchronous Generators in Transient State, 22nd International Conference on Electrical Machines and Systems (ICEMS) 2019. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728133980. doi: 10.1109/ICEMS.2019.8921753. Full text in Research Archive
• Ali, Ahmed Mohamed Salem; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). New Multilevel Inverter Topology with Reduced Component Count, EPE'19 ECCE Europe. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9789075815313. doi: 10.23919/EPE.2019.8915400. Full text in Research Archive
• Ali, Ahmed Mohamed Salem; Huynh, Khang; Robbersmyr, Kjell Gunnar; Norambuena, Margarita & Rodriguez, Jose (2019). Novel Three-Phase Multi-Level Inverter with Reduced Components, Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728148786. p. 6501–6506. doi: 10.1109/IECON.2019.8927732. Full text in Research Archive
• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Diagnostics of stator winding failures in wind turbine pitch motors using Vold-Kalman filter, Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728148786. p. 5992–5997. doi: 10.1109/IECON.2019.8926983. Full text in Research Archive Show summary

  Pitch systems are among the most failure-prone components in wind turbines. Winding failures in pitch motors are common due to high start-up loads and poor ventilation. This article presents a diagnostics scheme that can detect the stator winding failures in the pitch motors under time-varying speed and load conditions. The proposed approach based on three-phase motor currents can be directly integrated into the motor drive and can be used for induction as well as permanent magnet synchronous machines. The extended Park's vector calculated on the motor currents is order tracked based on the supply frequency from the drive using Vold-Kalman filter. The approach is shown to be robust under arbitrary load and speed variations in a laboratory setup of a pitch drive.

• Skylvik, Audun; Robbersmyr, Kjell Gunnar & Huynh, Khang (2019). Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network, 22nd International Conference on Electrical Machines and Systems (ICEMS) 2019. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728133980. doi: 10.1109/ICEMS.2019.8921738. Full text in Research Archive Show summary

  Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machine learning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density estimation. Further, several deep learning features are extracted using stacked autoencoder networks. The proposed scheme can predict the faults at high accuracy while reducing expertise demand. The proposed method is validated on an in-house laboratory setup under variable speed and load conditions, showing a classification accuracy over 95%. Furthermore, multiple support vector machine and k-nearest neighbor classifiers are tested with the same data for a comparative study. Finally, the performance of different fault classifiers are evaluated based on accuracy, computational burden and classification consistency.

• Kandukuri, Surya Teja; Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). A Two-Stage Fault Detection and Classification Scheme for Electrical Pitch Drives in Offshore Wind Farms Using Support Vector Machine. IEEE transactions on industry applications. ISSN 0093-9994. 55(5), p. 5109–5118. doi: 10.1109/TIA.2019.2924866. Full text in Research Archive Show summary

  Pitch systems are one of the components with the most frequent failure in wind turbines. This article presents a two-stage fault detection and classification scheme, for electric motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms, with electric pitch systems driven by induction motors as well as permanent magnet synchronous motors. The adopted strategy utilizes three-phase motor current sensing at the pitch drives for fault detection and only when a fault condition is detected at this stage, features extracted from the current signals are transmitted to a support vector machine classifier located centrally to the wind farm. The proposed method is validated in a laboratory setup of the pitch drive.

• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework, 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728118321. p. 264–270. doi: 10.1109/DEMPED.2019.8864889. Full text in Research Archive
• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Modelling Demagnetized Permanent Magnet Synchronous Generators using Permeance Network Model with Variable Flux Sources, 2019 22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728155920. doi: 10.1109/COMPUMAG45669.2019.9032791. Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults. IEEE Transactions on Industrial Informatics. ISSN 1551-3203. 15(8), p. 4569–4579. doi: 10.1109/TII.2018.2883357. Full text in Research Archive Show summary

  Detection and isolation of single and mixed-faults in a gearbox are very important to enhance the system reliability, lifetime, and service availability. This paper proposes a hybrid learning algorithm, consisting of Multilayer Perceptron (MLP)- and Convolutional Neural Network (CNN)-based classifiers, for diagnosis of gearbox mixed faults. Domain knowledge features are required to train the MLP classifier, while the CNN classifier can learn features itself, allowing to reduce the required knowledge features for the counterpart. Vibration data from an experimental setup with gearbox mixed faults is used to validate the effectiveness of the algorithms and compare them with conventional methods. The comparative study shows that accuracies and robustness of the individual MLP- and CNN-algorithms are better than those of the compared methods and can be significantly improved using data fusion at the feature level. Furthermore, the robustness of the algorithm is secured under noises by combining the results of individual classifiers.

