Liu, Z.-H. orcid.org/0000-0002-6597-4741, Chen, L., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (3 more authors) (2023) A tensor-based domain alignment method for intelligent fault diagnosis of rolling bearing in rotating machinery. Reliability Engineering & System Safety, 230. 108968. ISSN 0951-8320
Abstract
Fault diagnosis of rolling bearings plays a pivotal role in modern industry. Most existing methods have two disadvantages: 1) The assumption that the training and test data obey the same distribution; and 2) They are designed for vector representation which is unable to characterize the important structure of the rolling bearings data of interest. To overcome these drawbacks, this paper proposes a novel tensor based domain adaptation method. Firstly, this method uses the time domain signals, the frequency domain signals, and the Hilbert marginal spectrum and integrates them into a third-order tensor model. Secondly, these three types of signals are split into two parts: the source and target domain data; all the representative features are identified in the source domain. Thirdly, a tensor decomposition method is used to decompose the features into a series of third-order tensors, and several alignment matrices are defined to align the representation of the two domains to the tensor invariant subspace. Then, the alignment matrices and the tensor subspace are jointly optimized to realize the adaptive learning. Finally, the feature tensor is reconstructed into a matrix form to realize the fault diagnosis through the classifier. Extensive experiments are conducted on a public dataset and a dataset collected from our own laboratory; experimental results show the satisfactory performance of the proposed method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier. This is an author produced version of a paper subsequently published in Reliability Engineering & System Safety. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Tensor representation; Subspace learning; Tensor alignment; Fault diagnosis; Domain adaptation; Transfer learning; Rolling bearings; Rotating machinery |
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: | 30 Mar 2023 15:19 |
Last Modified: | 09 Nov 2023 01:13 |
Published Version: | http://dx.doi.org/10.1016/j.ress.2022.108968 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ress.2022.108968 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197848 |