Block adaptive kernel principal component analysis for nonlinear process monitoring
On‐line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing...
Saved in:
| Published in | AIChE journal Vol. 62; no. 12; pp. 4334 - 4345 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Blackwell Publishing Ltd
01.12.2016
American Institute of Chemical Engineers |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0001-1541 1547-5905 |
| DOI | 10.1002/aic.15347 |
Cover
| Abstract | On‐line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating process, (2) combines the up‐ and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank‐1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of
O(N) and high‐precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time‐varying nonlinear variable interrelationships in process monitoring. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4334–4345, 2016 |
|---|---|
| AbstractList | On‐line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating process, (2) combines the up‐ and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank‐1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of and high‐precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time‐varying nonlinear variable interrelationships in process monitoring. © 2016 American Institute of Chemical Engineers AIChE J , 62: 4334–4345, 2016 On-line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating process, (2) combines the up- and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank-1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of O (N ) and high-precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time-varying nonlinear variable interrelationships in process monitoring. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4334-4345, 2016 On‐line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating process, (2) combines the up‐ and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank‐1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of O(N) and high‐precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time‐varying nonlinear variable interrelationships in process monitoring. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4334–4345, 2016 On-line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating process, (2) combines the up- and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank-1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of [Formulaomitted] and high-precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time-varying nonlinear variable interrelationships in process monitoring. copyright 2016 American Institute of Chemical Engineers AIChE J, 62: 4334-4345, 2016 |
| Author | Zeng, Jiusun Xie, Lei Li, Zhe Kruger, Uwe |
| Author_xml | – sequence: 1 givenname: Lei surname: Xie fullname: Xie, Lei organization: State Key Laboratory of Industrial Control Technology, Zhejiang University, 310027, Hangzhou, P.R. China – sequence: 2 givenname: Zhe surname: Li fullname: Li, Zhe email: lizhe08@zju.edu.cn, lizhe08@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, Zhejiang University, 310027, Hangzhou, P.R. China – sequence: 3 givenname: Jiusun surname: Zeng fullname: Zeng, Jiusun organization: College of Metrology and Measurement Engineering, China Jiliang University, 310018, Hangzhou, P.R. China – sequence: 4 givenname: Uwe surname: Kruger fullname: Kruger, Uwe email: krugeu@rpi.edu, lizhe08@zju.edu.cn organization: Dept. of Biomedical Engineering, Rensselaer Polytechnic Institute, NY, 12180-3590, Troy |
