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A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks
Delgado Prieto, Miquel; Cirrincione,, Giansalvo; García Espinosa, Antonio; Ortega Redondo, Juan Antonio; Henao, Humberto
Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica; Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica; Universitat Politècnica de Catalunya. MCIA - Centre MCIA Innovation Electronics
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.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria electrònica::Electrònica de potència
Àrees temàtiques de la UPC::Enginyeria elèctrica
Power electronics
Electric machinery
Neural networks (Computer science)
Electric driving
Electrònica de potència
Màquines elèctriques
Xarxes neuronals (Informàtica)
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