Quality Relevant and Independent Two Block Monitoring Based on Mutual Information and KPCA

IEEE Transactions on Industrial Electronics

Volumn 64, Issue 8, 2017, Pages 6518-6527

  Huang, Junping ^a   Yan, Xuefeng ^a  

^a School of Information Science and Engineering   (China)

Author keywords

Kernel principal component analysis (KPCA); mutual information (MI); nonlinear process monitoring; quality independent monitoring; quality relevant monitoring

Indexed keywords

MONITORING; NUMERICAL METHODS; PROCESS CONTROL; PROCESS MONITORING; QUALITY CONTROL;

KERNEL PRINCIPAL COMPONENT; KERNEL PRINCIPAL COMPONENT ANALYSES (KPCA); MONITORING METHODS; MUTUAL INFORMATIONS; NONLINEAR PROCESS MONITORING; PREDICTION PERFORMANCE; PROCESS VARIABLES; TENNESSEE EASTMAN PROCESS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 85027397193     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2017.2682012     Document Type: Article

References (38)

1. S. Yin, S. X. Ding, X. Xie, and H. Luo, "A review on basic data-driven approaches for industrial processmonitoring, " IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418-6428, Nov. 2014.
2. S. Yin, X. Li, H. Gao, and O. Kaynak, "Data-based techniques focused on modern industry: An overview, " IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 657-667, Jan. 2015.
3. K. Zhang, H. Hao, Z. Chen, S. X. Ding, and K. Peng, "A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches, " J. Process Control, vol. 33, pp. 112-126, Sep. 2015.
4. F. Deng, S. Guo, R. Zhou, and J. Chen, "Sensor multifault diagnosis with improved support vector machines, " IEEE Trans. Autom. Sci. Eng., vol. 14, no. 2, pp. 1053-1063, Apr. 2017.
5. X. Li and Y. Wang, "Adaptive online monitoring for ICU patients by combining just-in-time learning and principal component analysis, " J. Clin. Monit. Comput., vol. 30, no. 6, pp. 807-820, Dec. 2016.
6. J. M. Lee, C. Yoo, and I. B. Lee, "On-line batch process monitoring using a consecutively updated multiway principal component analysis model, " Comput. Chem. Eng., vol. 27, no. 12, pp. 1903-1912, Dec. 2003.
7. Y. Zhang, "Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM, " Chem. Eng. Sci., vol. 64, no. 5, pp. 801-811, Mar. 2009.
8. C. Tong and X. Shi, "Decentralized monitoring of dynamic processes based on dynamic feature selection and informative fault pattern dissimilarity, " IEEE Trans. Ind. Electron., vol. 63, no. 6, pp. 3804-3814, Jun. 2016.
9. S. J. Qin and Y. Zheng, "Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures, " AIChE J., vol. 59, no. 2, pp. 496-504, Feb. 2013.
10. D. Zhou, G. Li, and S. J. Qin, "Total projection to latent structures for process monitoring, " AIChE J., vol. 56, no. 1, pp. 168-178, Jan. 2010.
11. S. Yin, X. Zhu, and O. Kaynak, "Improved PLS focused on keyperformance-indicator-related fault diagnosis, " IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1651-1658, Mar. 2015.
12. G. Li, B. Liu, and S. J. Qin, "Quality relevant data-driven modeling and monitoring of multivariate dynamic processes: The dynamic T-PLS approach, " IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 2262-2271, Dec. 2011.
13. Y. Zhang, R. Sun, and Y. Fan, "Fault diagnosis of nonlinear process based on KCPLS reconstruction, " Chemometrics Intell. Lab. Syst., vol. 140, pp. 49-60, Jan. 2015.
14. K. Peng, K. Zhang, and J. Dong, "Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multibatch measurements, " J. Franklin Inst., vol. 352, no. 3, pp. 987-1006, Mar. 2015.
15. G. Wang, H. Luo, and K. Peng, "Quality-related fault detection using linear and nonlinear principal component regression, " J. Franklin Inst., vol. 353, no. 10, pp. 2159-2177, Jul. 2016.
16. Y. Zhang and S. Li, "Modeling and monitoring between-mode transition of multimodes processes, " IEEE Trans. Ind. Informat., vol. 9, no. 4, pp. 2248-2255, Nov. 2013.