| BookMark | eNqNkUtvEzEURq2qSE0DC_7BSN3AYlp7_JpZtlEJlaoiRCvYWTfOdeXGsYM9AfLvMaSwqKjEyrJ1zn34OyaHMUUk5DWjp4zS7gy8PWWSC31AJkwK3cqBykMyoZSytj6wI3JcykO9dbrvJuTTRUh21cASNqP_hs0Kc8TQbLKP1m8gNDatN7VFHBuIEHbFl8al3NS2wUeEXNFksZRmnaIfU_XuX5IXDkLBV4_nlNy9u7ydvW-vP8yvZufXrZW0163q5EJ1yLjrADoHHDVDB7jEhe5sL7hTyIVcOG5Vb8XQo1JCucXAl3xwdckpebOvW0f4usUymrUvFkOAiGlbDOuVkIoPvfoPVChBqWS6oidP0Ie0zXX3Sg1Us0HrOtuUnO0pm1MpGZ2xfoTRpzhm8MEwan7lYWoe5nce1Xj7xKifvIa8-yf7WP27D7h7HjTnV7M_Rrs3fBnxx18D8soozbU0n2_mZvaxn3-50YOZ858nmas6 |
| CitedBy_id | crossref_primary_10_1016_j_conengprac_2019_07_017 crossref_primary_10_3390_rs11101219 crossref_primary_10_1007_s13042_022_01621_8 crossref_primary_10_1109_TNNLS_2021_3086323 crossref_primary_10_3390_su14031312 crossref_primary_10_1016_j_cherd_2018_12_028 crossref_primary_10_1109_TASE_2018_2865628 crossref_primary_10_1109_TSP_2018_2802445 crossref_primary_10_32604_cmc_2022_028184 crossref_primary_10_1016_j_conengprac_2021_104955 crossref_primary_10_1155_2020_1895341 crossref_primary_10_1016_j_inffus_2020_01_005 crossref_primary_10_3390_pr8010024 crossref_primary_10_1021_acs_iecr_0c02256 crossref_primary_10_1021_acs_iecr_8b05099 crossref_primary_10_1016_j_ces_2021_116851 crossref_primary_10_1016_j_ces_2022_118338 crossref_primary_10_1007_s00500_020_04673_6 crossref_primary_10_1016_j_jfranklin_2022_04_021 crossref_primary_10_1109_TCST_2021_3105540 crossref_primary_10_1016_j_isatra_2017_09_015 crossref_primary_10_1109_ACCESS_2019_2901128 crossref_primary_10_1016_j_chemolab_2021_104369 crossref_primary_10_1109_ACCESS_2017_2767698 crossref_primary_10_1016_j_bspc_2022_103992 crossref_primary_10_1016_j_conengprac_2018_11_020 crossref_primary_10_1109_TASE_2016_2636292 crossref_primary_10_1002_cjce_23249 crossref_primary_10_1016_j_cie_2020_106376 |
| Cites_doi | 10.1016/j.ces.2010.10.008 10.1016/j.neunet.2006.09.012 10.1021/ie9018947 10.1016/j.chemolab.2013.06.013 10.1016/j.conengprac.2013.06.017 10.1016/j.ces.2003.09.012 10.1016/j.ymssp.2011.03.002 10.1016/j.jprocont.2011.07.003 10.1016/j.sigpro.2009.11.001 10.1016/0098-1354(95)00003-K 10.1016/j.chemolab.2009.01.002 10.1016/j.compchemeng.2008.03.011 10.1109/ICSAI.2012.6223652 10.1002/aic.690370209 10.1007/11414353_7 10.1016/j.conengprac.2007.03.007 10.1109/34.877525 10.1016/j.chemolab.2011.12.001 10.1016/S0262-8856(02)00114-2 10.1007/s00521-011-0581-y 10.1016/j.ces.2009.01.050 10.1016/j.ces.2004.07.019 10.1016/j.cie.2011.02.014 10.1002/9780470517253 10.1007/BFb0020217 |
| ContentType | Journal Article |
| Copyright | 2016 American Institute of Chemical Engineers |
| Copyright_xml | – notice: 2016 American Institute of Chemical Engineers |
| DBID | BSCLL AAYXX CITATION 7ST 7U5 8FD C1K L7M SOI |
| DOI | 10.1002/aic.15347 |
| DatabaseName | Istex CrossRef Environment Abstracts Solid State and Superconductivity Abstracts Technology Research Database Environmental Sciences and Pollution Management Advanced Technologies Database with Aerospace Environment Abstracts |
| DatabaseTitle | CrossRef Solid State and Superconductivity Abstracts Technology Research Database Environment Abstracts Advanced Technologies Database with Aerospace Environmental Sciences and Pollution Management |
| DatabaseTitleList | CrossRef Solid State and Superconductivity Abstracts Technology Research Database Environment Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Statistics |
| EISSN | 1547-5905 |
| EndPage | 4345 |
| ExternalDocumentID | 10_1002_aic_15347 AIC15347 ark_67375_WNG_CQ8GXN79_G |
| Genre | article |
