Fault identification of Tennessee Eastman process based on FS-KPCA

Huagong Xuebao/Journal of Chemical Industry and Engineering (China)

Volumn 59, Issue 7, 2008, Pages 1783-1789

  Bo, Cuimei ^a,b   Zhang, Shi ^a   Zhang, Guangming ^a   Wang, Zhiquan ^b  

^a NANJING TECH UNIVERSITY   (China)

^b NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Fault identification; Feature sample extracting; Gradient arithmetic; Kernel PCA; TE process

Indexed keywords

ELECTRIC FAULT LOCATION; EXTRACTION; FEATURE EXTRACTION; FINANCIAL DATA PROCESSING; MATRIX ALGEBRA; PROCESS ENGINEERING;

DYNAMIC MONITORING; EXTRACTION METHODS; FAULT DIAGNOSIS METHOD; FAULT IDENTIFICATIONS; KERNEL MATRICES; KERNEL PCA; KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA); LARGE DATA; MODELING DATA; TENNESSEE EASTMAN PROCESS (TEP);

PRINCIPAL COMPONENT ANALYSIS;

EID: 48049091561     PISSN: 04381157     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Zhao L J, Cai T Y, Wang G. Double moving window MPCA for online adaptive batch monitoring. Chin. J. Chem. Eng., 2005, 13(5): 649-655
2. Kourtit Mac, Gregor J F. Multivariate statistical process control methods for monitoring and diagnosing process and product performance. Quality Technology, 1996, 28: 409-428
3. Bakshi B R. Multiscale PCA with application to multivariate statistical process monitoring. American Institute of Chemical Engineering Journal, 1998, 44: 1596-1610
4. Kramer M A. Non-linear principal component analysis using auto-associative neural networks. AIChE Journal, 1991, 37: 233-243
5. Jia F, Martin E B, Morris A J. Nonlinear principal components analysis with application to process fault detection. International Journal of Systems Science, 2001, 31: 1473-1487
6. Dong D, McAvoy T J. Nonlinear principal component analysis based on principal curves and neural networks. Computers and Chemical Engineering, 1996, 20(1): 65-78
7. Schölkopf B, Smola A J, Muller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10: 1299-1319
8. Lee J M, Yoo C K, Lee I B. Fault detection of batch processes using multiway kernel principal component analysis. Computers and Chemical Engineering, 2004, 28: 1837-1847
9. Choi S W, Lee I B. Nonlinear dynamic process monitoring based on dynamic kernel PCA. Chemical Engineering Science, 2004, 59: 5897-5908
10. Fan Liping, Yu Haibin, Yuan Decheng. Monitoring of SBR process using kernel principal component analysis. Chinese Journal of Scientific Instrument, 2006, 27(3): 249-253
11. Fan Yugang, Li Ping, Song Zhihuan. KPCA based on feature samples for fault detection. Control and Decision, 2005, 20(12): 1415-1418
12. Cho J H, Lee J M, Choi S W. Fault identification for process monitoring using kernel principal component analysis. Chemical Engineering Science, 2005, 60(1): 279-288
13. Baudat G, Anouar F. Feature vector selection and projection using kernels. Neurocomputing, 2003, 55: 21-38
14. Lyman P R, Georgakisc. Plant-wide control of the Tennessee Eastman problem. Computer and Chemical Engineering, 1995, 19: 321-331
15. Manabu Kano, Koji Nagao. Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem. Computers and Chemical Engineering, 2002, 26: 161-174