dc.contributor.author | Dibaj, Ali | |
dc.contributor.author | Nejad, Amir R. | |
dc.contributor.author | Gao, Zhen | |
dc.date.accessioned | 2023-02-16T13:10:53Z | |
dc.date.available | 2023-02-16T13:10:53Z | |
dc.date.created | 2022-10-10T21:03:04Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Journal of Physics: Conference Series (JPCS). 2022, 2265 . | en_US |
dc.identifier.issn | 1742-6588 | |
dc.identifier.uri | https://hdl.handle.net/11250/3051543 | |
dc.description.abstract | This paper deals with the condition monitoring of a floating wind turbine drivetrain using multi-point acceleration measurements. Single sensor data obtained from drivetrain system may provide insufficient information about the health condition due to the complicated structure and applied loading on this system. As a result, multi-point measurements are required to be employed for reliable fault diagnosis. The shared information between the multi-point measurements can be used for identifying the system's condition. In this study, the fault diagnosis of the floating wind turbine drivetrain system is performed using a data-driven approach. Fault cases are considered in bearings most likely to damage. A combined principal component analysis (PCA) and deep convolutional neural network (CNN) is proposed to extract common and fault-related information between the measurements on the one hand and to classify different health conditions of the drivetrain on the other. It will be demonstrated that PCA-based information provides more satisfactory fault diagnosis results than individual sensor data. The method is numerically validated using the acceleration responses obtained from a 5-MW reference drivetrain model installed on a spar-type floating wind turbine. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IOP Publishing | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | A data-driven approach for fault diagnosis of drivetrain system in a spar-type floating wind turbine based on the multi-point acceleration measurements | en_US |
dc.title.alternative | A data-driven approach for fault diagnosis of drivetrain system in a spar-type floating wind turbine based on the multi-point acceleration measurements | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.pagenumber | 10 | en_US |
dc.source.volume | 2265 | en_US |
dc.source.journal | Journal of Physics: Conference Series (JPCS) | en_US |
dc.identifier.doi | 10.1088/1742-6596/2265/3/032096 | |
dc.identifier.cristin | 2060240 | |
dc.relation.project | Norges forskningsråd: 309205 | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |