Title :
Bearing fault diagnosis by EXIN CCA
Author :
Cirrincione, G. ; Henao, H. ; Delgado, M. ; Ortega, J.A.
Author_Institution :
LTI-EESA Lab., Univ. of Picardy Jules Verne, Amiens, France
Abstract :
EXIN CCA is an extension of the Curvilinear Component Analysis (CCA), which solves for the noninvariant CCA projection and allows representing data drawn under different operating conditions. It can be applied to data visualization, interpretation (as a kind of sensor of the underlying physical phenomenon) and classification for real time industrial applications. Here an example is given for bearing fault diagnostics in an electromechanical device.
Keywords :
fault diagnosis; machine bearings; maintenance engineering; mechanical engineering computing; EXIN CCA; bearing fault diagnostics; curvilinear component analysis; data interpretation; data visualization; electromechanical device; noninvariant CCA projection; real time industrial applications; Employee welfare; Interpolation; Neural networks; Principal component analysis; Torque; Training; Vectors; bearing fault; classification; curvilinear component analysis; intrinsic dimension; least squares; multilayer perceptron; principal component analysis; visualization;
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
DOI :
10.1109/IJCNN.2012.6252408
