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

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

7

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2012 International Joint Conference on

Conference_Location

Brisbane, QLD

ISSN

2161-4393

Print_ISBN

978-1-4673-1488-6

Electronic_ISBN

2161-4393

Type

conf

DOI

10.1109/IJCNN.2012.6252408

Filename

6252408