TY - GEN
T1 - A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks
AU - Delgado, M.
AU - Cirrincione, G.
AU - Garcia, A.
AU - Ortega, J. A.
AU - Henao, H.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Ball bearings
KW - Classification algorithms
KW - Curvilinear Component Analysis
KW - Discriminant Analysis
KW - Fault diagnosis
KW - Least Squares approximation
KW - Motor Fault detection
KW - Multilayer perceptrons
KW - Neural Networks
KW - Radial basis function networks
KW - Time domain analysis
KW - Vibrations
UR - https://www.scopus.com/pages/publications/84870817852
U2 - 10.1109/ICElMach.2012.6350231
DO - 10.1109/ICElMach.2012.6350231
M3 - Conference contribution
AN - SCOPUS:84870817852
SN - 9781467301428
T3 - Proceedings - 2012 20th International Conference on Electrical Machines, ICEM 2012
SP - 2472
EP - 2478
BT - Proceedings - 2012 20th International Conference on Electrical Machines, ICEM 2012
T2 - 2012 20th International Conference on Electrical Machines, ICEM 2012
Y2 - 2 September 2012 through 5 September 2012
ER -