Moving window kernel PCA for adaptive monitoring of nonlinear processes
Tài liệu tham khảo
AlGhazzawi, 2008, Monitoring a complex refining process using multivariate statistics, Control Engineering Practice, 16, 294, 10.1016/j.conengprac.2007.04.014
Anderson, 2003
Bunch, 1978, Rank-one modification of the symmetric eigenproblem, Numerische Mathematik, 31, 31, 10.1007/BF01396012
Cattell, 1966, The scree test for the number of factors, Multivariate Behavioral Research, 1, 245, 10.1207/s15327906mbr0102_10
Chin, 2006, Incremental kernel svd for face recognition with image sets, 461
Choi, 2005, Fault identification for process monitoring using kernel principal component analysis, Chemical Engineering Science, 60, 279, 10.1016/j.ces.2004.08.007
Choi, 2005, Fault detection and identification of nonlinear processes based on kpca, Chemometrics and Intelligent Laboratory Systems, 75, 55, 10.1016/j.chemolab.2004.05.001
Cui, 2008, Improved kernel principal component analysis for fault detection, Expert Systems With Applications, 34, 1210, 10.1016/j.eswa.2006.12.010
Dong, 1996, Nonlinear principal component analysis-based on principal curves and neural networks, Computers and Chemical Engineering, 20, 65, 10.1016/0098-1354(95)00003-K
Ge, 2007, Process monitoring based on independent component analysis–principal component analysis (ica–pca) and similarity factors, Industrial & Engineering Chemistry Research, 46, 2054, 10.1021/ie061083g
Golub, 1973, Some modified matrix eigenvalue problems, SIAM Review, 15, 318, 10.1137/1015032
Golub, 1996
Hall, 1998, Incrementally computing eigenspace models, 286
Hall, 2000, Merging and splitting eigenspace models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1042, 10.1109/34.877525
Hall, 2002, Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition, Image and Vision Computing, 20, 1009, 10.1016/S0262-8856(02)00114-2
He, 2007, Subspace-based gearbox condition monitoring by kernel principal component analysis, Mechanical Systems and Signal Processing, 21, 1755, 10.1016/j.ymssp.2006.07.014
Hoegaerts, 2007, Efficiently updating and tracking the dominant kernel principal components, Neural Networks, 20, 220, 10.1016/j.neunet.2006.09.012
Jackson, 1991
Jia, 2000, Nonlinear principal components analysis with application to process fault detection, International Journal of Systems Science, 31, 1473, 10.1080/00207720050197848
Joliffe, 1986
Kim, 2004, Incremental nonlinear pca for classification
Kramer, 1991, Nonlinear principal component analysis using autoassociative neural networks, AIChE journal, 37, 233, 10.1002/aic.690370209
Kruger, 2005, Introduction of a nonlinearity measure for principal component models, Computers & Chemical Engineering, 29, 2355, 10.1016/j.compchemeng.2005.05.013
Kruger, 2001, Extended pls approach for enhanced condition monitoring of industrial processes, AIChE Journal, 47, 2076, 10.1002/aic.690470918
U. Kruger, J. Zhang, L. Xie, 2007. Principal manifolds for data visualization and dimension reduction. Vol. 58 of Lecture notes in computational science and engineering. Springer Verlag, Berlin-Heidelberg-New York, Ch. Developments and applications of nonlinear principal component analysis — A review, pp. 1–44.
Lee, 2006, Multivariate online monitoring of a full-scale biological anaerobic filter process using kernel-based algorithms, Industrial & Engineering Chemistry Research, 45, 4335, 10.1021/ie050916k
Lee, 2006, Fault detection and diagnosis based on modified independent component analysis, AIChE Journal, 52, 3501, 10.1002/aic.10978
Lee, 2004, Nonlinear process monitoring using kernel principal component analysis, Chemical Engineering Science, 59, 223, 10.1016/j.ces.2003.09.012
Li, 2000, Recursive pca for adaptive process monitoring, Journal of Process Control, 10, 471, 10.1016/S0959-1524(00)00022-6
Liu, 2008, Statistical-based monitoring of multivariate non-Gaussian systems, AIChE Journal, 54, 2379, 10.1002/aic.11526
Malinowski, 1991
Morud, 1996, Multivariate statistical process control; example from the chemical process industry, Journal of Chemometrics, 10, 669, 10.1002/(SICI)1099-128X(199609)10:5/6<669::AID-CEM467>3.0.CO;2-Q
Nomikos, 1995, Multivariate spc charts for monitoring batch processes, Technometrics, 37, 41, 10.2307/1269152
Paige, 1980, Accuracy and effectiveness of the Lanczos algorithm, Linear Algebra and Its Applications, 34, 235, 10.1016/0024-3795(80)90167-6
Parlett, 1980
Qin, 2000, Determining the number of principal components for best reconstruction, Journal of Process Control, 10, 245, 10.1016/S0959-1524(99)00043-8
Scholkopf, 1999, Input space versus feature space in kernel-based methods, IEEE Transactions on Neural Networks, 10, 1000, 10.1109/72.788641
Tan, 1995, Reducing data dimensionality through optimizing neural network inputs, AIChE Journal, 41, 1471, 10.1002/aic.690410612
Tracy, 1992, Multivariate control charts for individual observations, Journal of Quality Control, 24, 88
Valle, 1999, Selection of the number of principal components: the variance of the reconstruction error criterion compared to other methods, Industrial & Engineering Chemistry Research, 38, 4389, 10.1021/ie990110i
Vapnik, 1998
Wang, 2005, Process monitoring approach using fast moving window pca, Industrial & Engineering Chemistry Research, 44, 5691, 10.1021/ie048873f
Wang, 2003, Recursive partial least squares algorithms for monitoring complex industrial processes, Control Engineering Practice, 11, 613, 10.1016/S0967-0661(02)00096-5
Xie, 2007, Recursive kernel pca and its application in adaptive monitoring of nonlinear processes, Journal of Chemical Industry and Engineering, 58, 1776
Yoo, 2006, Nonlinear multivariate filtering and bioprocess monitoring for supervising nonlinear biological processes, Process Biochemistry, 41, 1854, 10.1016/j.procbio.2006.03.038
Zhang, 2006, Performance monitoring of processes with multiple operating modes through multiple pls models, Journal of Process Control, 16, 763, 10.1016/j.jprocont.2005.12.002
Zhao, 2007, Stage-based soft-transition multiple pca modeling and on-line monitoring strategy for batch processes, Journal of Process Control, 17, 728, 10.1016/j.jprocont.2007.02.005