• Hemmer, Martin; Huynh, Khang; Robbersmyr, Kjell Gunnar; Waag, Tor Inge & Meyer, Thomas (2018). Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network. Designs. doi: 10.3390/designs2040056. Full text in Research Archive Show summary

  Detecting bearing faults is very important in preventing non-scheduled shutdowns, catastrophic failures, and production losses. Localized faults on bearings are normally detected based on characteristic frequencies associated with faults in time and frequency spectra. However, missing such characteristic frequency harmonics in a spectrum does not guarantee that a bearing is healthy, or noise might produce harmonics at characteristic frequencies in the healthy case. Further, some defects on roller bearings could not produce characteristic frequencies. To avoid misclassification, bearing defects can be detected via machine learning algorithms, namely convolutional neural network (CNN), support vector machine (SVM), and sparse autoencoder-based SVM (SAE-SVM). Within this framework, three fault classifiers based on CNN, SVM, and SAE-SVM utilizing transfer learning are proposed. Transfer of knowledge is achieved by extracting features from a CNN pretrained on data from the imageNet database to classify faults in roller bearings. The effectiveness of the proposed method is investigated based on vibration and acoustic emission signal datasets from roller bearings with artificial damage. Finally, the accuracy and robustness of the fault classifiers are evaluated at different amounts of noise and training data.

• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors. In Kim, Chang-Eob (Eds.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9788986510201. p. 531–536. doi: 10.23919/ICEMS.2018.8549064. Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life. In Kim, Chang-Eob (Eds.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9788986510201. p. 537–542. doi: 10.23919/ICEMS.2018.8549006. Full text in Research Archive
• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning. In Kim, Chang-Eob (Eds.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9788986510201. p. 1002–1007. doi: 10.23919/ICEMS.2018.8549293. Full text in Research Archive
• Attestog, Sveinung & Huynh, Khang (2018). Electromagnetic and Thermal Modelling for Calculating Ageing Rate of Distribution Transformers. In Kim, Chang-Eob (Eds.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9788986510201. p. 1720–1725. doi: 10.23919/ICEMS.2018.8549517. Full text in Research Archive
• Huynh, Khang; Kandukuri, Surya Teja; Pawlus, Witold & Robbersmyr, Kjell Gunnar (2018). Parameter Identification of a Winding Function Based Model for Fault Detection of Induction Machines, 2018 Eighth International Conference on Information Science and Technology (ICIST). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538637821. p. 200–205. doi: 10.1109/ICIST.2018.8426188. Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Online Fault Diagnosis System for Electric Powertrains Using Advanced Signal Processing and Machine Learning, Proceedings 2018 XIII International Conference on Electrical Machines (ICEM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538624777. p. 1932–1938. doi: 10.1109/ICELMACH.2018.8507171. Full text in Research Archive

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• Eid, Mahmoud Sayed Mahmoud; Huynh, van Khang; Senanayaka, Jagath Sri Lal; Robbersmyr, Kjell Gunnar & Panadero, Ruben (2023). A two-stage Fault Diagnosis Scheme of Partial Demagnetization Faults in PMSMs with Low Labelled Rates. Full text in Research Archive
• 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
• Husebø, Arild Bergesen; Huynh, Khang; Robbersmyr, Kjell Gunnar & Klausen, Andreas (2021). Classification of Mechanical Fault-Excited Events based on Frequency. Full text in Research Archive
• Attestog, Sveinung; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2020). Improved Quadratic Time-Frequency Distributions for Fault Detection in Transient States. Full text in Research Archive Show summary

