Notice of RetractionA study on applications of principal component analysis and kernel principal component analysis for gearbox fault diagnosis

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.

[1]  Jianshe Kang,et al.  Gearbox fault diagnosis method based on wavelet packet analysis and support vector machine , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

[2]  I. Trendafilova.  An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition , 2010 .

[3]  Yang Qing,et al.  The research of pattern recognition of gear pump based on EMD and KPCA-SVM , 2011, 2011 International Conference on System science, Engineering design and Manufacturing informatization.

[4]  Irem Y. Tumer,et al.  Analysis of Triaxial Vibration Data for Health Monitoring of Helicopter Gearboxes , 2003 .

[5]  Qian He,et al.  Early Identification of Machine Fault Based on Kernel Principal Components Analysis , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[6]  Bernhard Schölkopf,et al.  A tutorial on kernel methods for categorization , 2007, Journal of Mathematical Psychology.

[7]  Weihua Li.  KERNEL PRINCIPAL COMPONENT ANALYSIS AND ITS APPLICATION IN GEAR FAULT DIAGNOSIS , 2003 .

[8]  Radoslaw Zimroz,et al.  Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes , 2011 .

[9]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[10]  Theodoros Loutas,et al.  The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery , 2011 .

[11]  Li Li,et al.  Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method , 2011 .

[12]  Pan Hong-xia.  Review of the Gearbox Fault Diagnosis Technology , 2006 .

[13]  Xu Yudong,et al.  Notice of RetractionApplication of principal component analysis and BP neural network in gear noise prediction , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[14]  Matthew P. Cartmell,et al.  Vibration-based damage detection in an aircraft wing scaled model using principal component analysis and pattern recognition , 2008 .

[15]  Fanrang Kong,et al.  Machine condition monitoring using principal component representations , 2009 .

[16]  Peng Lv,et al.  Kernel Principal Components Analysis for Early Identification of Gear Tooth crack , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[17]  Fanrang Kong,et al.  Subspace-based gearbox condition monitoring by kernel principal component analysis , 2007 .

[18]  Xinping Yan,et al.  Gear faults diagnosis based on wavelet-AR model and PCA , 2010, International Conference on Image Processing and Pattern Recognition in Industrial Engineering.

[19]  Naim Baydar,et al.  Detection of incipient gear failures using statistical techniques , 2002 .

[20]  Dominik Füssel,et al.  Supervision, Fault-Detection and Fault-Diagnosis Methods , 1999 .

[21]  R. Tibshirani,et al.  Sparse Principal Component Analysis , 2006 .

[22]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[23]  Pan Hongxia,et al.  Study of fault feature extraction based on KPCA optimized by PSO algorithm , 2010, FUZZ-IEEE.

[24]  Pilar Gargallo,et al.  A dynamic principal components analysis based on multivariate matrix normal dynamic linear models , 2003 .

[25]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[26]  W. Cholewa,et al.  Fault Diagnosis: Models, Artificial Intelligence, Applications , 2004 .

[27]  Weihua Li,et al.  Feature extraction and classification of gear faults using principal component analysis , 2003 .

[28]  H. Abdi,et al.  Principal component analysis , 2010 .

[29]  Radoslaw Zimroz,et al.  Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions , 2013 .

[30]  Liao Guang.  GEARBOX CONDITION MONITORING BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS , 2005 .

[31]  Radoslaw Zimroz,et al.  Gearbox vibration signal pre-processing and input values choice for neural network training , 2003 .

[32]  E. P. de Moura,et al.  Applications of detrended-fluctuation analysis to gearbox fault diagnosis , 2009 .

[33]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[34]  Radoslaw Zimroz,et al.  Sparse PCA for gearbox diagnostics , 2011, 2011 Federated Conference on Computer Science and Information Systems (FedCSIS).

[35]  Viliam Makis,et al.  Gearbox failure diagnosis based on vector autoregressive modelling of vibration data and dynamic principal component analysis , 2007 .

[36]  Tao Liu,et al.  Fault diagnosis of a mine hoist using PCA and SVM techniques , 2008 .

[37]  Li Li,et al.  Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis , 2013 .