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DC Field | Value | Language |
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dc.contributor.author | Liu, Y-K | - |
dc.contributor.author | Zhou, W | - |
dc.contributor.author | Ayodeji, A | - |
dc.contributor.author | Zhou, X-Q | - |
dc.contributor.author | Peng, M-J | - |
dc.contributor.author | Chao, N | - |
dc.date.accessioned | 2024-05-08T13:38:08Z | - |
dc.date.available | 2024-05-08T13:38:08Z | - |
dc.date.issued | 2020-08-08 | - |
dc.identifier | ORCiD: Abiodun Ayodeji https://orcid.org/0000-0003-3257-7616 | - |
dc.identifier.citation | Liu, Y.-K. et al. (2021) 'A multi-layer approach to DN 50 electric valve fault diagnosis using shallow-deep intelligent models', Nuclear Engineering and Technology, 53 (1), pp. 148 - 163. doi: 10.1016/j.net.2020.07.001. | en_US |
dc.identifier.issn | 1738-5733 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28953 | - |
dc.description | Research data for this article: Data not available / Data will be made available on request. | en_US |
dc.description | Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S1738573320300279#appsec1 . | - |
dc.description.abstract | Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper. | en_US |
dc.description.sponsorship | Project for State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment (No.K-A2019.418); Technical Support Project for Suzhou Nuclear Power Research Institute (SNPI, No.029-GN-b-2018-C45-P.0.99–00003); The Basic Research Project (No. JCKY2017xx7B019); Foundation of Science and Technology on Reactor System Design Laboratory (No. HT-KFKT-14-2017003). | en_US |
dc.format.extent | 148 - 163 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier on behalf of the Korean Nuclear Society | en_US |
dc.rights | Copyright © 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | electric valve | en_US |
dc.subject | vibration and acoustic signal | en_US |
dc.subject | support vector machine | en_US |
dc.subject | particle swarm optimization | en_US |
dc.subject | deep belief network | en_US |
dc.title | A multi-layer approach to DN 50 electric valve fault diagnosis using shallow-deep intelligent models | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2020-07-03 | - |
dc.identifier.doi | https://doi.org/10.1016/j.net.2020.07.001 | - |
dc.relation.isPartOf | Nuclear Engineering and Technology | - |
pubs.issue | 1 | - |
pubs.publication-status | Published | - |
pubs.volume | 53 | - |
dc.identifier.eissn | 2234-358X | - |
dc.rights.license | https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.en | - |
dc.rights.holder | Korean Nuclear Society | - |
Appears in Collections: | Brunel Innovation Centre |
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FullText.pdf | Copyright © 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). | 5.21 MB | Adobe PDF | View/Open |
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