| GroupedDBID | -~X .3N .4S .DC .GA .Y3 05W 0R~ 10A 1L6 1OB 1OC 1ZS 23M 31~ 33P 3EH 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5VS 66C 6J9 6P2 6TJ 702 7PT 7XC 8-0 8-1 8-3 8-4 8-5 88I 8FE 8FG 8FH 8G5 8R4 8R5 8UM 8WZ 930 9M8 A03 A6W AAESR AAEVG AAHQN AAIHA AAIKC AAMMB AAMNL AAMNW AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDEX ABDPE ABEML ABIJN ABJCF ABJIA ABJNI ABPVW ABUWG ACAHQ ACBEA ACBWZ ACCZN ACGFO ACGFS ACGOD ACIWK ACNCT ACPOU ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADMLS ADNMO ADOZA ADXAS ADZMN AEFGJ AEGXH AEIGN AEIMD AENEX AEUYN AEUYR AEYWJ AFBPY AFFPM AFGKR AFKRA AFRAH AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AIAGR AIDQK AIDYY AITYG AIURR AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ASPBG ATCPS ATUGU AUFTA AVWKF AZBYB AZFZN AZQEC AZVAB BAFTC BDRZF BENPR BFHJK BGLVJ BHBCM BHPHI BLYAC BMNLL BMXJE BNHUX BPHCQ BROTX BRXPI BSCLL BY8 CCPQU CS3 CZ9 D-E D-F D1I DCZOG DPXWK DR1 DR2 DRFUL DRSTM DWQXO EBS EJD F00 F01 F04 FEDTE G-S G.N GNP GNUQQ GODZA GUQSH H.T H.X HBH HCIFZ HF~ HGLYW HHY HHZ HVGLF HZ~ IX1 J0M JPC KB. KC. KQQ L6V LATKE LAW LC2 LC3 LEEKS LH4 LH6 LITHE LOXES LP6 LP7 LUTES LW6 LYRES M2O M2P M7S MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NDZJH NF~ NNB O66 O9- OIG P2P P2W P2X P4D PALCI PATMY PDBOC PHGZM PHGZT PQGLB PQQKQ PRG PROAC PTHSS PUEGO PYCSY Q.N Q11 Q2X QB0 QRW R.K RIWAO RJQFR RNS ROL RX1 S0X SAMSI SUPJJ TAE TN5 TUS UB1 UHS V2E V8K W8V W99 WBFHL WBKPD WH7 WIB WIH WIK WJL WOHZO WQJ WXSBR WYISQ XG1 XPP XSW XV2 Y6R ZE2 ZZTAW ~02 ~IA ~KM ~WT 3V. AAHHS ACCFJ ADZOD AEEZP AEQDE AEUQT AFPWT AIWBW AJBDE RBB RWI UAO WRC WSB AAYXX CITATION 7ST 7U5 8FD C1K L7M SOI |
| ID | FETCH-LOGICAL-c5087-625b62e13f2aa2fa3e71efaedeb72c843f6e345bf3c68c498e6646fb93d39f153 |
| IEDL.DBID | DR2 |
| ISSN | 0001-1541 |
| IngestDate | Fri Jul 11 16:22:56 EDT 2025 Tue Oct 07 09:25:23 EDT 2025 Fri Jul 25 10:45:24 EDT 2025 Thu Oct 09 00:33:04 EDT 2025 Thu Apr 24 22:53:06 EDT 2025 Wed Jan 22 16:49:22 EST 2025 Sun Sep 21 06:20:10 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c5087-625b62e13f2aa2fa3e71efaedeb72c843f6e345bf3c68c498e6646fb93d39f153 |
| Notes | ark:/67375/WNG-CQ8GXN79-G ArticleID:AIC15347 istex:CADFC0D2B766B3F19110689F37E58D231E426F28 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PQID | 1907197784 |
| PQPubID | 7879 |
| PageCount | 12 |
| ParticipantIDs | proquest_miscellaneous_1864563986 proquest_miscellaneous_1846400517 proquest_journals_1907197784 crossref_citationtrail_10_1002_aic_15347 crossref_primary_10_1002_aic_15347 wiley_primary_10_1002_aic_15347_AIC15347 istex_primary_ark_67375_WNG_CQ8GXN79_G |
| PublicationCentury | 2000 |
| PublicationDate | December 2016 |
| PublicationDateYYYYMMDD | 2016-12-01 |
| PublicationDate_xml | – month: 12 year: 2016 text: December 2016 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | AIChE journal |
| PublicationTitleAlternate | AIChE J |
| PublicationYear | 2016 |
| Publisher | Blackwell Publishing Ltd American Institute of Chemical Engineers |
| Publisher_xml | – name: Blackwell Publishing Ltd – name: American Institute of Chemical Engineers |
| References | Zvokelj M, Zupan S, Prebil I. Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method. Mech Syst Signal Process. 2011;25:2631-2653. Jiang QC, Yan XF. Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring. Chemometr Intell Lab Syst. 2013;127(0):121-131. Khediri IB, Limam M, Weihs C. Variable window adaptive kernel principal component analysis for nonlinear nonstationary process monitoring. Comput Ind Eng. 2011;61(3):437-446. Ding M, Tian Z, Xu H. Adaptive kernel principal component analysis. Signal process. 90(5):1542-1553, 2010. Kruger U, Xie L. Statistical Monitoring of Complex Multivariate Processes. Chichester, U. K.: Wiley, 2012. Alcala CF, Qin SJ. Reconstruction-based contribution for process monitoring with kernel principal component analysis. Ind Eng Chem Res. 2010;49:7849-7857. Hall P, Marshall D, Martin R. Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image Vision Comput. 