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DC Field | Value | Language |
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dc.contributor.author | Wang, Q | - |
dc.contributor.author | Yang, C | - |
dc.contributor.author | Wan, H | - |
dc.contributor.author | Deng, D | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2021-06-12T13:22:42Z | - |
dc.date.available | 2021-06-12T13:22:42Z | - |
dc.date.issued | 2021-05-11 | - |
dc.identifier | ORCID iD: Asoke K. Nandi https://orcid.org/0000-0001-6248-2875 | - |
dc.identifier | 104007 | - |
dc.identifier.citation | Wang, Q.,et al. (2021) 'Bearing Fault Diagnosis Based on Optimized Variational Mode Decomposition and 1-D Convolutional Neural Networks', Measurement Science and Technology, 32, 104007, pp. 1 - 16. doi: 10.1088/1361-6501/ac0034/ | en_US |
dc.identifier.issn | 0957-0233 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22832 | - |
dc.description | Data availability statement: All data that support the findings of this study are included within the article (and any supplementary files). | - |
dc.description.abstract | Due to the fact that measured vibration signals from a bearing are complex and non-stationary in nature, and that impulse characteristics are always immersed in stochastic noise, it is usually difficult to diagnose fault symptoms manually. A novel hybrid fault diagnosis approach is developed for the denoising signals and fault classification in this work, which combines successfully the variational mode decomposition (VMD) and one dimensional convolutional neural network (1-D CNN). VMD is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the modal number and penalty parameter are very important in VMD, a particle swarm mutation optimization (PSMO) as a novel optimization method and the weighted signal difference average (WSDA) as a new fitness function are proposed to optimize the parameters of VMD. The reconstructed signals of mode components decomposed by optimized VMD are used as the input of the 1-D CNN to obtain fault diagnosis models. The performance of the proposed hybrid approach has been evaluated using the sets of experimental data of rolling bearings. The experimental results demonstrate that the VMD can eliminate signal noise and strengthen status characteristics, and the proposed hybrid approach has a superior capability for fault diagnosis from vibration signals of bearings. | en_US |
dc.description.sponsorship | This research was funded by the National Natural Science Foundation of China, Grant Nos. 51105291 and 51605364, by the Shaanxi Provincial Science and Technology Department, Grant Nos. 2020GY-124, 2019GY-125 and 2018JQ5127, and by the Key Laboratory Project of Department of Education of Shaanxi Province, Grant Nos. 19JS034 and 18JS045. Also, this work is supported by Brunel University London (UK) and the National Fund for Study Abroad (China). | en_US |
dc.format.extent | 1 - 16 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | IOP Publishing | en_US |
dc.rights | Copyright © 2021 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | fault diagnosis | en_US |
dc.subject | bearing | en_US |
dc.subject | variational mode decomposition (VMD) | en_US |
dc.subject | one dimensional convolutional neural network (1-D CNN) | en_US |
dc.subject | PSMO optimization method | en_US |
dc.title | Bearing Fault Diagnosis Based on Optimized Variational Mode Decomposition and 1-D Convolutional Neural Networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1088/1361-6501/ac0034 | - |
dc.relation.isPartOf | Measurement Science and Technology | - |
pubs.publication-status | Published | - |
pubs.volume | 32 | - |
dc.identifier.eissn | 1361-6501 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2021 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | 3.54 MB | Adobe PDF | View/Open |
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