Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/8394
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dc.contributor.authorSoua, S-
dc.contributor.authorVan Lieshout, P-
dc.contributor.authorPerera, A-
dc.contributor.authorGan, T-H-
dc.contributor.authorBridge, B-
dc.date.accessioned2014-05-08T10:14:01Z-
dc.date.available2014-05-08T10:14:01Z-
dc.date.issued2013-
dc.identifier.citationRenewable Energy, 51, 175 - 181, 2013en_US
dc.identifier.issn0960-1481-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0960148112004302en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/8394-
dc.descriptionThis is the post-print version of the final paper published in Renewable Energy. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.en_US
dc.description.abstractA review of current progress in Condition Monitoring (CM) of wind turbine gearboxes and generators is presented, as an input to the design of a new continuous CM system with automated warnings based on a combination of vibrational and Acoustic Emission (AE) analysis. For wind turbines, existing reportage on vibrational monitoring is restricted to a few case histories whilst data on AE is even scarcer. In contrast, this paper presents combined vibration and AE monitoring performed over a continuous period of 5 days on a wind turbine. The vibrational and AE signatures for a healthy wind turbine gearbox and generator were obtained as a function of wind speed and turbine power, for the full normal range of these operational variables. i.e. 5–25 m/s and 0–300 kW respectively. The signatures have been determined as a vital pre-requisite for the identification of abnormal signatures attributable to shaft and gearbox defects. Worst-case standard deviations have been calculated for the sensor data. These standard deviations determine the minimum defect signal that could be detected within the defined time interval without false alarms in an automated warning system.en_US
dc.description.sponsorshipUK Northern Wind Innovation Program NWIPen_US
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectSignature of healthy wind turbineen_US
dc.subjectCondition monitoringen_US
dc.subjectAcoustic emissionen_US
dc.subjectVibrationen_US
dc.subjectGearboxen_US
dc.subjectProbability of detectionen_US
dc.titleDetermination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoringen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.renene.2012.07.004-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Active Staff-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Engineering & Design-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Engineering & Design/Electronic and Computer Engineering-
pubs.organisational-data/Brunel/University Research Centres and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Engineering and Design - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Engineering and Design - URCs and Groups/Brunel Innovation Centre-
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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