  This paper aims to improve quadratic time-frequency distributions to adapt condition monitoring of electrical machines in transient states. Short-Time Fourier transform (STFT) has been a baseline signal processing technique for detecting fault characteristic frequencies. However, limits of window sizes due to loss of frequency- or time-resolution, make it hard to capture rapid changes in frequencies. Within this study, Choi-Williams and Wigner-Ville distributions are proposed to effectively detect peaks at characteristic frequencies while still maintaining low computation time. The improved quadratic time-frequency distributions allow for generating spectrograms of a longer lasting data signal and capturing multi-component signals with a better separation of the components than STFT. Further, the time resolution of the spectrograms generated by the proposed method is not affected by the window size. The effectiveness of the proposed methods is numerically verified from the data of an in-house test setup.

• Attestog, Sveinung; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Modelling Demagnetized Permanent Magnet Synchronous Generator for Fault Diagnosis. Full text in Research Archive Show summary

  Prediction of the lifetime for transformers is very important for maintenance and asset management. Finite element analysis was performed on a 5 MVA distribution transformers with aluminium foil-type windings and voltage rating 6600 V/23000 V. Electromagnetic modelling is implemented on the full three-phase transformer to calculate distributed losses, taking the skin effect into account. To reduce the computational burden, the distributed losses in one phase are used to analyse temperature rise in one phase of the transformer. The temperature rise results were used to determine the ageing rate of the transformer. Further, the influence of ambient temperature and cooling on the temperature rise and ageing rate of the transformer is numerically investigated.

• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Electromagnetic and Vibration Modelling of an Electric Powertrain for a Simulation-driven Fault Diagnosis System. Full text in Research Archive
• Ebrahimi, Saeed Hasan; Choux, Martin Marie Hubert & Huynh, Khang (2023). Real-Time Fault Diagnosis of Permanent Magnet Synchronous Motor and Drive System. Universitetet i Agder. ISSN 9788284271293. Full text in Research Archive Show summary

  Permanent Magnet Synchronous Motors (PMSMs) have gained massive popularity in industrial applications such as electric vehicles, robotic systems, and offshore industries due to their merits of efficiency, power density, and controllability. PMSMs working in such applications are constantly exposed to electrical, thermal, and mechanical stresses, resulting in different faults such as electrical, mechanical, and magnetic faults. These faults may lead to efficiency reduction, excessive heat, and even catastrophic system breakdown if not diagnosed in time. Therefore, developing methods for real-time condition monitoring and detection of faults at early stages can substantially lower maintenance costs, downtime of the system, and productivity loss. In this dissertation, condition monitoring and detection of the three most common faults in PMSMs and drive systems, namely inter-turn short circuit, demagnetization, and sensor faults are studied. First, modeling and detection of inter-turn short circuit fault is investigated by proposing one FEM-based model, and one analytical model. In these two models, efforts are made to extract either fault indicators or adjustments for being used in combination with more complex detection methods. Subsequently, a systematic fault diagnosis of PMSM and drive system containing multiple faults based on structural analysis is presented. After implementing structural analysis and obtaining the redundant part of the PMSM and drive system, several sequential residuals are designed and implemented based on the fault terms that appear in each of the redundant sets to detect and isolate the studied faults which are applied at different time intervals. Finally, real-time detection of faults in PMSMs and drive systems by using a powerful statistical signal-processing detector such as generalized likelihood ratio test is investigated. By using generalized likelihood ratio test, a threshold was obtained based on choosing the probability of a false alarm and the probability of detection for each detector based on which decision was made to indicate the presence of the studied faults. To improve the detection and recovery delay time, a recursive cumulative GLRT with an adaptive threshold algorithm is implemented. As a result, a more processed fault indicator is achieved by this recursive algorithm that is compared to an arbitrary threshold, and a decision is made in real-time performance. The experimental results show that the statistical detector is able to efficiently detect all the unexpected faults in the presence of unknown noise and without experiencing any false alarm, proving the effectiveness of this diagnostic approach.

