Publication
Fault diagnosis for wind turbine gearboxes by using deep enhanced fusion network
| dc.contributor.author | Pu, Ziqiang | |
| dc.contributor.author | Li, Chuan | |
| dc.contributor.author | Zhang, Shaohui | |
| dc.contributor.author | Bai, Yun | |
| dc.date.accessioned | 2021-09-08T10:58:15Z | |
| dc.date.available | 2021-09-08T10:58:15Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | The gearbox will directly affect the safety and reliability of the wind turbine, whose failure leads to low processing accuracy and certain economic losses. To address this issue, a deep enhanced fusion network (DEFN) is proposed for the fault diagnosis of the wind turbine gearbox with the experimental vibration data. In the proposed DEFN, three sparse autoencoders are first applied to extract deep features of three-axial vibration signals, respectively. Second, a feature enhancement mapping is developed to minimize the intraclass distance of the deep features in the three-axial vibration. Finally, the fused three-axis features are put into an echo state network for fault classification. The results of the experiment carried out in a wind turbine show that the proposed DEFN has a good fault diagnosis accuracy compared with other peer models. | |
| dc.description.sponsorship | National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51775112, 71801044, 51975121]; Guangdong Research Foundation [2019B1515120095]; Chongqing Natural Science Fund [cstc2019jcyjzdxmX0013]; CTBU Project [KFJJ2019060] | |
| dc.description.version | info:eu-repo/semantics/publishedVersion | |
| dc.identifier.doi | 10.1109/TIM.2020.3024048 | |
| dc.identifier.issn | 0018-9456 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/17074 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | |
| dc.subject | Echo state network (ESN) | |
| dc.subject | Fault diagnosis | |
| dc.subject | Feature enhancement | |
| dc.subject | Sparse autoencoder (SAE) | |
| dc.subject | Wind turbine gearbox | |
| dc.subject.other | Engineering; Instruments & Instrumentation | |
| dc.title | Fault diagnosis for wind turbine gearboxes by using deep enhanced fusion network | |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.startPage | 2501811 | |
| oaire.citation.title | Ieee Transactions on Instrumentation and Measurement | |
| oaire.citation.volume | 70 | |
| person.familyName | Pu | |
| person.familyName | Bai | |
| person.givenName | Ziqiang | |
| person.givenName | Yun | |
| person.identifier.orcid | 0000-0001-8186-8239 | |
| person.identifier.orcid | 0000-0003-2710-7994 | |
| person.identifier.scopus-author-id | 55461096500 | |
| rcaap.rights | restrictedAccess | |
| rcaap.type | article | |
| relation.isAuthorOfPublication | 34dba8e4-de5d-4df1-86cd-9e8d5252c255 | |
| relation.isAuthorOfPublication | 395ae945-8e87-47b3-9edf-6fa1f380097f | |
| relation.isAuthorOfPublication.latestForDiscovery | 34dba8e4-de5d-4df1-86cd-9e8d5252c255 |
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