2002;20(13-14):1009-1016. Mika S, Schölkopf B, Smola A, Müller KR, Scholz M, Rätsch G. Kernel pca and de-noising in feature space. Neural Inf Process Syst. 1998;11:536-542. Zhang YW, Chi M. Fault diagnosis of nonlinear processes using multiscale kpca and multiscale kpls. Chem Eng Sci. 2011;66(1):64-72. Franc V, Hlavac V. Greedy kernel principal component analysis. Lecture Notes Comput Sci. 2006;3948:87-105. Hall P, Marshall D, Martin R. Merging and splitting eigenspace models. IEEE Trans Pattern Anal Mach Intell. 2000;22(9):1042-1049. Dong D, McAvoy TJ. Nonlinear principal component analysis-based on principal curves and neural networks. Comput Chem Eng. 1996;20(1):65-78. Hoegaerts L, Lathauwer LD, Goethals I, Suykens JAK, Vandewalle J, De Moor B. Efficiently updating and tracking the dominant kernel principal components. Neural Networks 2007;20(2):220-229. Kramer MA. Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 1991;37(3):233-243. Kampjarvi P, Sourander M, Komulainen T, Vatanski N, Nikus M, Jämsä-Jounela SL. Fault detection and isolation of an on-line analyzer for an ethylene cracking process. Contr Eng Prac. 2008;16(1):1-13. Liu XQ, Li K, McAfee M, Deng J. Improved nonlinear pca for process monitoring using support vector data description. J Process Contr. 2011;21(9):1306-1317. Fan JC, Qin SJ, Wang YQ. Online monitoring of nonlinear multivariate industrial processes using filtering kica-pca. Contr Eng Prac. 2014;22:205-216. Liu X, Kruger U, Littler T, Xie L, Wang SQ. Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemometr Intell Lab Syst. 2009;96(2):132-143. Ge ZQ, Yang CJ, Song ZH. Improved kernelpca-based monitoring approach for nonlinear processes. Chem Eng Sci. 2009;64(0):2245-2255. Lee JM, Yoo C, Choi SW, Vanrolleghem PA, Lee IB. Nonlinear process monitoring using kernel principal component analysis. Chem Eng Sci. 2004;59(1):223-234. Liu XQ, Li K, McAfee M, Deng J. Application of nonlinear pca for fault detection in polymer extrusion processes. Neural Comput Appl. 2012;21(6):1141-1148. Choi SW, Lee IB. Nonlinear dynamic process monitoring based on dynamic kernel pca. Chem Eng Sci. 2004;59(0):5897-5908. Zhang J, Yang X. Multivariate Statistical Process Control. Beijing, China: Chemical Industry, 2000. Kettunen M, Zhang P, Jämsä-Jounela SL. An embedded fault detection, isolation and accommodation system in a model predictive controller for an industrial benchmark process. Comput Chem Eng. 2008;32(12):2966-2985. Vapnik VN. Statistical Learning Theory. New York: Wiley, 1998. Zhang Z, Song K, Tong TP, Wu F. A novel nonlinear adaptive mooney-viscosity model based on drpls-gp algorithm for rubber mixing process. Chemometr Intell Lab Syst. 2012;112:17-23. 1991; 37 2009; 64 2012 2011 2010 2013; 127 2006; 3948 2000; 22 2008; 16 2011; 61 2009 1998 1997 2008; 32 2014; 22 2010; 49 2009; 96 2012; 112 2002; 20 2000 2004; 59 2011; 66 2011; 21 2011; 25 2013 2007; 20 2010; 90 2012; 21 1996; 20 1998; 11 Ozawa S (e_1_2_13_19_1) 2010 e_1_2_13_24_1 e_1_2_13_27_1 e_1_2_13_26_1 e_1_2_13_21_1 e_1_2_13_20_1 e_1_2_13_23_1 e_1_2_13_9_1 e_1_2_13_8_1 e_1_2_13_7_1 e_1_2_13_6_1 Ogawa T (e_1_2_13_25_1) 2011 Zhang J (e_1_2_13_34_1) 2000 Oh CK (e_1_2_13_18_1) 2010 e_1_2_13_17_1 e_1_2_13_13_1 e_1_2_13_14_1 e_1_2_13_16_1 e_1_2_13_32_1 e_1_2_13_10_1 e_1_2_13_11_1 e_1_2_13_12_1 e_1_2_13_33_1 Washizawa Y. (e_1_2_13_15_1) 2009 Vapnik VN. (e_1_2_13_30_1) 1998 e_1_2_13_5_1 Chakour C (e_1_2_13_22_1) 2013 e_1_2_13_4_1 e_1_2_13_3_1 e_1_2_13_2_1 Mika S (e_1_2_13_31_1) 1998; 11 e_1_2_13_29_1 e_1_2_13_28_1 |
