Nonlinear process monitoring based on linear subspace and Bayesian inference

Journal of Process Control

Volumn 20, Issue 5, 2010, Pages 676-688

  Ge, Zhiqiang ^a   Zhang, Muguang ^a   Song, Zhihuan ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Bayesian inference; Fault diagnosis; Linear subspace; Process monitoring; Statistical analysis

Indexed keywords

ALGORITHM COMPLEXITY; BAYESIAN INFERENCE; FAULT DIAGNOSIS; FAULT DIAGNOSIS METHOD; FAULT PROBABILITIES; LINEAR SUBSPACE; LINEAR SUBSPACE METHODS; MONITORING METHODS; NONLINEAR PROCESS; NONLINEAR PROCESS MONITORING; NUMERICAL EXAMPLE; STATISTICAL ANALYSIS; SUBMODELS; TENNESSEE EASTMAN; VARIABLE SELECTION;

FAILURE ANALYSIS; INFERENCE ENGINES; PROCESS CONTROL; PROCESS MONITORING; STATISTICAL METHODS;

BAYESIAN NETWORKS;

EID: 77955305868     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2010.03.003     Document Type: Article

References (27)

1. U. Kruger, Q. Chen, D.J. Sandoz, and R.C. McFarlane Extended PLS approach for enhanced condition monitoring of industrial processes AIChE J. 47 2001 2076 2091
2. S.J. Qin Statistical process monitoring: basics and beyond J. Chemometrics 17 2003 480 502
3. X.Z. Wang, S. Medasani, F. Marhoon, and H. Albazzaz Multidimensional visualization of principal component scores for process historical data analysis Ind. Eng. Chem. Res. 43 2004 7036 7048
4. A. Simoglou, P. Georgieva, E.B. Martin, A.J. Morris, and S. Feyo de Azevedo On-line monitoring of a sugar crystallization process Comput. Chem. Eng. 29 2005 1411 1422
5. U. Kruger, and G. Dimitriadis Diagnosis of process faults in chemical systems using a local partial least squares approach AIChE J. 54 2008 2581 2596
6. C.H. Zhao, F.L. Wang, and M.X. Jia Dissimilarity analysis based batch process monitoring using moving windows AIChE J. 53 2007 1267 1277
7. M.A. Kramer Autoassociative neural networks Comput. Chem. Eng. 16 1992 313 328
8. D. Dong, and T.J. McAvoy Nonlinear principal component analysis-based on principal curves and neural networks Comput. Chem. Eng. 20 1996 65 78
9. U. Kruger, D. Antory, J. Hahn, G.W. Irwin, and G. McCullough Introduction of a nonlinearity measure for principal component models Comput. Chem. Eng. 29 2005 2355 2362
10. J.M. Lee, C.K. Yoo, S.W. Choi, P.A. Vanrolleghem, and I.B. Lee Nonlinear process monitoring using kernel principal component analysis Chem. Eng. Sci. 59 2004 223 234
11. C. Cheng, and M.S. Chiu Nonlinear process monitoring using JITL-PCA Chem. Intell. Lab. Syst. 76 2005 1 13
12. A. Maulud, D. Wang, and J.A. Romagnoli A multi-scale orthogonal nonlinear strategy for multi-variate statistical process monitoring J. Process Control 16 2006 671 683
13. X. Wang, U. Kruger, G.W. Irwin, G. McCullough, and N. McDowell Nonlinear PCA with the local approach for diesel engine fault detection and diagnosis IEEE T. Cont. Syst. Tech. 16 2008 122 129
14. Y.W. Zhang, and S.J. Qin Improved nonlinear fault detection technique and statistical analysis AIChE J. 54 2008 3207 3220
15. C.M. Bishop Pattern Recognition and Machine Learning 2006 Springer
16. P. Miller, R.E. Swanson, C.F. Heckler, Contribution plots: the missing link in multivariate quality control, In Fall Conf. of the ASQC and ASA, Milwaukee, WI, 1993.
17. J. Westerhuis, S. Gurden, and A. Smilde Generalized contribution plots in multivariate statistical process monitoring Chem. Intell. Lab. Syst. 51 2000 95 114
18. R. Dunia, and S.J. Qin Subspace approach to multidimensional fault identification and reconstruction AIChE J. 44 1998 1813 1831
19. D. Lieftuche, U. Kruger, G.W. Irwin, R.J. Treasure, Fault reconstruction in linear dynamic systems using multivariate statistics, IEE Proc. Control Theory Appl. 153 (2006) 437-446.
20. C. Alcala, and S.J. Qin Reconstruction-based contribution for process monitoring Automatica 45 2009 1593 1600
21. A. Singhai, and D.E. Seborg Evaluation of a pattern matching method for the Tennessee Eastman challenge process J. Process Control 16 2006 601 613
22. Z.Q. Ge, C.J. Yang, Z.H. Song, and H.Q. Wang Robust online monitoring for multimode processes based on nonlinear external analysis Ind. Eng. Chem. Res. 47 2008 4775 4783
23. M. Kano, K. Nagao, H. Hasebe, I. Hashimoto, H. Ohno, R. Strauss, and B.R. Bakshi Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem Comput. Chem. Eng. 26 2002 161 174
24. L.H. Chiang, E.L. Russell, and R.D. Braatz Fault Detection and Diagnosis in Industrial Systems 2001 Springer-Verlag London
25. P.R. Lyman, and C. Georgakist Plant-wide control of the Tennessee Eastman problem Comput. Chem. Eng. 19 1995 321 331
26. J.J. Downs, and E.F. Vogel A plant-wide industrial process control problem Comput. Chem. Eng. 17 1993 245 255
27. K.V. Kumar, and A. Negi SubXPCA and a generalized feature partitioning approach to principal component analysis Pattern Recogn. 41 2008 1398 1409 (Pubitemid 350212964)