Pattern Matching in Historical Batch Data Using PCA

IEEE Control Systems

Volumn 22, Issue 5, 2002, Pages 53-63

  Singhal, Ashish ^a   Seborg, Dale E ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BATCH DATA PROCESSING; COMPUTER SIMULATION; DATA MINING; DATABASE SYSTEMS; EIGENVALUES AND EIGENFUNCTIONS; PRINCIPAL COMPONENT ANALYSIS; SPEECH RECOGNITION;

ADAPTIVE RESONANCE THEORY (ART);

PATTERN MATCHING;

EID: 0036804076     PISSN: 1066033X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCS.2002.1035217     Document Type: Article

References (35)

1. Exhibit at the Edmonton Space and Science Centre, Edmonton, Canada, 2000.
2. A. Singhal and D.E. Seborg, “Pattern matching in multivariate time-series databases using a moving window approach,” Ind. Eng. Chem. Res., to be published.
3. R. Agrawal, P. Stoloroz, and G. Piatetsky-Shapiro, Eds., Proc. 4th Intl. Conf. on Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press, 1998.
4. C. Aptè, “Data mining: An industrial research perspective,” IEEE Trans. Computational Sci. Eng., vol. 4, pp. 6-9, 1997.
5. R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
6. J. Shurmann, Pattern Classification: A Unified View of Statistical and Neural Approaches. New York: Wiley, 1996.
7. G. Stephanopoulos and C. Han, “Intelligent systems in process engineering- A review,” Comput. Chem. Engr., vol. 20, pp. 743-791, 1996.
8. M.A. Kramer and R.S.H. Mah, “Model based monitoring,” in Proc. 2nd Int. Conf. Foundations of Computer Aided Process Operations, Austin, TX, 1994, pp. 45-71.
9. J.F. Davis, M.J. Piovoso, K.A. Kosanovich, and B.R. Bakshi, “Process data analysis and interpretation,” in Advances in Chemical Engineering, J.Wei, J.L. Anderson, K. Bischoff, and J. Seinfeld, Eds., vol. 25.NewYork: Academic, 1999, pp. 1-199.
10. G.A. Carpenter, S. Grossberg, and D.B. Rosen, “ART-2A: An adaptive resonance algorithm for rapid category learning and recognition,” Neural Networks, vol. 4, pp. 493-504, 1991.
11. C.G. Looney, Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists. New York: Oxford Univ. Press, 1997.
12. H. Vedam and V. Venkatasubramanian, “A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis,” in Proc. American Control Conf., Albuquerque, NM, June 1997, pp. 309-313.
13. J.C. Wong, K.A. McDonald, and A. Palazoglu, “Classification of abnormal plant operation using multiple process variable trends,” J. Process Contr., vol. 11, pp. 409-418, 2001.
14. L. Kaufman and P.R. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, New York: Wiley, 1990.
15. L. Rabiner and B.H. Juang, Fundamentals of Speech Recognition. Englewood Cilffs, NJ: Prentice Hall, 1993.
16. A. Bakhtazad, A. Palazoglu, and J.A. Romagnoli, “Detection and classification of abnormal process situations using multidimensional wavelet domain hidden Markov trees,” Comput. Chem. Eng., vol. 24, pp. 769-75, 2000.
17. T. Kourti and J.F. MacGregor, “Multivariate SPC methods for process and product monitoring,” J. Quality Tech., vol. 28, pp. 409-428, 1996.
18. E.B. Martin and A.J. Morris, “An overview of multivariate statistical process control in continuous and batch performance monitoring,” Trans. Inst. Meas. Contr., vol. 18, pp. 51-60, 1996.
19. A. Raich and A. Cinar, “Diagnosis of process disturbances by statistical distance and angle measures,” Comput. Chem. Engr., vol. 21, pp. 661-673, 1997.
20. W. Ku, R.H. Storer, and C. Georgakis, “Disturbance detection and isolation by dynamic principal component analysis,” Chemometrics Intel. Lab. Syst., vol. 30, pp. 179-196, 1995.
21. R. Dunia and S.J. Qin, “Joint diagnosis of process and sensor faults using principal component analysis,” Control Eng. Practice, vol. 6, pp. 457-469, 1998.
22. W.J. Krzanowski, “Between-groups comparison of principal components,” J. Amer. Stat. Assoc., vol. 74, no. 367, pp. 703-707, 1979.
23. A. Raich and A. Cinar, “Multivariate statistical methods for monitoring continuous processes: Assessment of discrimination power of disturbance models and diagnosis of multiple disturbances,” Chem. Intel. Lab. Syst., vol. 30, pp. 37-48, 1995.
24. M. Kano, K. Nagao, H. Ohno, S. Hasebe, and I. Hashimoto, “Dissimilarity of process data for statistical process monitoring,” in Preprints IFAC Symp. Adv. Cont. of Chem. Proc. (ADCHEM), Pisa, Italy, June 2000, vol. I, pp. 231-236.
25. M.C. Johannesmeyer, A. Singhal, and D.E. Seborg, “Pattern-matching in historical data,” AIChE J., to be published.
26. J.E. Jackson, A User's Guide to Principal Components. New York: Wiley, 1991.
27. I.T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
28. M.C. Johannesmeyer, “Abnormal situation analysis using pattern recognition techniques and historical data,” M.Sc. thesis, Univ. of California, Santa Barbara, 1999.
29. K.V. Mardia, J.T. Kent, and J.M. Bibby, Multivariate Analysis. London: Academic, 1979.
30. T.J. Kourti, J. Lee, and J.F. MacGregor, “Experiences with industrial applications of projection methods for multivariate statistical process control,” Comput. Chem. Eng., vol. 20, pp. s745-s750, 1996.
31. A. Singhal and D.E. Seborg, “Dynamic data rectification using the expectation maximization algorithm,” AIChE J., vol. 46, pp. 1556-1565, 2000.
32. A. Signhal, “Pattern-matching in multivariate time-series data,” Ph.D. dissertation, Univ. of California, Santa Barbara, 2002.
33. J.C. Hale and H.L. Sellars, “Historical data recording for process computers,” Chemical Eng. Prog., vol. 77, no. 11, pp. 38-43, Nov. 1981.
34. E.H. Bristol, “Swinging door trending: Adaptive trend recording?” Advances in Instrumentation and Control, vol. 45, pp. 749-754, 1990.
35. J. Vortruba, B. Volesky, and L. Yerushalmi, “Mathematical model of a batch acetone-butanol fermentation,” Biotechnol. Bioeng., vol. 28, pp. 247-255, 1986.