Articles & Issues
- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
-
Received October 12, 2012
Accepted March 3, 2013
-
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © KIChE. All rights reserved.
All issues
Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure
Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China 1State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
Korean Journal of Chemical Engineering, June 2013, 30(6), 1181-1186(6), 10.1007/s11814-013-0034-7
Download PDF
Abstract
Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And, the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.
References
Jackson JE, Technometrics., 1, 359 (1959)
Qin SJ, J. Chemometr., 17, 480 (2003)
Kresta JV, Macgregor JF, Marlin TE, Can. J. Chem. Eng., 69, 35 (2009)
Lee JM, Yoo CK, Choi SW, Vanrolleghem PA, Lee IB, Chem. Eng. Sci., 59(1), 223 (2004)
Liu XQ, Xie L, Kruger U, Littler T, Wang SQ, AIChE J., 54(9), 2379 (2008)
Nomikos P, Macgregor JF, AIChE J., 40(8), 1361 (1994)
Pei XD, Yamashita Y, Yoshida M, Matsumoto S, J. Chem. Eng. Jpn., 41(1), 25 (2008)
Kim MH, Yoo CK, Korean J. Chem. Eng., 25(5), 947 (2008)
Han K, Park KJ, Chae H, Yoon ES, Korean J. Chem. Eng., 25(1), 13 (2008)
Chiang LH, Russell E, Braatz RD, Fault detection and diagnosis in industrial systems, Springer Verlag (2001)
Ge ZQ, Yang CJ, Song ZH, Chem. Eng. Sci., 64(9), 2245 (2009)
Kramer MA, AIChE J., 37, 233 (1991)
Dong D, Mcavoy TJ, Comput. Chem. Eng., 20(1), 65 (1996)
Cui P, Li J, Wang G, Expert Syst. Appl., 34, 1210 (2008)
Jia F, Martin E, Morris A, Int. J. Syst. Sci., 31, 1473 (2000)
Choi SW, Lee C, Lee JM, Park JH, Lee IB, Chemom.Intell. Lab. Syst., 75, 55 (2005)
Nguyen VH, Golinval JC, Eng. Struct., 32, 3683 (2010)
Scholkopf B, Smola A, Muller KR, Neural Computation., 10, 1299 (1998)
Jenssen R, IEEE PAMI., 32, 847 (2010)
Jenssen R, Eltoft T, Neurocomputing., 72, 23 (2008)
Renyi A, On measures of entropy and information, in Proc. Fourth Berkeley Symp. on Math. Statist. and Prob., Univ. of Calif. Press, 1, 547 (1961)
Parzen E, The Annals of Mathematical Statistics., 33, 1065 (1962)
Dehnad K, Technometrics., 29, 495 (1987)
Scott DW, Density estimation, Encyclopedia of Biostatistics (2005)
Webb AR, Statistical pattern recognition, Wiley (2003)
Yoon SY, MacGregor JF, J. Process Control, 11(4), 387 (2001)
Yue HH, Qin SJ, Ind. Eng. Chem. Res., 40(20), 4403 (2001)
Qin SJ, J. Chemometr., 17, 480 (2003)
Kresta JV, Macgregor JF, Marlin TE, Can. J. Chem. Eng., 69, 35 (2009)
Lee JM, Yoo CK, Choi SW, Vanrolleghem PA, Lee IB, Chem. Eng. Sci., 59(1), 223 (2004)
Liu XQ, Xie L, Kruger U, Littler T, Wang SQ, AIChE J., 54(9), 2379 (2008)
Nomikos P, Macgregor JF, AIChE J., 40(8), 1361 (1994)
Pei XD, Yamashita Y, Yoshida M, Matsumoto S, J. Chem. Eng. Jpn., 41(1), 25 (2008)
Kim MH, Yoo CK, Korean J. Chem. Eng., 25(5), 947 (2008)
Han K, Park KJ, Chae H, Yoon ES, Korean J. Chem. Eng., 25(1), 13 (2008)
Chiang LH, Russell E, Braatz RD, Fault detection and diagnosis in industrial systems, Springer Verlag (2001)
Ge ZQ, Yang CJ, Song ZH, Chem. Eng. Sci., 64(9), 2245 (2009)
Kramer MA, AIChE J., 37, 233 (1991)
Dong D, Mcavoy TJ, Comput. Chem. Eng., 20(1), 65 (1996)
Cui P, Li J, Wang G, Expert Syst. Appl., 34, 1210 (2008)
Jia F, Martin E, Morris A, Int. J. Syst. Sci., 31, 1473 (2000)
Choi SW, Lee C, Lee JM, Park JH, Lee IB, Chemom.Intell. Lab. Syst., 75, 55 (2005)
Nguyen VH, Golinval JC, Eng. Struct., 32, 3683 (2010)
Scholkopf B, Smola A, Muller KR, Neural Computation., 10, 1299 (1998)
Jenssen R, IEEE PAMI., 32, 847 (2010)
Jenssen R, Eltoft T, Neurocomputing., 72, 23 (2008)
Renyi A, On measures of entropy and information, in Proc. Fourth Berkeley Symp. on Math. Statist. and Prob., Univ. of Calif. Press, 1, 547 (1961)
Parzen E, The Annals of Mathematical Statistics., 33, 1065 (1962)
Dehnad K, Technometrics., 29, 495 (1987)
Scott DW, Density estimation, Encyclopedia of Biostatistics (2005)
Webb AR, Statistical pattern recognition, Wiley (2003)
Yoon SY, MacGregor JF, J. Process Control, 11(4), 387 (2001)
Yue HH, Qin SJ, Ind. Eng. Chem. Res., 40(20), 4403 (2001)
