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dc.contributor.authorPozo Montero, Francesc
dc.contributor.authorVidal Seguí, Yolanda
dc.contributor.authorAcho Zuppa, Leonardo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2016-11-02T13:39:29Z
dc.date.available2016-11-02T13:39:29Z
dc.date.issued2016
dc.identifier.citationPozo, F., Vidal, Y., Acho, L. Wind turbine fault detection through principal component analysis and multivariate statistical inference. A: "8th European Workshop on Structural Health Monitoring": Bilbao, Spain, 5-8 July 2016.
dc.identifier.urihttp://hdl.handle.net/2117/91344
dc.description.abstractThis work addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the wind turbine is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data—the baseline and the data from the current wind turbine— are compared, a multivariate statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault- detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides an early fault detection, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant
dc.subject.lcshMultivariate analysis
dc.subject.otherwind turbine
dc.subject.otherfault detection
dc.subject.otherprincipal component analysis
dc.subject.othermultivariate statistical hypothesis testing
dc.titleWind turbine fault detection through principal component analysis and multivariate statistical inference
dc.typeConference lecture
dc.subject.lemacAnàlisi multivariant -- Congressos
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.rights.accessOpen Access
local.identifier.drac18766723
dc.description.versionPostprint (published version)
local.citation.authorPozo, F.; Vidal, Y.; Acho, L.
local.citation.contributor8th European Workshop on Structural Health Monitoring
local.citation.pubplaceBilbao


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