DocumentCode
3443017
Title
Notice of Retraction
A study on applications of principal component analysis and kernel principal component analysis for gearbox fault diagnosis
Author
Deng Pan ; Zhiliang Liu ; Longlong Zhang ; Yinjiang Liu ; Zuo, Ming J.
Author_Institution
Sch. of Mech., Electron., & Ind. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear
2013
fDate
15-18 July 2013
Firstpage
1917
Lastpage
1922
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Principal component analysis (PCA) and kernel principal component analysis (KPCA) are widely used approaches of dimensionality reduction. They have been demonstrated useful for gearbox fault diagnosis. This paper provides a brief review of applications of PCA and KPCA for gearbox fault diagnosis. Literature is mainly grouped into two categories: applications of the conventional PCA/KPCA and applications of the improved PCA/KPCA. Discussions about the future work of PCA/KPCA on gearbox fault diagnosis are also provided in this paper.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Principal component analysis (PCA) and kernel principal component analysis (KPCA) are widely used approaches of dimensionality reduction. They have been demonstrated useful for gearbox fault diagnosis. This paper provides a brief review of applications of PCA and KPCA for gearbox fault diagnosis. Literature is mainly grouped into two categories: applications of the conventional PCA/KPCA and applications of the improved PCA/KPCA. Discussions about the future work of PCA/KPCA on gearbox fault diagnosis are also provided in this paper.
Keywords
fault diagnosis; maintenance engineering; power transmission (mechanical); principal component analysis; KPCA; dimensionality reduction; gearbox fault diagnosis; kernel principal component analysis; Covariance matrices; Fault diagnosis; Feature extraction; Gears; Kernel; Principal component analysis; Support vector machines; fault diagnosis; kernel method; principal component analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013 International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4799-1014-4
Type
conf
DOI
10.1109/QR2MSE.2013.6625954
Filename
6625954
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