• Attestog, Sveinung; Robbersmyr, Kjell Gunnar & Huynh, Khang (2022). Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations. Universitetet i Agder. ISSN 9788284270951. Full text in Research Archive Show summary

  Permanent magnet synchronous motors (PMSMs) have played a key role in commercial and industrial applications, i.e. electric vehicles and wind turbines. They are popular due to their high efficiency, control simplification and large torque-to-size ratio although they are expensive. A fault will eventually occur in an operating PMSM, either by improper maintenance or wear from thermal and mechanical stresses. The most frequent PMSM faults are bearing faults, short-circuit and eccentricity. PMSM may also suffer from demagnetisation, which is unique in permanent magnet machines. Condition monitoring or fault diagnosis schemes are necessary for detecting and identifying these faults early in their incipient state, e.g. partial demagnetisation and inter-turn short circuit. Successful fault classification will ensure safe operations, speed up the maintenance process and decrease unexpected downtime and cost. The research in recent years is drawn towards fault analysis under dynamic operating conditions, i.e. variable load and speed. Most of these techniques have focused on the use of voltage, current and torque, while magnetic flux density in the air-gap or the proximity of the motor has not yet been fully capitalised. This dissertation focuses on two main research topics in modelling and diagnosis of faulty PMSM in dynamic operations. The first problem is to decrease the computational burden of modelling and analysis techniques. The first contributions are new and faster methods for computing the permeance network model and quadratic time-frequency distributions. Reducing their computational burden makes them more attractive in analysis or fault diagnosis. The second contribution is to expand the model description of a simpler model. This can be achieved through a field reconstruction model with a magnet library and a description of both magnet defects and inter-turn short circuits. The second research topic is to simplify the installation and complexity of fault diagnosis schemes in PMSM. The aim is to reduce required sensors of fault diagnosis schemes, regardless of operation profiles. Conventional methods often rely on either steady-state or predefined operation profiles, e.g. start-up. A fault diagnosis scheme robust to any speed changes is desirable since a fault can be detected regardless of operations. The final contribution is the implementation of reinforcement learning in an active learning scheme to address the imbalance dataset problem. Samples from a faulty PMSM are often initially unavailable and expensive to acquire. Reinforcement learning with a weighted reward function might balance the dataset to enhance the trained fault classifier’s performance.

• Ali, Ahmed Mohamed Salem; Huynh, Khang & Robbersmyr, Kjell Gunnar (2021). Multilevel Inverters with Reduced Component Count for Energy Systems. Universitetet i Agder. ISSN 9788284270456. Full text in Research Archive Show summary

  Multilevel inverters (MLIs) have gained increasing interest for advanced energy-conversion systems with a wide range of voltage levels, due to their attractive features of low harmonic contents, low dv/dt stress, low filtering requirements, low switching frequency, low electromagnetic interference (EMI), and employing low-rated semiconductor devices for producing high voltages. Further, some MLIs have a modularity feature, facilitating voltage and current scalability with high redundancy in switching states, allowing for fault-tolerant operations. The mentioned features are directly related to the number of generated voltage levels. However, enlarging level count renders challenges of requiring massive component counts, including DC sources, capacitors, power diodes, switches, inductors, and transformers. The high count of components negatively affects the size, cost, efficiency, lifespan, reliability, complexity of MLI-based energy conversion systems. Thus, proposing novel MLIs, which can enlarge the voltage level number with a low component count, is currently one of the most attractive topics in this research theme. In this dissertation, four three-phase topologies are proposed to mitigate the aforementioned shortcomings. Two transformerless topologies are proposed for low- and medium voltage applications, while two transformer-based topologies are intended for medium- and high-voltage applications. The proposed topologies have the key features of being capacitor-, diode-free, and low counts of DC sources, switches, and transformers. Further, three of them have a high modularity degree, allowing for higher voltage operations without increasing the voltage stress across the switches. The working principle of the proposed topologies are theoretically demonstrated, numerically verified, and experimentally validated through an in-house experimental setup. The effectiveness of the proposed topologies is proven through detailed comparative studies regarding component counts and voltage ratings. The proposed topologies are briefly described in this dissertation, while detailed explanations and results can be found in the appended papers.

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