Liu, Z.-H., Lu, B.-L., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (3 more authors) (2021) Deep adversarial domain adaptation model for bearing fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51 (7). pp. 4217-4226. ISSN 2168-2216
Abstract
Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis. This model constructs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack autoencoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Third, comprehensive analysis is performed on real data to validate the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.
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; bearing; feature extraction; stack auto-encoder (SAE); unsupervised learning; domain adaptation; adversarial network; machine learning; deep learning; deep neural networks |
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 National Natural Science Foundation of China 61503134; 61573299 Hunan Provincial Young Talents Project 2018RS3095 Hunan Provincial Natural Science Foundation of China 13JJ8014 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Sep 2019 10:44 |
Last Modified: | 12 Nov 2021 14:28 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tsmc.2019.2932000 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151428 |