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dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.authorCirrincione,, Giansalvo
dc.contributor.authorGarcía Espinosa, Antonio
dc.contributor.authorOrtega Redondo, Juan Antonio
dc.contributor.authorHenao, Humberto
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2013-05-16T09:59:44Z
dc.date.created2012
dc.date.issued2012
dc.identifier.citationDelgado, M. [et al.]. A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks. A: International Conference on Electrical Machines. "Electrical Machines (ICEM), 2012 XXth International Conference on". Marsella: 2012.
dc.identifier.isbn978-1-4673-0143-5
dc.identifier.urihttp://hdl.handle.net/2117/19280
dc.description.abstractMostly the faults in electrical machines are related with the bearings. Thus, a reliable bearing condition monitoring scheme able to detect either local or distributed defects are mandatory to avoid a breakdown in the machine. So far, the research has been carried out mainly in the detection of local faults, such as balls and raceways faults, but surface roughness is not so reported. This paper deals with a novel and reliable scheme capable to detect any fault that may occur in a bearing, based on EXIN Curvilinear Component Analysis, CCA, and Neural Network. The EXIN CCA, which is an improvement of the Curvilinear Component Analysis, has been conceived for data visualization, interpretation and classification for real time industrial applications. The effectiveness of this condition monitoring scheme has been verified by experimental results obtained from different operation conditions.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica::Electrònica de potència
dc.subjectÀrees temàtiques de la UPC::Enginyeria elèctrica
dc.subject.lcshPower electronics
dc.subject.lcshElectric machinery
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshElectric driving
dc.titleA novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks
dc.typeConference report
dc.subject.lemacElectrònica de potència
dc.subject.lemacMàquines elèctriques
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/ICElMach.2012.6350231
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac12365969
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorDelgado, M.; Cirrincione,; Garcia, A.; Ortega, J.; Henao, H.
local.citation.contributorInternational Conference on Electrical Machines
local.citation.pubplaceMarsella
local.citation.publicationNameElectrical Machines (ICEM), 2012 XXth International Conference on


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