Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure
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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.
Key words
Kernel Entropy Component Analysis Process Monitoring Fault Detection Angular StructurePreview
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References
- 1.J. E. Jackson, Technometrics, 1, 359 (1959).CrossRefGoogle Scholar
- 2.S. J. Qin, J. Chemometr., 17, 480 (2003).CrossRefGoogle Scholar
- 3.J.V. Kresta, J. F. Macgregor and T. E. Marlin, Can. J. Chem. Eng., 69, 35 (2009).CrossRefGoogle Scholar
- 4.J.M. Lee, C. K. Yoo, S.W. Choi, P. A. Vanrolleghem and I. B. Lee, Chem. Eng. Sci., 59, 223 (2004).CrossRefGoogle Scholar
- 5.X. Liu, L. Xie, U. Kruger, T. Littler and S. Wang, AIChE J., 54, 2379 (2008).CrossRefGoogle Scholar
- 6.P. Nomikos and J. F. MacGregor, AIChE J., 40, 1361 (1994).CrossRefGoogle Scholar
- 7.X. Pei, Y. Yamashita, M. Yoshida and S. Matsumoto, JCEJ, 41, 25 (2008).CrossRefGoogle Scholar
- 8.M. H. Kim and C. K. Yoo, Korean J. Chem. Eng., 25, 947 (2008).CrossRefGoogle Scholar
- 9.K. Han, K. J. Park, H. Chae and E. S. Yoon, Korean J. Chem. Eng., 25, 13 (2008).CrossRefGoogle Scholar
- 10.L. H. Chiang, E. Russell and R. D. Braatz, Fault detection and diagnosis in industrial systems, Springer Verlag (2001).CrossRefGoogle Scholar
- 11.Z. Ge, C. Yang and Z. Song, Chem. Eng. Sci., 64, 2245 (2009).CrossRefGoogle Scholar
- 12.M. A. Kramer, AIChE J., 37, 233 (1991).CrossRefGoogle Scholar
- 13.D. Dong and T. J. McAvoy, Comput. Chem. Eng., 20, 65 (1996).CrossRefGoogle Scholar
- 14.P. Cui, J. Li and G. Wang, Expert Syst. Appl., 34, 1210 (2008).CrossRefGoogle Scholar
- 15.F. Jia, E. Martin and A. Morris, Int. J. Syst. Sci., 31, 1473 (2000).CrossRefGoogle Scholar
- 16.S.W. Choi, C. Lee, J. M. Lee, J. H. Park and I. B. Lee, Chemom. Intell. Lab. Syst., 75, 55 (2005).CrossRefGoogle Scholar
- 17.V. H. Nguyen and J. C. Golinval, Eng. Struct., 32, 3683 (2010).CrossRefGoogle Scholar
- 18.B. Schölkopf, A. Smola and K.R. Müller, Neural Computation, 10, 1299 (1998).CrossRefGoogle Scholar
- 19.R. Jenssen, IEEE PAMI., 32, 847 (2010).CrossRefGoogle Scholar
- 20.R. Jenssen and T. Eltoft, Neurocomputing, 72, 23 (2008).CrossRefGoogle Scholar
- 21.A. Renyi, On measures of entropy and information, in Proc. Fourth Berkeley Symp. on Math. Statist. and Prob., Univ. of Calif. Press, 1, 547 (1961).Google Scholar
- 22.E. Parzen, The Annals of Mathematical Statistics, 33, 1065 (1962).CrossRefGoogle Scholar
- 23.K. Dehnad, Technometrics, 29, 495 (1987).CrossRefGoogle Scholar
- 24.D.W. Scott, Density estimation, Encyclopedia of Biostatistics (2005).Google Scholar
- 25.A. R. Webb, Statistical pattern recognition, Wiley (2003).Google Scholar
- 26.S. Yoon and J. F. MacGregor, J. Process Control, 11, 387 (2001).CrossRefGoogle Scholar
- 27.H. H. Yue and S. J. Qin, Ind. Eng. Chem. Res., 40, 4403 (2001).CrossRefGoogle Scholar