| References_xml | – reference: Liu X, Kruger U, Littler T, Xie L, Wang SQ. Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemometr Intell Lab Syst. 2009;96(2):132-143. – reference: Liu XQ, Li K, McAfee M, Deng J. Improved nonlinear pca for process monitoring using support vector data description. J Process Contr. 2011;21(9):1306-1317. – reference: Fan JC, Qin SJ, Wang YQ. Online monitoring of nonlinear multivariate industrial processes using filtering kica-pca. Contr Eng Prac. 2014;22:205-216. – reference: Kramer MA. Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 1991;37(3):233-243. – reference: Choi SW, Lee IB. Nonlinear dynamic process monitoring based on dynamic kernel pca. Chem Eng Sci. 2004;59(0):5897-5908. – reference: Hoegaerts L, Lathauwer LD, Goethals I, Suykens JAK, Vandewalle J, De Moor B. Efficiently updating and tracking the dominant kernel principal components. Neural Networks 2007;20(2):220-229. – reference: Zhang YW, Chi M. Fault diagnosis of nonlinear processes using multiscale kpca and multiscale kpls. Chem Eng Sci. 2011;66(1):64-72. – reference: Hall P, Marshall D, Martin R. Merging and splitting eigenspace models. IEEE Trans Pattern Anal Mach Intell. 2000;22(9):1042-1049. – reference: Zvokelj M, Zupan S, Prebil I. Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method. Mech Syst Signal Process. 2011;25:2631-2653. – reference: Ding M, Tian Z, Xu H. Adaptive kernel principal component analysis. Signal process. 90(5):1542-1553, 2010. – reference: Franc V, Hlavac V. Greedy kernel principal component analysis. Lecture Notes Comput Sci. 2006;3948:87-105. – reference: Ge ZQ, Yang CJ, Song ZH. Improved kernelpca-based monitoring approach for nonlinear processes. Chem Eng Sci. 2009;64(0):2245-2255. – reference: Mika S, Schölkopf B, Smola A, Müller KR, Scholz M, Rätsch G. Kernel pca and de-noising in feature space. Neural Inf Process Syst. 1998;11:536-542. – reference: Jiang QC, Yan XF. Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring. Chemometr Intell Lab Syst. 2013;127(0):121-131. – reference: Dong D, McAvoy TJ. Nonlinear principal component analysis-based on principal curves and neural networks. Comput Chem Eng. 1996;20(1):65-78. – reference: Hall P, Marshall D, Martin R. Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image Vision Comput. 2002;20(13-14):1009-1016. – reference: Kampjarvi P, Sourander M, Komulainen T, Vatanski N, Nikus M, Jämsä-Jounela SL. Fault detection and isolation of an on-line analyzer for an ethylene cracking process. Contr Eng Prac. 2008;16(1):1-13. – reference: Zhang Z, Song K, Tong TP, Wu F. A novel nonlinear adaptive mooney-viscosity model based on drpls-gp algorithm for rubber mixing process. Chemometr Intell Lab Syst. 2012;112:17-23. – reference: Lee JM, Yoo C, Choi SW, Vanrolleghem PA, Lee IB. Nonlinear process monitoring using kernel principal component analysis. Chem Eng Sci. 2004;59(1):223-234. – reference: Zhang J, Yang X. Multivariate Statistical Process Control. Beijing, China: Chemical Industry, 2000. – reference: Liu XQ, Li K, McAfee M, Deng J. Application of nonlinear pca for fault detection in polymer extrusion processes. Neural Comput Appl. 2012;21(6):1141-1148. – reference: Alcala CF, Qin SJ. Reconstruction-based contribution for process monitoring with kernel principal component analysis. Ind Eng Chem Res. 2010;49:7849-7857. – reference: Vapnik VN. Statistical Learning Theory. New York: Wiley, 1998. – reference: Kettunen M, Zhang P, Jämsä-Jounela SL. An embedded fault detection, isolation and accommodation system in a model predictive controller for an industrial benchmark process. Comput Chem Eng. 2008;32(12):2966-2985. – reference: Kruger U, Xie L. Statistical Monitoring of Complex Multivariate Processes. Chichester, U. K.: Wiley, 2012. – reference: Khediri IB, Limam M, Weihs C. Variable window adaptive kernel principal component analysis for nonlinear nonstationary process monitoring. Comput Ind Eng. 2011;61(3):437-446. – volume: 22 start-page: 1042 issue: 9 year: 2000 end-page: 1049 article-title: Merging and splitting eigenspace models publication-title: IEEE Trans Pattern Anal Mach Intell. – volume: 22 