Monitoring for nonlinear multiple modes process based on LL-SVDD-MRDA

IEEE Transactions on Automation Science and Engineering

Volumn 11, Issue 4, 2014, Pages 1133-1148

  Du, Wenli ^a   Tian, Ying ^a   Qian, Feng ^a  

^a School of Information Science and Engineering   (China)

Author keywords

Between mode dynamic process; Lazy learning (LL); Modified receptor density algorithm (MRDA); Multiple operation modes; Nonlinear; Support vector data description (SVDD)

Indexed keywords

LAZY LEARNING; MODE DYNAMICS; MULTIPLE OPERATION MODES; NONLINEAR; RECEPTOR DENSITY ALGORITHMS; SUPPORT VECTOR DATA DESCRIPTION;

EID: 84908575684     PISSN: 15455955     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASE.2013.2285571     Document Type: Article

References (46)

1. L. H. Chiang, E. L. Russell, and R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems. London, U.K.: Springer-Verlag, 2001.
2. Y. S. Ng and R. Srinivasan, "An adjoined multi-model approach for monitoring batch and transient operations," Comput. Chem. Eng., Vol. 33, no. 4, pp. 887-902, 2009.
3. J. H. Cho, J. M. Lee, S. W. Choi, D. Lee, and I. B. Lee, "Fault identification for process monitoring using kernel principal component analysis," Chem. Eng. Sci., Vol. 60, no. 4, pp. 279-288, 2005.
4. M. Ma, D. S. H. Wong, S. Jang, and S. Tseng, "Fault detection based on statistical multivariate analysis and microarray visualization," IEEE Trans. Ind. Informat., Vol. 6, no. 1, pp. 18-24, Feb. 2010.
5. Y. W. Zhang, "Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM," Chem. Eng. Sci., Vol. 64, no. 5, pp. 801-811, 2009.
6. W. H. Li, H. H. Yue, S. V. Cervantes, and S. J. Qin, "Recursive PCA for adaptive process monitoring," J. Process Control, Vol. 10, no. 5, pp. 471-486, 2000.
7. L. Gang, C. F. Alcala, S. J. Qin, and D. H. Zhou, "Generalized reconstruction-based contributions for output-relevant fault diagnosis with application to the Tennessee Eastman process," IEEE Trans. Control Syst. Technol., Vol. 19, no. 5, pp. 1114-1127, 2011.
8. J. Camacho and J. Pico, "Multi-phase analysis framework for handling batch processes data," J. Chemometrics, Vol. 22, no. 11-12, pp. 632-643, 2008.
9. S. J. Qin, "Statistical process monitoring: Basics and beyond," J. Chemometrics, Vol. 17, no. 8-9, pp. 480-502, 2003.
10. 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., Vol. 47, no. 14, pp. 4775-4783, 2008.
11. C. H. Zhao, F. L. Wang, Z. Z. Mao, N. Y. Lu, and M. X. Jia, "Improved batch process monitoring and quality prediction based on multiphase statistical analysis," Ind. Eng. Chem. Res., Vol. 47, no. 3, pp. 835-849, 2008.
12. M. Maestri, A. Farall, P. Groisman, M. Cassanello, and G. Horowitz, "A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes," Comput. Chem. Eng., Vol. 34, no. 2, pp. 223-231, 2010.
13. A. Hoskuldsson and K. Svinning, "Modelling of multi-block data," J. Chemometrics, Vol. 20, no. 8-10, pp. 376-385, 2006.
14. Z. Q. Ge and Z. H. Song, "Mixture Bayesian regularization method of PPCA for multimode process monitoring," AIChE J., Vol. 56, no. 11, pp. 2838-2849, 2010.
15. Z. Q. Ge and Z. H. Song, "Multimode process monitoring based on Bayesian method," J. Chemometrics, Vol. 23, no. 12, pp. 636-650, 2009.
16. T. Kourti, "Application of latent variable methods to process control and multivariate statistical process control in industry," Int. J. Adaptive Contr. Signal Process., Vol. 19, no. 4, pp. 213-246, 2005.
17. C. H. Zhao, F. L. Wang, N. Y. Lu, and M. X. Jia, "Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes," J. Process Contr., Vol. 17, no. 9, pp. 728-741, 2007.
18. Y. W. Zhang, J. Y. An, and C. Ma, "Fault detection of Non-Gaussian processes based on model migration," IEEE Trans. Contr. Syst. Technol., Vol. 21, no. 5, pp. 1517-1526, 2013.
19. Y. W. Zhang and S. Li, "Modeling and monitoring between-mode transition of multimode processes," IEEE Trans. Ind. Informat., Vol. 9, no. 4, pp. 2248-2255, Nov. 2013.
20. Y. W. Zhang, T. Y. Chai, and Z. M. Li, "Modelling and monitoring of dynamic processes," IEEE Trans. Neural Netw. Learn. Syst., Vol. 23, no. 2, pp. 277-284, 2012.
21. G. Bontempi, M. Birattari, and H. Bersini, "Lazy learning for local modelling and control design," Int. J. Control, Vol. 72, no. 7-8, pp. 643-658, 1999.
