Bearing fault diagnosis by EXIN CCA

G. Cirrincione; H. Henao; M. Delgado; J. A. Ortega

doi:10.1109/IJCNN.2012.6252408

Bearing fault diagnosis by EXIN CCA

G. Cirrincione^*
, H. Henao
, M. Delgado
, J. A. Ortega

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs	https://doi.org/10.1109/IJCNN.2012.6252408
Publication status	Published - 2012
Externally published	Yes
Event	2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia Duration: 10 Jun 2012 → 15 Jun 2012

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/Territory	Australia
City	Brisbane, QLD
Period	10/06/12 → 15/06/12

Keywords

bearing fault
classification
curvilinear component analysis
intrinsic dimension
least squares
multilayer perceptron
principal component analysis
visualization

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10.1109/IJCNN.2012.6252408

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title = "Bearing fault diagnosis by EXIN CCA",

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 = "bearing fault, classification, curvilinear component analysis, intrinsic dimension, least squares, multilayer perceptron, principal component analysis, visualization",

author = "G. Cirrincione and H. Henao and M. Delgado and Ortega, \{J. A.\}",

year = "2012",

doi = "10.1109/IJCNN.2012.6252408",

language = "English",

isbn = "9781467314909",

series = "Proceedings of the International Joint Conference on Neural Networks",

booktitle = "2012 International Joint Conference on Neural Networks, IJCNN 2012",

note = "2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 ; Conference date: 10-06-2012 Through 15-06-2012",

}

Cirrincione, G, Henao, H, Delgado, M & Ortega, JA 2012, Bearing fault diagnosis by EXIN CCA. in 2012 International Joint Conference on Neural Networks, IJCNN 2012., 6252408, Proceedings of the International Joint Conference on Neural Networks, 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, QLD, Australia, 10/06/12. https://doi.org/10.1109/IJCNN.2012.6252408

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AU - Cirrincione, G.

AU - Henao, H.

AU - Delgado, M.

AU - Ortega, J. A.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - bearing fault

KW - classification

KW - curvilinear component analysis

KW - intrinsic dimension

KW - least squares

KW - multilayer perceptron

KW - principal component analysis

KW - visualization

UR - https://www.scopus.com/pages/publications/84865067896

U2 - 10.1109/IJCNN.2012.6252408

DO - 10.1109/IJCNN.2012.6252408

M3 - Conference contribution

AN - SCOPUS:84865067896

SN - 9781467314909

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012

T2 - 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

Bearing fault diagnosis by EXIN CCA

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Keywords

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