start-page: 205 year: 2014 end-page: 216 article-title: Online monitoring of nonlinear multivariate industrial processes using filtering kica‐pca publication-title: Contr Eng Prac. – volume: 11 start-page: 536 year: 1998 end-page: 542 article-title: Kernel pca and de‐noising in feature space publication-title: Neural Inf Process Syst. – volume: 20 start-page: 220 issue: 2 year: 2007 end-page: 229 article-title: Efficiently updating and tracking the dominant kernel principal components publication-title: Neural Networks – volume: 21 start-page: 1141 issue: 6 year: 2012 end-page: 1148 article-title: Application of nonlinear pca for fault detection in polymer extrusion processes publication-title: Neural Comput Appl. – start-page: 432 year: 2012 end-page: 438 – volume: 21 start-page: 1306 issue: 9 year: 2011 end-page: 1317 article-title: Improved nonlinear pca for process monitoring using support vector data description publication-title: J Process Contr. – volume: 49 start-page: 7849 year: 2010 end-page: 7857 article-title: Reconstruction‐based contribution for process monitoring with kernel principal component analysis publication-title: Ind Eng Chem Res. – start-page: 1133 year: 2011 end-page: 1136 – volume: 20 start-page: 65 issue: 1 year: 1996 end-page: 78 article-title: Nonlinear principal component analysis‐based on principal curves and neural networks publication-title: Comput Chem Eng. – volume: 20 start-page: 1009 issue: 13‐14 year: 2002 end-page: 1016 article-title: Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition publication-title: Image Vision Comput. – volume: 112 start-page: 17 year: 2012 end-page: 23 article-title: A novel nonlinear adaptive mooney‐viscosity model based on drpls‐gp algorithm for rubber mixing process publication-title: Chemometr Intell Lab Syst. – year: 2000 – volume: 37 start-page: 233 issue: 3 year: 1991 end-page: 243 article-title: Nonlinear principal component analysis using autoassociative neural networks publication-title: AIChE J. – start-page: 1 year: 2013 end-page: 6 – volume: 64 start-page: 2245 issue: 0 year: 2009 end-page: 2255 article-title: Improved kernelpca‐based monitoring approach for nonlinear processes publication-title: Chem Eng Sci. – volume: 25 start-page: 2631 year: 2011 end-page: 2653 article-title: Non‐linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method publication-title: Mech Syst Signal Process. – volume: 96 start-page: 132 issue: 2 year: 2009 end-page: 143 article-title: Moving window kernel PCA for adaptive monitoring of nonlinear processes publication-title: Chemometr Intell Lab Syst. – start-page: 487 year: 2010 end-page: 497 – year: 1998 – year: 2012 – volume: 90 start-page: 1542 issue: 5 year: 2010 end-page: 1553 article-title: Adaptive kernel principal component analysis publication-title: Signal process. – volume: 59 start-page: 5897 issue: 0 year: 2004 end-page: 5908 article-title: Nonlinear dynamic process monitoring based on dynamic kernel pca publication-title: Chem Eng Sci. – volume: 16 start-page: 1 issue: 1 year: 2008 end-page: 13 article-title: Fault detection and isolation of an on‐line analyzer for an ethylene cracking process publication-title: Contr Eng Prac. – start-page: 1 year: 2009 end-page: 6 – volume: 66 start-page: 64 issue: 1 year: 2011 end-page: 72 article-title: Fault diagnosis of nonlinear processes using multiscale kpca and multiscale kpls publication-title: Chem Eng Sci. – volume: 32 start-page: 2966 issue: 12 year: 2008 end-page: 2985 article-title: An embedded fault detection, isolation and accommodation system in a model predictive controller for an industrial benchmark process publication-title: Comput Chem Eng. – volume: 59 start-page: 223 issue: 1 year: 2004 end-page: 234 