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Accurate Bearing Faults Classification based on Statistical-Time Features, Curvilinear Component Analysis and Neural Networks

Autor
Delgado Prieto, M.; Cirrincione, G.; Garcia, A.; Ortega, J.A.; Henao, H.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
38th Annual Conference on IEEE Industrial Electronics Society
Any de l'edició
2012
Data de presentació
2012-10-25
Llibre d'actes
IECON 2012: the 38th Annual Conference of the IEEE Industrial Electronics Society: Montreal, Canada: 25-28 October 2012: proceedings
DOI
https://doi.org/10.1109/IECON.2012.6389596 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/19288 Obrir en finestra nova
Resum
Bearing faults are the commonest form of malfunction associated with electrical machines. So far, the research has been carried out mainly in the detection of localized faults, but the diagnosis of distributed faults is still under development. In this context, this work presents a new scheme for detecting and classifying both kinds of faults. This work deals with a new diagnosis monitoring scheme, which is based on statistical-time features calculated from vibration signal, curvilinear componen...
Citació
Delgado, M. [et al.]. Accurate Bearing Faults Classification based on Statistical-Time Features, Curvilinear Component Analysis and Neural Networks. A: Annual Conference of the IEEE Industrial Electronics Society. "IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society". 2012.
Grup de recerca
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Centre de Recerca d'Electrònica de Potència UPC

Participants