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Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks

Autor
Delgado Prieto, M.; Cirrincione, G.; Garcia, A.; Ortega, J.A.; Henao, H.
Tipus d'activitat
Article en revista
Revista
IEEE transactions on industrial electronics
Data de publicació
2013-08
Volum
30
Número
8
Pàgina inicial
3398
Pàgina final
3407
DOI
https://doi.org/10.1109/TIE.2012.2219838 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/19572 Obrir en finestra nova
URL
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6307844 Obrir en finestra nova
Resum
Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines...
Citació
Delgado, M. [et al.]. Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. "IEEE transactions on industrial electronics", Agost 2013, vol. 30, núm. 8, p. 3398-3407.
Paraules clau
Ball bearings classification algorithms condition monitoring fault diagnosis feature extraction induction motors neural networks vibrations
Grup de recerca
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Centre de Recerca d'Electrònica de Potència UPC

Participants