Generalized orthogonal locality preserving projections for nonlinear fault detection and diagnosis

Chemometrics and Intelligent Laboratory Systems

Volumn 96, Issue 1, 2009, Pages 75-83

  Shao, Ji Dong ^a   Rong, Gang ^a   Lee, Jong Min ^b  

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

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Fault detection; Fault diagnosis; Kernel methods; Nonlinear dimensionality reduction; Structure preserving

Indexed keywords

ARTICLE; DATA ANALYSIS; DISCRIMINANT ANALYSIS; GENERALIZED ORTHOGONAL LOCALITY PRESERVING PROJECTIONS; KERNEL METHOD; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROCESS MODEL;

EID: 61749100891     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2009.01.001     Document Type: Article

References (36)

1. Venkatasubramanian V., Rengaswamy R., Kavuri S.N., and Yin K. Comput. Chem. Eng. 27 (2003) 327-346
2. MacGregor J.F., and Kourti T. Contr. Eng. Pract. 3 (1995) 403-414
3. Ku W., Storer R.H., and Georgakis C. Chemometr. Intell. Lab. Syst. 30 (1995) 179-196
4. Raich A., and Cinar A. AIChE J. 42 (1996) 995-1009
5. Bakshi B.R. AIChE J. 44 (1998) 1596-1610
6. Chiang L.H., Russell E., and Braatz R.D. Fault Detection and Diagnosis in Industrial Systems (2001), Springer
7. Misra M., Yue H.H., Qin S.J., and Ling C. Comput. Chem. Eng. 26 (2002) 1281-1293
8. Chiang L.H., Russell E.L., and Braatz R.D. Chemometr. Intell. Lab. Syst. 50 (2000) 243-252
9. Chiang L.H., Kotanchek M.E., and Kordon A.K. Comput. Chem. Eng. 28 (2004) 1389-1401
10. Jolliffe I.T. Principal Component Analysis (2002), Springer
11. Fukunaga K. Introduction to Statistical Pattern Recognition (1990), Academic Press
12. Kramer M.A. AIChE J. 37 (1991) 233-243
13. Dong D., and McAvoy T.J. Comput. Chem. Eng. 20 (1996) 65-78
14. Scholköpf B., and Smola A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (2001), MIT Press Cambridge, MA, USA
15. Scholköpf B., Smola A., and Müller K.R. Neural Comput. 10 (1998) 1299-1319
16. Baudat G., and Anouar F.E. Neural Comput. 12 (2000) 2385-2404
17. Lee J.M., Yoo C.K., Choi S.W., VanrolleghemLee P.A., and Lee I.B. Chem. Eng. Sci. 59 (2004) 223-234
18. Choi S.W., Lee C., Lee J.M., Park J.H., and Lee I.B. Chemometr. Intell. Lab. Syst. 75 (2005) 55-67
19. Cho H.W. Expert Syst. Appl. 32 (2007) 534-542
20. Chapelle O., Scholköpf B., and Zien A. Semi-supervised Learning (2006), MIT Press
21. Tenenbaum J.B., Silva V., and Langford J.C. Science 290 (2000) 2319-2323
22. Saul L.K., Roweis S.T., and Singer Y. J. Mach. Learn. Res. 4 (2004) 119-155
23. Belkin M., and Niyogi P. Neural Comput. 15 (2003) 1373-1396
24. He X., and Niyogi P. In: Thrun S., Saul L.K., and Schölkopf B. (Eds). Advances in Neural Information Processing Systems, 8-13 December 2003, Vancouver, Canada (2003) 8-13
25. Hu K., and Yuan J. J. Process Control 18 (2007) 797-807
26. Foley D.H., and J.W.S. IEEE Trans. Comput. 24 (1975) 281-289
27. Ye J. J. Mach. Learn. Res. 6 (2005) 483-502
28. Xiong T., Ye J., and Cherkassky V. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 17-22 June 2006, New York, NY, USA (2006) 125-131
29. Cai D., He X., Han J., and Zhang H.J. IEEE Trans. Image Process. 15 (2006) 3608-3614
30. Wahba G. Spline Models for Observational Data (1990), Society for Industrial Mathematics
31. Cattell R.B. Multivariate Behav. Res. 1 (1966) 245-276
32. Martin E.B., and Morris A.J. J. Process Control 6 (1996) 349-358
33. Stone M. J. R. Stat. Soc. 36 (1974) 111-133
34. Lu J., Plataniotis K.N., and Venetsanopoulos A.N. IEEE Trans. Neural Netw. 14 (2003) 117-126
35. Strang G. Introduction to Linear Algebra (2003), Wellesley Cambridge Press
36. Lyman P.R., and Georgakis C. Comput. Chem. Eng. 19 (1995) 321-331