Liu, Z.-H., Jiang, L.-B., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (2 more authors) (2021) Optimal transport based deep domain adaptation approach for fault diagnosis of rotating machine. IEEE Transactions on Instrumentation and Measurement, 70. 3508912. ISSN 0018-9456
Abstract
Rotating machinery working under changing operation conditions is prone to failure. In recent years, domain adaptation has been successfully used for fault diagnosis. However, the existing fault diagnosis methods based on domain adaptation have two main disadvantages: 1) With these methods, it is difficult to precisely measure and estimate the differences between the source and target domains; 2) They only consider the discrepancies in the feature space, but not in the label space. In this paper, a new optimal transport based deep domain adaptation model is proposed for rotating machine fault diagnosis. The framework of the proposed method comprises three main components. Firstly, an autoencoder network is designed to extract compact and class discriminative features from the raw data. Secondly, the domain-invariant representation features are trained by searching an optimal transport plan with a predefined cost function between source and target domains and by minimizing the discrepancies of a joint distribution of the feature and label spaces based on optimal transport. Finally, the classifier trained with data in the source domain is directly used to perform the classification task in the target domain. In addition, the optimal selection of the model hyper-parameters is verified through empirical analysis, and the transfer ability of the proposed model is visually illustrated in a reduced feature space. The experimental results show that the proposed method outperforms the existing machine learning and domain adaptation fault diagnosis methods, in terms of, e.g., classification accuracy and generalization ability.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 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: | Autoencoder; deep learning; domain adaptation; fault diagnosis; optimal transport; rotating machine; transfer learning |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Jan 2021 09:21 |
Last Modified: | 10 Feb 2022 12:37 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tim.2021.3050173 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170081 |