17. J. Jiao, H. Yu, and G. Wang, "A quality-related fault detection approach based on dynamic least squares for process monitoring, " IEEE Trans. Ind. Electron., vol. 63, no. 4, pp. 2625-2632, Apr. 2016.
18. Y. Zhang, Y. Fan, and W. Du, "Nonlinear process monitoring using regression and reconstruction method, " IEEE Trans. Ind. Electron., vol. 13, no. 3, pp. 1343-1354, Jul. 2016.
19. K. Ghosh, M. Ramteke, and R. Srinivasan, "Optimal variable selection for effective statistical process monitoring, " Comput. Chem. Eng., vol. 60, pp. 260-276, Jan. 2014.
20. J. F. MacGregor, C. Jaeckle, C. Kiparissides, and M. Koutoudi, "Process monitoring and diagnosis by multiblock PLS methods, " AIChE J., vol. 40, no. 5, pp. 826-838, May 1994.
21. S. J. Qin, S. Valle, and M. J. Piovoso, "On unifying multiblock analysis with application to decentralized process monitoring, " J. Chemometrics, vol. 15, no. 9, pp. 715-742, Oct. 2001.
22. J. Kohonen, S. P. Reinikainen, K. Aaljoki, A. Perkio, T. Vaanaen, and A. Hoskuldsson, "Multi-block methods in multivariate process control, " J. Chemometrics, vol. 22, nos. 3/4, pp. 281-287, Apr. 2008.
23. Z. Ge and Z. Song, "Distributed PCA model for plant-wide process monitoring, " Ind. Eng. Chem. Res., vol. 52, no. 5, pp. 1947-1957, Feb. 2013.
24. J. Huang and X. Yan, "Gaussian and non-Gaussian double subspace statistical process monitoring based on principal component analysis and independent component analysis, " Ind. Eng. Chem. Res., vol. 54, no. 3, pp. 1015-1027, Jan. 2015.
25. Q. Jiang and X. Yan, "Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA, " J. Process Control, vol. 32, pp. 38-50, Aug. 2015.
26. J. Huang and X. Yan, "Related and independent variable fault detection based on KPCA and SVDD, " J. Process Control, vol. 39, pp. 88-99, Mar. 2016.
27. J. M. Lee, C. K. Yoo, and S. W. Choi, "Nonlinear process monitoring using kernel principal component analysis, " Chem. Eng. Sci., vol. 59, no. 1, pp. 223-234, Jan. 2004.
28. A. Kraskov, H. Stogbauer, and P. Grassberger, "Estimating mutual information, " Phys. Rev. E, vol. 69, no. 6, pp. 1-16, Jun. 2004, doi: 10. 1103/PhysRevE. 69. 066138.
29. M. M. Rashid and J. Yu, "A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-Gaussian dynamic process monitoring, " Chemometrics Intell. Lab., vol. 115, pp. 44-58, Jun. 2012.
30. G. Doquire and M. Verleysen, "Pattern recognition-Applications and methods, " Adv. Intell. Syst. Comput., vol. 204, pp. 51-63, 2013, doi: 10. 1007/978-3-642-36530-0-5.
31. G. Brown, A. Pocock, M. J. Zhao, and M. Luján, "Conditional likelihood maximisation: A unifying framework for information theoretic feature selection, " J. Mach. Learn. Res., vol. 13, pp. 27-66, Jan. 2012.
32. L. Wang and H. Shi, "Multivariate statistical process monitoring using an improved independent component analysis, " Chem. Eng. Res. Des., vol. 88, no. 4A, pp. 403-414, Apr. 2010.
33. Q. Jiang and X. Yan, "Just-in-time reorganizedPCAintegrated withSVDD for chemical process monitoring, " AIChE J., vol. 60, no. 3, pp. 949-965, Mar. 2014.
34. J. J. Downs and E. F. Vogel, "A plant-wide industrial process control problem, " Comput. Chem. Eng., vol. 17, no. 3, pp. 245-255, Mar. 1993.
35. P. R. Lyman and C. Georgakis, "Plant-wide control of the Tennessee Eastman problem, " Comput. Chem. Eng., vol. 19, pp. 321-331, Mar. 1995.
36. Z. Ge, M. Zhang, and Z. Song, "Nonlinear process monitoring based on linear subspace and Bayesian inference, " J. Process Control, vol. 20, no. 5, pp. 676-688, Jun. 2010.
37. M. F. S. V. D'Angelo, R. M. Palhares, M. C. O. C. Filho, R. D. Maia, J. B. Mendes, and P. Y. Ekel, "A new fault classification approach applied to Tennessee Eastman benchmark process, " Appl. Soft Comput., vol. 49, pp. 676-686, Sep. 2016.
38. G. Jia, Y. Wang, and B. Huang, "Dynamic higher-order cumulants analysis for state monitoring based on a novel lag selection, " Inf. Sci., vol. 331, pp. 45-66, Feb. 2016.