article-title: Nonlinear process monitoring using kernel principal component analysis publication-title: Chem Eng Sci. – volume: 127 start-page: 121 issue: 0 year: 2013 end-page: 131 article-title: Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring publication-title: Chemometr Intell Lab Syst – year: 1997 – volume: 61 start-page: 437 issue: 3 year: 2011 end-page: 446 article-title: Variable window adaptive kernel principal component analysis for nonlinear nonstationary process monitoring publication-title: Comput Ind Eng. – volume: 3948 start-page: 87 year: 2006 end-page: 105 article-title: Greedy kernel principal component analysis publication-title: Lecture Notes Comput Sci. – start-page: 1865 year: 2010 end-page: 1871 – ident: e_1_2_13_14_1 doi: 10.1016/j.ces.2010.10.008 – ident: e_1_2_13_27_1 doi: 10.1016/j.neunet.2006.09.012 – ident: e_1_2_13_3_1 doi: 10.1021/ie9018947 – ident: e_1_2_13_6_1 doi: 10.1016/j.chemolab.2013.06.013 – ident: e_1_2_13_11_1 doi: 10.1016/j.conengprac.2013.06.017 – ident: e_1_2_13_32_1 doi: 10.1016/j.ces.2003.09.012 – start-page: 1 volume-title: 2013 9th ASIAN CONTROL CONFERENCE (ASCC) year: 2013 ident: e_1_2_13_22_1 – ident: e_1_2_13_4_1 doi: 10.1016/j.ymssp.2011.03.002 – ident: e_1_2_13_12_1 doi: 10.1016/j.jprocont.2011.07.003 – start-page: 1865 volume-title: Probabilistic kernel principal component analysis for monitoring a suspension bridge under environmental variations year: 2010 ident: e_1_2_13_18_1 – ident: e_1_2_13_24_1 doi: 10.1016/j.sigpro.2009.11.001 – start-page: 1133 volume-title: 18th IEEE International Conference on Image Processing (ICIP) year: 2011 ident: e_1_2_13_25_1 – volume-title: Statistical Learning Theory year: 1998 ident: e_1_2_13_30_1 – ident: e_1_2_13_9_1 doi: 10.1016/0098-1354(95)00003-K – ident: e_1_2_13_26_1 doi: 10.1016/j.chemolab.2009.01.002 – ident: e_1_2_13_7_1 doi: 10.1016/j.compchemeng.2008.03.011 – ident: e_1_2_13_21_1 doi: 10.1109/ICSAI.2012.6223652 – volume: 11 start-page: 536 year: 1998 ident: e_1_2_13_31_1 article-title: Kernel pca and de‐noising in feature space publication-title: Neural Inf Process Syst. – ident: e_1_2_13_8_1 doi: 10.1002/aic.690370209 – ident: e_1_2_13_16_1 doi: 10.1007/11414353_7 – start-page: 1 volume-title: Subset kernel principal component analysis year: 2009 ident: e_1_2_13_15_1 – ident: e_1_2_13_2_1 doi: 10.1016/j.conengprac.2007.03.007 – ident: e_1_2_13_28_1 doi: 10.1109/34.877525 – volume-title: Multivariate Statistical Process Control year: 2000 ident: e_1_2_13_34_1 – start-page: 487 volume-title: A fast incremental kernel principal component analysis for online feature extraction year: 2010 ident: e_1_2_13_19_1 – ident: e_1_2_13_5_1 doi: 10.1016/j.chemolab.2011.12.001 – ident: e_1_2_13_29_1 doi: 10.1016/S0262-8856(02)00114-2 – ident: e_1_2_13_13_1 doi: 10.1007/s00521-011-0581-y – ident: e_1_2_13_20_1 doi: 10.1016/j.ces.2009.01.050 – ident: e_1_2_13_17_1 doi: 10.1016/j.ces.2004.07.019 – ident: e_1_2_13_23_1 doi: 10.1016/j.cie.2011.02.014 – ident: e_1_2_13_33_1 doi: 10.1002/9780470517253 – ident: e_1_2_13_10_1 doi: 10.1007/BFb0020217 |
| SSID | ssj0012782 |
| Score | 2.366472 |
| Snippet | On‐line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In... On-line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In... |
| SourceID | proquest crossref wiley istex |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 4334 |
| SubjectTerms | Adaptation Adaptive algorithms Algorithms block adaptive Kernel principal component analysis Complexity Computation Computer applications Computing time Eigenvalues Eigenvectors Gram matrix iterative algorithm approach Iterative methods Kernels Mathematical models Monitoring Nonlinearity On-line systems online monitoring Principal components analysis Series (mathematics) Statistical methods Statistics |