22. G. Bontempi and M. Birattari, "From linearization to lazy learning: A survey of divide-and-conquer techniques for nonlinear control," Int. J. Comput. Cognition, Vol. 3, no. 1, pp. 26-72, 2005.
23. T. H. Pan, S. Y. Li, and W. J. Cai, "Lazy learning-based online identification and adaptive PID control: A case study for CSTR process," Ind. Eng. Chem. Res., Vol. 46, no. 2, pp. 472-480, 2007.
24. Z. Ge and Z. Song, "Online monitoring of nonlinear multiple mode processes based on adaptive local model approach," Contr. Eng. Practice, Vol. 16, no. 12, pp. 1427-1437, 2008.
25. R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 4th ed. Englewood Cliff, NJ: Prentice Hall, 1998.
26. H. G. Hiden, M. J. Willis, M. T. Tham, and G. A. Montague, "Non-linear principal components analysis using genetic programming," Comput. Chem. Eng., Vol. 23, pp. 413-425, 1999.
27. S. Mika, B. Schölkopf, A. J. Smola, K. R. Muller, M. Scholz, and G. Ratsch, "Kernel PCA and de-noising in feature spaces," Adv. Neural Inf. Process. Syst., Vol. 11, pp. 536-542, 1999.
28. J. H. Cho, J. M. Lee, S. W. Choi, D. Lee, and I. B. Lee, "Fault identification for process monitoring using kernel principal component analysis," Chem. Eng. Sci., Vol. 60, no. 1, pp. 279-288, 2005.
29. S. Rännar, F. Lindgren, P. Geladi, and S. Wold, "A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm," J. Chemometrics, Vol. 8, no. 2, pp. 111-125, 1994.
30. R. Rosipal, "Kernel partial least squares for nonlinear regression and discrimination," Neural Network World, Vol. 13, pp. 291-300, 2003.
31. Y. Zhang and S. J. Qin, "Fault detection of nonlinear processes using multiway kernel independent component analysis," Ind. Eng. Chem. Res., 3vol. 46, no. 23, pp. 7780-7787, 2007.
32. Z. Q. Ge, C. J. Yang, and Z. H. Song, "Improved kernel PCA-based monitoring approach for nonlinear processes," Chem. Eng. Sc, Vol. 64, no. 9, pp. 2245-2255, 2009.
33. C. H. Zhao, F. R. Gao, and F. L. Wang, "Nonlinear batch process monitoring using phase-based kernel-independent component analysis-principal component analysis (KICA-PCA)," Ind. Eng. Chem. Res., Vol 48, no. 20, pp. 9163-9174, 2009.
34. Y. Zhang, H. Zhou, S. J. Qin, and T. Y. Chai, "Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares," IEEE Trans. Ind. Informat., Vol. 6, no. 1, pp. 3-12, Aug. 2010.
35. V. Vapnik, TheNature of Statistical Learning Theory. New York, NY, USA: Springer, 1999.
36. N. Owens, A. Greensted, J. Timmis, and A. Tyrell, "T cell receptor signaling inspired kernel density estimation and anomaly detection," in IEEE Proc. 8th Int. Conf. Artif Immune Syst., York, U.K., 2009, pp. 122-135.
37. N Owens, "From biology to algorithms," Ph.D. dissertation, Dept. Electronics, Univ. York, Heslington, U.K., 2010.
38. J. Hilder, N. Owens, P. Hickey, S. Cairns, D. Kilgour, J. Timmis, and A. Tyrrell, "Parameter optimization in the receptor density algorithm," in Proc. IEEE Process. 10th Int. Conf. Artif. Immune Syst., Cambridge, U.K., 2011, pp. 226-239.
39. K. Lau, I. Bate, P. Cairns, and J. Timmis, "Adaptive data-driven error detection in swarm robotics with statistical classifiers," Robot. Autonomous Syst., Vol. 59, no. 12, pp. 1021-1035, 2011.
40. K. Lau, J. Timmis, and I. Bate, "Collective self-detection scheme for adaptive error detection in a foraging swarm of robots," in Proc. IEEE 10th Int. Conf. Artif. Immune Syst. Cambridge, U.K., 2011, pp. 254-267.
41. D. M. J. Tax and R. P. W. Duin, "Support vector data description," Mach. Learn., Vol. 54, no. 1, pp. 45-66, 2004.
42. X. Q. Liu, K. Li, M. McAfeeb, and G. W. Irwin, "Improved nonlinear PCA for process monitoring using support vector data description," J. Process Contr., Vol. 21, no 9, pp 1306-1317, 2011.
43. X. Q. Liu, L. Xie, U. Kruger, and T. Littler, "Statistical-based monitoring of multivariate Non-Gaussian systems," AIChE J., Vol. 54, no. 9, pp. 2379-2391, July 14, 2008.
44. M. Ge, Y. S. Xu, and R. X. Du, "An intelligent online monitoring and diagnostic system for manufacturing automation," IEEE Trans. Autom. Sci Eng., Vol. 5, no. 1, pp. 127-138, Jan. 2008.
45. D. Dong and T. J. McAvoy, "Nonlinear principal component analysis-based on principal curves and neural network," Comput. Chem. Eng., Vol. 20, no. 1, pp. 65-78, 1996.
46. J. J. Downs and E. F. Vogel, "A plant-wide industrial process control problem," Comput. Chem. Eng., Vol. 17, no. 3, pp. 245-255, 1993.