Fault diagnosis based on Fisher discriminant analysis and support vector machines

Computers and Chemical Engineering

Volumn 28, Issue 8, 2004, Pages 1389-1401

  Chiang, Leo H ^a   Kotanchek, Mark E ^a   Kordon, Arthur K ^a  

^a DOW CHEMICAL COMPANY   (United States)

Author keywords

Classification; Fault diagnosis; Fisher discriminant analysis; Support vector machines

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; DATA REDUCTION; GENETIC ALGORITHMS; SET THEORY;

FISHER DISCRIMINANT ANALYSIS; SUPPORT VECTOR MACHINES (SVM);

CHEMICAL ENGINEERING;

COMPUTER;

ARTICLE; CLASSIFICATION; CORRELATION ANALYSIS; DISCRIMINANT ANALYSIS; FISHER EXACT TEST; GENETIC ALGORITHM; MATHEMATICAL COMPUTING; NONLINEAR SYSTEM; SIMULATOR;

EID: 2342505830     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2003.10.002     Document Type: Article

References (36)

1. Allwein E.L., Schapire R.E., Singer Y. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research. 1:2000;113-141
2. Baughman, D. R. (1995). Neural networks in bioprocessing and chemical engineering. New York: Academic Press.
3. Beebe, K. R., Pell, R. J., & Seasholtz, M. B. (1998). Chemometrics: A practical guide. New York: Wiley.
4. Bharath, R., & Drosen, J. (1994). Neural network computing. New York: McGraw-Hill.
5. Burbidge, R., & Buxton, B. (2001). An introduction to support vector machine for data mining. Technical Report [http://www.cs.ucl.ac.uk/staff/r. burbidge/pubs/yor12-svm-intro.html].
6. Cherkassky, V., & Mullier, F. (1998). Learning from data, concepts, theory, and methods. New York: Wiley.
7. Chiang L.H., Pell R.J. Genetic algorithms combined with discriminant analysis for key variable identification. Journal of Process Control. 14:2004;143-155
8. Chiang L.H., Russell E.L., Braatz R.D. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometrics and Intelligent Laboratory Systems. 50:2000;243-252
9. Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001). Fault detection and diagnosis in industrial systems. New York: Springer-Verlag.
10. Conlin A.K., Martin E.B., Morris A.J. Confidence limits for contribution plots. Journal of Chemometrics. 14:2000;725-736
11. Downs J.J., Vogel E.F. A plant-wide industrial-process control problem. Computers and Chemical Engineering. 17:1993;245-255
12. Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis. New York: Wiley.
13. Engl, H. W., Pontil, M., & Neubauer, A. (1998). Regularisation of inverse problem. Dordrecht: Kluwer Academic Publishers.
14. Fung, G., & Mangasarian, O. L. (2001a). Proximal support vector machine classifiers. Data Mining Institute Technical Report 01-02 [http://www.cs.wisc.edu/∼olvi/olvi.html].
15. Fung, G., & Mangasarian, O. L. (2001b). Multicategory proximal support vector machine classifiers. Data Mining Institute Technical Report 01-06 [http://www.cs.wisc.edu/∼olvi/olvi.html].
16. Gurden S.P., Martin E.B., Morris A.J. The introduction of process chemometrics into an industrial pilot plant laboratory. Chemometrics and Intelligent Laboratory Systems. 44:1998;319-330
17. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning, data mining, inference, and prediction. New York: Springer-Verlag.
18. Jordaan, E. M. (2002). Development of robust inferential sensors, industrial application of support vector machines for regression. Ph.D. thesis, Technische Universiteit Eindhoven.
19. Jordaan, E. M., & Smits, G. F. (2002). Estimation of the regularization parameter for support vector regression. In Proceedings of World Conference on Computational Intelligence I, Honolulu Hawaii (pp. 2785-2791).
20. Kecman, V. (2001). Learning and soft computing, support vector machines, neural networks, and fuzzy logic models. Cambridge, MA: MIT Press.
21. Kourti T., MacGregor J.F. Multivariate SPC methods for process and product monitoring. Journal of Quality Technology. 28:1996;409-428
22. Ku K., Storer R.H., Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics Intelligent Laboratory Systems. 30:1995;179-196
23. Lee, Y., & Mangasarian, O. L. (1999). A smooth support vector machine for classification. Data Mining Institute Technical Report 99-03 [http://www.cs.wisc.edu/∼olvi/olvi.html].
24. Lee, Y., & Mangasarian, O. L. (2001). RSVM: Reduce support vector machines. In Proceedings of the SIAM International Conference on Data Mining, Chicago, Illinois.
25. Lyman P.R., Georgakis C. Plant-wide control of the Tennessee Eastman problem. Computers and Chemical Engineering. 19:1995;321-331
26. MacGregor J.F., Kourti T. Statistical process control of multivariate processes. Controlled Engineering Practice. 3:1995;403-414
27. McAvoy T.J. A methodology for screening level control structures in plantwide control systems. Computers and Chemical Engineering. 22:1998;1543-1552
28. Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Muller, K. (1999). Fisher discriminant analysis with kernels. Technical Report [http://www.citeseer.nj.nec.com/mika99fisher.html].
29. Miller P., Swanson R.E. Contribution plots: a missing link in multivariate quality control. Applied Mathematics and Computer Science. 8:1998;775-792
30. Misra M., Yue H.H., Qin S.J., Ling C. Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers and Chemical Engineering. 26:2002;1281-1293
31. Qin S.J., Valle S., Piovoso M.J. On unifying multiblock analysis with application to decentralized process monitoring. Journal of Chemometrics. 15:2001;715-742
32. Raich A.C., Cinar A. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes. AIChE Journal. 42:1996;995-1009
33. Scholkopf, B., & Smola, A. J. (2002). Learning with kernels, support vector machines, regularization, optimization, and beyond. New York: MIT Press.
34. Umetrics. (2002), User's guide to SIMCA-P, SIMCA-P+, version 10.0, software manual.
35. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
36. Westerhuis J.A., Gurden S.P., Smilde A.K. Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems. 51:2000;95-114