Liu, Z.-H., Lu, B.-L., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (2 more authors) (2019) Fault diagnosis for electromechanical drivetrains using a joint distribution optimal deep domain adaptation approach. IEEE Sensors Journal, 19 (24). pp. 12261-12270. ISSN 1530-437X
Abstract
Robust and reliable drivetrain is important for preventing electromechanical (e.g., wind turbine) downtime. In recent years, advanced machine learning (ML) techniques including deep learning have been introduced to improve fault diagnosis performance for electromechanical systems. However, electromechanical systems (e.g., wind turbine) operate in varying working conditions, meaning that the distribution of the test data (in the target domain) is different from the training data used for model training, and the diagnosis performance of an ML method may become downgraded for practical applications. This paper proposes a joint distribution optimal deep domain adaptation approach (called JDDA) based auto-encoder deep classifier for fault diagnosis of electromechanical drivetrains under the varying working conditions. First, the representative features are extracted by the deep auto-encoder. Then, the joint distribution adaptation is used to implement the domain adaptation, so the classifier trained with the source domain features can be used to classify the target domain data. Lastly, the classification performance of the proposed JDDA is tested using two test-rig datasets, compared with three traditional machine learning methods and two domain adaptation approaches. Experimental results show that the JDDA can achieve better performance compared with the reference machine learning, deep learning and domain adaptation approaches.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | fault diagnosis; electromechanical drivetrain; deep neural network; deep learning; domain adaptation (DA); joint distribution optimal; auto-encoder(AE); machine learning; artificial intelligence; bearing; gearboxes; wind turbine; varying working conditions |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council (EPSRC) EP/I011056/1; EP/H00453X/1 National Natural Science Foundation of China 61972443; 61573299; 61503134 Hunan Provincial Hu-Xiang Young Talents Project of China 2018RS3095 Hunan Provincial Natural Science Foundation of China 2018JJ2134 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Sep 2019 10:55 |
Last Modified: | 12 Nov 2021 14:34 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/jsen.2019.2939360 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151429 |