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dc.contributor.authorSantos González, Pedro
dc.contributor.authorVilla, Luisa F.
dc.contributor.authorReñones, Anibal
dc.contributor.authorBustillo Iglesias, Andrés 
dc.contributor.authorMaudes Raedo, Jesús M. 
dc.date.accessioned2016-11-09T10:55:54Z
dc.date.available2016-11-09T10:55:54Z
dc.date.issued2015-03
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10259/4264
dc.description.abstractResearch into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.en
dc.description.sponsorshipProjects, CENIT-2008-1028, TIN2011-24046, IPT-2011-1265-020000 and DPI2009-06124-E/DPI of the Spanish Ministry of Economy and Competitivenessen
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIen
dc.relation.ispartofSensors, 2015, V. 15, n. 3, p. 4605-7083en
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectfault diagnosisen
dc.subjectneural networksen
dc.subjectsupport vector machinesen
dc.subjectwind turbinesen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleAn SVM-based solution for fault detection in wind turbinesen
dc.typeArtículoes
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttp://dx.doi.org/10.3390/s150305627
dc.identifier.doi10.3390/s150305627
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/CENIT-2008-1028
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/TIN2011-24046
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/IPT-2011-1265-020000
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/DPI2009-06124-E/DPI
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen


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