A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/19280

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor	Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.contributor.author	Delgado Prieto, Miquel
dc.contributor.author	Cirrincione,, Giansalvo
dc.contributor.author	García Espinosa, Antonio
dc.contributor.author	Ortega Redondo, Juan Antonio
dc.contributor.author	Henao, Humberto
dc.date	2012
dc.identifier.citation	Delgado, 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.citation	978-1-4673-0143-5
dc.identifier.citation	10.1109/ICElMach.2012.6350231
dc.identifier.uri	http://hdl.handle.net/2117/19280
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria electrònica::Electrònica de potència
dc.subject	Àrees temàtiques de la UPC::Enginyeria elèctrica
dc.subject	Power electronics
dc.subject	Electric machinery
dc.subject	Neural networks (Computer science)
dc.subject	Electric driving
dc.subject	Electrònica de potència
dc.subject	Màquines elèctriques
dc.subject	Xarxes neuronals (Informàtica)
dc.title	A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	Mostly 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.description.abstract	Peer Reviewed

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