Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS

Chemical Engineering Science

Volumn 66, Issue 1, 2011, Pages 64-72

  Zhang, Yingwei ^a   Ma, Chi ^a  

^a KEY LABORATORY OF INTEGRATED AUTOMATION OF PROCESS INDUSTRY   (China)

Author keywords

Control; Design; Fault detection; Fault diagnosis; Multiscale modeling; Process monitoring

Indexed keywords

CONTINUOUS ANNEALING PROCESS; CONTRIBUTION PLOTS; DIFFERENT SCALE; FAULT DIAGNOSIS; KERNEL PRINCIPAL COMPONENT ANALYSIS; MULTI-SCALE MODELING; MULTISCALES; NEW APPROACHES; NONLINEAR FAULTS; NONLINEAR PROCESS; NONLINEAR PROCESS MONITORING; NONLINEAR SCALE; PROCESS VARIABLES;

DIAGNOSIS; FAULT DETECTION; MAGNESIUM; PRINCIPAL COMPONENT ANALYSIS; PROCESS MONITORING;

PROCESS CONTROL;

EID: 78149468553     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2010.10.008     Document Type: Article

References (31)

1. Aradhye H.B., Bakshi B.R., Strauss R.A., Davis J.F. Multiscale SPC using wavelets: theoretical analysis and properties. A.I.Ch.E. J. 2003, 49(4):939-958.
2. Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring. A.I.Ch.E. J. 1998, 44(7):1596-1610.
3. Chen J.H., Liao C.M. Dynamic process fault monitoring based on neural network and PCA. J. Process Control 2002, 12:277-289.
4. Chiang L.H., Russell F.L., Braatz R.D. Fault Detection and Diagnosis in Industrial Systems 2001, Springer, London.
5. Cho J.H., Lee J.M., Choi S.W., Lee D., Lee I.B. Fault identification for process monitoring using kernel principal component analysis. Chem. Eng. Sci. 2005, 60:279-288.
6. Choi S.W., Lee D., Park J.H., Lee I.B. Nonlinear regression using RBFN with linear submodels. Chemom. Intell. Lab. Syst. 2003, 65:191-208.
7. Choi S.W., Lee C., Lee J.M., Park J.H., Lee I.-B. Fault detection and identification of nonlinear processes based on KPCA. Chemom. Intell. Lab. Syst. 2005, 75:55-67.
8. Dong D., McAvoy T.J. Nonlinear principal component analysis-based on principal curves and neural networks. Comput. Chem. Eng. 1996, 20:65-78.
9. Geng Z.Q., Zhu Q.X. Multiscale nonlinear principal component analysis (NLPCA) and its application for chemical process monitoring. Ind. Eng. Chem. Res. 2005, 44:3585-3593.
10. Hiden H.G., Willis M.J., Tham M.T., Montague G.A. Non-linear principal components analysis using genetic programming. Comput. Chem. Eng. 1999, 23:413-425.
11. Jia F., Martin E.B., Morris A.J. Non-linear principal component analysis for process fault detection. Comput. Chem. Eng. 1998, 22:S851-S854.
12. Kramer M.A. Non-linear principal component analysis using autoassociative neural networks. AIChE J. 1991, 37:233-243.
13. Kulkarni S.G., Chaudhary A.K., Nandi S., Tambe S.S., Kulkarni B.D. Modeling and monitoring of batch processes using principal component analysis (PCA) assisted generalized regression neural networks (GRNN). Biochem. Eng. J. 2004, 18:193-210.
14. Lee G., Han C.H., Yoon E.S. Multiple-fault diagnosis of the Tennessee Eastman process based on system decomposition and dynamic PLS. Ind. Eng. Chem. Res. 2004, 43:8037-8048.
15. Lee J.M., Yoo C.K., Lee I.B. Statistical process monitoring with independent component analysis. J. Process Control 2004, 14:467-485.
16. MacGregor J.F., Kourti T. Statistical process control of multivariate processes. Control Eng. Pract. 1995, 3:403-414.
17. Mika S., Schölkopf B., Smola A.J., Mjuller K.R., Scholz M., Rjatsch G. KPCA and de-noising in feature spaces. Adv. Neural Inf. Process. Syst. 1999, 11:536-542.
18. Misra M., Yue H.H., Qin S.J., Ling C. Multivariate process monitoring and fault diagnosis by multi-scale PCA. Comput. Chem. Eng. 2002, 26(9):1281-1293.
19. Nomikos P., MacGregor J.F. Multivariate spc charts for monitoring batch processes. Technometrics 1995, 37(1):41-59.
20. Qin S.J. Statistical process monitoring: basics and beyond. J. Chemom. 2003, 17:480-502.
21. Rännar S., Lindgren F., Geladi P., Wold S. A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: theory and algorithm. J. Chemom. 1994, 8:111-125.
22. Romdhani S., Gong S., Psarrou A., 1999. A multi-view nonlinear active shape model using KPCA. In: Proceedings of BMVC, Nottingham, UK, pp. 483-492.
23. Rosipal R. Kernel partial least squares for nonlinear regression and discrimination. Neural Network World 2003, 13:291-300.
24. Schölkopf B., Sung K.K., Burges C.J.C., Girosi F., Niyogi P., Poggio T., Vapnik V. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process 1997, 45:2758-2765.
25. Schölkopf B., Smola A.J., Mjuller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 1998, 10:1299-1399.
26. Tan S., Mavrovouniotis M.L. Reducing data dimensionality through optimizing neural networks inputs. AIChE J. 1995, 41:1471-1480.
27. Teppola P., Minkkinen P. Wavelet-PLS regression models for both exploratory data analysis and process monitoring. J. Chemom. 2000, 14:383-399.
28. Walczak B., Massart D.L. Local odeling with radial basis function networks. Chemom. Intell. Lab. Syst. 2000, 50:179-198.
29. Yoon S., MacGregor J.F. Principal-component analysis of multiscale data for process monitoring and fault diagnosis. AIChE J. 2004, 50(11):2891-2903.
30. Zhang Y., Qin S.J. Fault detection of nonlinear processes using multiway kernel independent analysis. Ind. Eng. Chem. Res. 2007, 46:7780-7787.
31. Zhang Y., Qin S.J. Improved nonlinear fault detection technology and statistical analysis. AIChE J. 2008, 54(12):3207-3220.