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dc.contributor.authorGhane, Mahdi
dc.contributor.authorRasekhi Nejad, Amir
dc.contributor.authorBlanke, Mogens
dc.contributor.authorGao, Zhen
dc.contributor.authorMoan, Torgeir
dc.identifier.citationJournal of Physics, Conference Series 2016, 753nb_NO
dc.description.abstractDeployment of larger scale wind turbine systems, particularly offshore wind turbine, requires more organized operation and maintenance strategies to make it as competitive as the classical electric power stations. It is important to ensure systems are safe, profitable and cost-effective. In this regards, the ability to detect, isolate, estimate faults play an important role. One of the critical wind turbine components is the gearbox. Failures in the gearbox are costly both due to the cost of the gearbox itself, but also due to high repair downtime. In order to detect faults as fast as possible to prevent them to develop into failure, statistical change detection is used. In this paper, Cumulative Sum method (CUSUM) is used to diagnose fault in downwind main bearing in a high fidelity gearbox model of a 5-MW spar-type wind turbine. Residuals are found to be non-Gaussian following a t-distribution with multivariable characteristic parameters. Results show CUSUM method could detect change and estimate change time very agile with desired false alarm and detection probabilitiesnb_NO
dc.publisherIOP Publishingnb_NO
dc.rightsNavngivelse 3.0 Norge*
dc.titleStatistical fault diagnosis of wind turbine drivetrain applied to a 5MW floating wind turbinenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalJournal of Physics, Conference Seriesnb_NO
dc.relation.projectNorges forskningsråd: 223254nb_NO
dc.description.localcodeContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.nb_NO

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Navngivelse 3.0 Norge
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