Jagath Sri Lal Senanayaka

• 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 conference proceedings. ISSN 979-8-3503-9899-1. doi: 10.1109/IEMDC55163.2023.10238950.
• 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). ISSN 2644-1284. 4, s. 762–772. doi: 10.1109/OJIES.2024.3350443. Fulltekst i vitenarkiv
• 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), s. 9291–9301. doi: 10.1109/TII.2022.3227628. Fulltekst i vitenarkiv
• 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 978-1-6654-8025-3. doi: 10.1109/IECON49645.2022.9968572.
• 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 conference proceedings. ISSN 978-1-7281-6117-4. doi: 10.1109/ICEMS56177.2022.9983411.
• 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. ISSN 1424-8220. 22(3). doi: 10.3390/s22031216. Fulltekst i vitenarkiv Vis sammendrag

  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%.

• 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 conference proceedings. ISSN 978-8-9865-1021-8. doi: 10.23919/ICEMS52562.2021.9634364.
• 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 conference proceedings. ISSN 978-1-7281-5635-4. doi: 10.1109/ISIE45552.2021.9576257.
• 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. Vis sammendrag

  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.

• 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), s. 5109–5118. doi: 10.1109/TIA.2019.2924866. Vis sammendrag

  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.

• 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. I Kim, Chang-Eob (Red.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-89-86510-20-1. s. 531–536. doi: 10.23919/ICEMS.2018.8549064.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life. I Kim, Chang-Eob (Red.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-89-86510-20-1. s. 537 –542. doi: 10.23919/ICEMS.2018.8549006.
• 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), s. 4569–4579. doi: 10.1109/TII.2018.2883357. Vis sammendrag

  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.

• 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 978-1-5386-2477-7. s. 1932–1938. doi: 10.1109/ICELMACH.2018.8507171.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks, Proceedings 2018 XIII International Conference on Electrical Machines (ICEM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-2477-7. s. 1900 –1905. doi: 10.1109/ICELMACH.2018.8507096.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Fault detection and classification of permanent magnet synchronous motor in variable load and speed conditions using order tracking and machine learning. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 1037(3). doi: 10.1088/1742-6596/1037/3/032028.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). A robust method for detection and classification of permanent magnet synchronous motor faults: Deep autoencoders and data fusion approach. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 1037, s. 1–10. doi: 10.1088/1742-6596/1037/3/032029.
• Senanayaka, Jagath Sri Lal; Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar (2017). A novel soft-stall power control for a small wind turbine, IEEE 26th International Symposium on Industrial Electronics (ISIE 2017), 19-21 June 2017. IEEE conference proceedings. ISSN 978-1-5090-1412-5. doi: 10.1109/ISIE.2017.8001372.
• Kandukuri, Surya Teja; Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2017). A two-stage fault detection and classification for electric pitch drives in offshore wind farms using support vector machine, 2017 20th International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-1-5386-3246-8. doi: 10.1109/ICEMS.2017.8056262.
• Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2017). Towards online bearing fault detection using envelope analysis of vibration signal and decision tree classification algorithm, 2017 20th International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-1-5386-3246-8. doi: 10.1109/ICEMS.2017.8056146.
• Kandukuri, Surya Teja; Senanayaka, Jagath Sri Lal; Huynh, Khang; Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar (2017). Current signature based fault diagnosis of field-oriented and direct torque–controlled induction motor drives. Proceedings of the Institution of mechanical engineers. Part I, journal of systems and control engineering. ISSN 0959-6518. 231(10), s. 849–866. doi: 10.1177/0959651817731259. Vis sammendrag

  In this article, the operation of three-phase squirrel-cage induction motors is analysed under faulty conditions in closed loop with state-of-the-art controllers, namely, the field-oriented control and the direct torque control. The motivation behind this study is to examine the effectiveness of current signature–based fault detection schemes under closed-loop operation, in the presence of inverter harmonics. Various commonly occurring induction motor fault conditions are modelled based on the modified winding function theory, and each fault case is further simulated in a closed-loop framework to verify the fault detectability. The effectiveness of current signature–based diagnostics in varying fault severity, loads and speeds is studied. Furthermore, the faults are artificially seeded in a laboratory test set-up of an induction motor, and the effectiveness of current signature analysis is verified with commercially available field-oriented and direct torque control drives in the closed-loop framework

• Senanayaka, Jagath Sri Lal; Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2017). Early detection and classification of bearing faults using support vector machine algorithm. I Gerada, Chris (Red.), Proceedings- 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). IEEE conference proceedings. ISSN 978-1-5090-5853-2. s. 250–255. doi: 10.1109/WEMDCD.2017.7947755.
• Huynh, Khang; Puche-Panadero, Ruben; Senanayaka, Jagath Sri Lal & Robbersmyr, Kjell Gunnar (2017). Bearing fault detection of gear-box drive train using active filters, 2016 19th International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-4-88686-098-9.
• Senanayaka, Jagath Sri Lal; Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar (2016). Direct Torque Control of a Small Wind Turbine with a Sliding-Mode Speed Controller. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 753. doi: 10.1088/1742-6596/753/5/052031.
• Senanayaka, Jagath Sri Lal; Karimi, Hamid Reza & Robbersmyr, Kjell G (2015). Sensorless Small Wind Turbine with a Sliding-Mode Observer for Water Heating Applications, 41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015. IEEE Press. ISSN 978-1-4799-1762-4. s. 863–868. doi: 10.1109/IECON.2015.7392207.
• Senanayaka, Jagath Sri Lal & Kolhe, Mohan Lal (2015). Integration of distributed energy systems in micro-grid architecture for making a virtual power plant, 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES 2015). Curran Associates, Inc.. ISSN 9781479918249. doi: 10.1109/SPICES.2015.7091500.
• Senanayaka, Jagath Sri Lal; Karimi, Hamid Reza & Robbersmyr, Kjell G (2015). Sliding-mode observer based sensor-less control of a small wind energy conversion system, 2015 International Workshop on Recent Advances in Sliding Modes (RASM 2015). Curran Associates, Inc.. ISSN 9781479989485. doi: 10.1109/RASM.2015.7154583.

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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.
• 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.
• Senanayaka, Jagath Sri Lal & Robbersmyr, Kjell Gunnar (2020). Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion. 07 Media. ISSN 978-82-7117-971-7. Fulltekst i vitenarkiv
• Senanayaka, Jagath Sri Lal & Kolhe, Mohan Lal (2014). Power dispatching of active generators using droop control in grid connected micro-grid. Universitetet i Agder.

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