Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion
Doctoral thesis
Published version
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https://hdl.handle.net/11250/2653755Utgivelsesdato
2020Metadata
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Originalversjon
Senanayaka, J. S. L. (2020). Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion (Doctoral thesis). University of Agder, Kristiansand.Sammendrag
Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts.
In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.
Består av
Paper I: Senanayaka, J. S. L., Kandukuri, S. T., Huynh, K. & Robbersmyr, K. G. (2017). Early detection and classification of bearing faults using support vector machine algorithm. Proceedings of 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, 250-255. doi: https://doi.org/10.1109/WEMDCD.2017.7947755. Author's accepted manuscript. Full-text is not available in AURA as a separate file.Paper II: Senanayaka, J. S. L., Huynh, K. & Robbersmyr, K. G. (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, 1037(3): 032028. doi: https://doi.org/10.1088/1742-6596/1037/3/032028. Full-text is available in AURA as a separate file: .
Senanayaka, J. S. L., Huynh, K. & Robbersmyr, K. G. (2018). Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults. IEEE Transactions on Industrial Informatics, 15(8), 4569-4579. doi: https://doi.org/10.1109/TII.2018.2883357. Author's accepted manuscript. Full-text is not available in AURA as a separate file.
Senanayaka, J. S. L., Huynh, K. & Robbersmyr, K. G. (2018). Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks. Proceedings of 2018 XIII International Conference on Electrical Machines, 1900-1905. doi: https://doi.org/10.1109/ICELMACH.2018.8507096. Author's accepted manuscript. Full-text is not available in AURA as a separate file.
Senanayaka, J. S. L., Huynh, K. & Robbersmyr, K. G. (2018). Online Fault Diagnosis System for Electric Powertrains Using Advanced Signal Processing and Machine Learning. Proceedings of 2018 XIII International Conference on Electrical Machines, 1932-1938. doi: https://doi.org/10.1109/ICELMACH.2018.8507171. Author's accepted manuscript. Full-text is not available in AURA as a separate file.
Senanayaka, J. S. L., Huynh, K. & Robbersmyr, K. G. (Forthcoming). Towards Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains. Author-submitted manuscript. Full-text is not available in AURA as a separate file.