| Title | Block adaptive kernel principal component analysis for nonlinear process monitoring |
| URI | https://api.istex.fr/ark:/67375/WNG-CQ8GXN79-G/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1002%2Faic.15347 https://www.proquest.com/docview/1907197784 https://www.proquest.com/docview/1846400517 https://www.proquest.com/docview/1864563986 |
| Volume | 62 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1547-5905 dateEnd: 20241105 omitProxy: false ssIdentifier: ssj0012782 issn: 0001-1541 databaseCode: ADMLS dateStart: 20120601 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVWIB databaseName: Wiley Online Library - Core collection (SURFmarket) issn: 0001-1541 databaseCode: DR2 dateStart: 19980101 customDbUrl: isFulltext: true eissn: 1547-5905 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012782 providerName: Wiley-Blackwell |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3daxQxEA-lvuiD1i-8WiWKiC97vU2ySZY-1cNeFTzwo3gPQkiyE5Cz1-N6B8W_vpNkd21FRXxb2MnmYzKZ32QnvxDygnFfhYpB0dSgCsFDKHQTRoW2GDNDXTcO4j7k-6k8PhHvZtVsixx0Z2EyP0S_4RYtI63X0cCtO9__SRpqv_khmquIJ8lLLlM49bGnjiqZ0pkpHMNlhAllxyo0Yvt9yWu-6EYc1otrQPMqXE3-5ugO-dq1NKeZzIebtRv6H7-QOP5nV3bI7RaH0sM8ce6SLVjcI7eusBPeJ59eo6ObU9vYZVwT6RxWWBFd5u15LBzT0c8W6LWobalNKEJgushNsiu6zKcQ6GlaOOJXH5CTozefx8dFewdD4RG6qQLDIycZlDwwa1mwHFQJwUIDTjGvUbkSuKhc4F5qL2oNUgoZXM0bXgfs1EOyjdXCI0JLFRxiee-ZBiEUWBSwIwu1rljjeDMgrzptGN8SlMd7Mr6bTK3MDI6TSeM0IM970WVm5fid0Muk0l7CruYxjU1V5st0YsYf9GQ2VbWZDMhep3PTWvC5QaCkSgTHWgzIs_412l78oWIXcLZBGQRvIrGc_U1GIkbltZbYvzQJ_txic_h2nB52_130MbmJGE7mDJs9sr1ebeAJ4qS1e5oM4hINsA9J |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwED-N7QF44BtRGGAQQryka2zHHxIvo2LtYKsEbKIvk-U4toS6dVVpJcRfzzlOwoYAId4i5Rx_nM_3O-f8M8ALylwRCuqzSnuZcRZCpqowyJTFmNlrXZU-7kMeTsT4mL-bFtMNeN2ehUn8EN2GW7SMer2OBh43pHd-sobaL66P9srlFdjiAuOUCIk-duRROZUqcYVjwIxAIW95hQZ0pyt6yRttxYH9dglqXgSstcfZuwknbVtTosmsv16Vfff9FxrH_-3MLbjRQFGym-bObdjw8ztw_QJB4V349AZ93YzYyi7iskhmfok1kUXaocfCMSP9fI6Oi9iG3YQgCibz1Ca7JIt0EIGc1WtH_Oo9ON57ezQcZ801DJlD9CYzjJBKQX3OArWWBsu8zH2wvvKlpE6hfoVnvCgDc0I5rpUXgotQalYxHbBT92ETq_UPgOQylAjnnaPKcy69RQE7sF6rglYlq3rwqlWHcQ1Hebwq49QkdmVqcJxMPU49eN6JLhIxx--EXtY67STschYz2WRhPk9GZvhBjaYTqc2oB9ut0k1jxF8NYiWZIz5WvAfPutdofvGfip378zXKqDj1ItHZ32QEwlSmlcD-1bPgzy02u_vD-uHhv4s-havjo8MDc7A_ef8IriGkEynhZhs2V8u1f4ywaVU-qa3jBwCyE2o |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwELfGJiF44BtR2MAghHhJ19iO7Uh72TrajY-Kr2l9QZbjnKWpo4tKKyH--p3jJGwIEOItUs7xx_l8P1_OPxPynHGX-YxBUuagEsG9T3TpB4m2uGeGPC8LCHHIdxN5cCReT7PpGtlpz8JEfogu4BYso16vg4FDVfrtn6yh9sT10V6FukI2RJbrkNC3_7Ejj0qZ0pErHDfMCBTSlldowLa7ope80UYY2O-XoOZFwFp7nNFN8qVta0w0mfVXy6LvfvxC4_i_nblFbjRQlO7GuXObrMH8Drl-gaDwLvm0h75uRm1pq7As0hkssCZaxQg9Fg4Z6WdzdFzUNuwmFFEwncc22QWt4kEE-rVeO8JX75Gj0avPw4OkuYYhcYjeVII7pEIySLln1jJvOagUvIUSCsWcRv1K4CIrPHdSO5FrkFJIX-S85LnHTt0n61gtPCA0Vb5AOO8c0yCEAosCdmABlcfKgpc98rJVh3ENR3m4KuPURHZlZnCcTD1OPfKsE60iMcfvhF7UOu0k7GIWMtlUZo4nYzP8oMfTicrNuEc2W6Wbxoi_GcRKKkV8rEWPPO1eo_mFfyp2DmcrlEH8Jmqis7_JSISpPNcS-1fPgj-32OweDuuHh_8u-oRcfb8_Mm8PJ28ekWuI6GTMt9kk68vFCrYQNS2Lx7VxnAOOsBLu |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Block+adaptive+kernel+principal+component+analysis+for+nonlinear+process+monitoring&rft.jtitle=AIChE+journal&rft.au=Xie%2C+Lei&rft.au=Li%2C+Zhe&rft.au=Zeng%2C+Jiusun&rft.au=Kruger%2C+Uwe&rft.date=2016-12-01&rft.issn=0001-1541&rft.eissn=1547-5905&rft.volume=62&rft.issue=12&rft.spage=4334&rft.epage=4345&rft_id=info:doi/10.1002%2Faic.15347&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_aic_15347 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0001-1541&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0001-1541&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0001-1541&client=summon |