Loading...
Loading...

Go to the content (press return)

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

Author
Delgado Prieto, M.; Cirrincione, G.; Garcia, A.; Ortega, J.A.; Henao, H.
Type of activity
Presentation of work at congresses
Name of edition
38th Annual Conference on IEEE Industrial Electronics Society
Date of publication
2012
Presentation's date
2012-10-25
Book of congress proceedings
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 Open in new window
Repository
http://hdl.handle.net/2117/19288 Open in new window
Abstract
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...
Citation
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.
Group of research
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Power Electronics Research Centre

Participants