Decentralized Monitoring of Dynamic Processes Based on Dynamic Feature Selection and Informative Fault Pattern Dissimilarity

IEEE Transactions on Industrial Electronics

Volumn 63, Issue 6, 2016, Pages 3804-3814

  Tong, Chudong ^a   Shi, Xuhua ^a  

^a NINGBO UNIVERSITY   (China)

Author keywords

Decentralized Modeling; Dissimilarity Analysis; Dynamic Process Monitoring; Feature Selection

Indexed keywords

COMPUTER AIDED DIAGNOSIS; DATA HANDLING; FAILURE ANALYSIS; FEATURE EXTRACTION; MONITORING; PATTERN RECOGNITION; PROCESS MONITORING;

CROSS CORRELATIONS; DECENTRALIZED FAULT DETECTION; DECENTRALIZED MODELS; DECENTRALIZED MONITORING; DISSIMILARITY ANALYSIS; DYNAMIC FEATURE SELECTIONS; DYNAMIC PROCESS MONITORING; MONITORING PURPOSE;

FAULT DETECTION;

EID: 84970003967     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2016.2530047     Document Type: Article

References (35)

1. S. J. Qin, "Survey on data-driven industrial process monitoring and diagnosis," Annu. Rev. Control, vol. 36, no. 2, pp. 220-234, 2012.
2. Z. Ge, Z. Song, and F. Gao, "Review of recent research on data-based process monitoring," Ind. Eng. Chem. Res., vol. 52, no. 10, pp. 3543-3562, 2013.
3. S. Yin, S. X. Ding, X. Xie, and H. Luo, "A review on basic datadriven approaches for industrial process monitoring," IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418-6428, Nov. 2014.
4. S. J. Qin, "Statistical process monitoring: Basics and beyond," J. Chemom., vol. 17, no. 8/9, pp. 480-502, 2003.
5. Q. Liu, S. J. Qin, and T. Chai, "Multiblock concurrent PLS for decentralized monitoring of continuous annealing processes," IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6429-6437, Nov. 2014.
6. J. Zhu, Z. Ge, and Z. Song, "HMM-driven robust probabilistic principal component analyzer for dynamic process fault classification," IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3814-3821, Jun. 2015.
7. H. Kaneko and K. Funatsu, "A new process variable and dynamics selection method based on a genetic algorithm-based wavelength selection method," AIChE J., vol. 58, no. 6, pp. 1829-1840, 2012.
8. S. Yin, X. Zhu, and O. Kaynak, "Improved PLS focused on key-performance-indicator-related fault diagnosis," IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1651-1658, Mar. 2015.
9. 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.
10. 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, 2010.
11. S. Yin, H. Gao, and O. Kaynak, "Data-driven control and process monitoring for industrial applications-Part I," IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6356-6359, Nov. 2014.
12. J. Mori and J. Yu, "A quality relevant non-Gaussian latent subspace projection method for chemical process monitoring and fault detection," AIChE J., vol. 60, no. 2, pp. 485-499, 2014.
13. Y. Zhang, J. An, and H. Zhang, "Monitoring of time-varying processes using kernel independent component analysis," Chem. Eng. Sci., vol. 88, pp. 23-32, 2013.
14. W. Ku, R. H. Storer, and C. Georgakis, "Disturbance detection and isolation by dynamic principal component analysis," Chemom. Intell. Lab. Syst., vol. 30, no. 1, pp. 179-196, 1995.
15. P. P. Odiowei and Y. Cao, "Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations," IEEE Trans. Ind. Informat., vol. 6, no. 1, pp. 36-45, Feb. 2010.
16. A. Haghani, T. Jeinsch, and S.X. Ding, "Quality-related fault detection in industrial multimode dynamic processes," IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6446-6453, Nov. 2014.
17. A. Negiz and A. Cinar, "Statistical monitoring of multivariable dynamic processes with state-space models," AIChE J., vol. 43, no. 8, pp. 2002-2020, 1997.
18. G. Li, S. J. Qin, and D. Zhou, "A new method of dynamic latent-variable modeling for process monitoring," IEEE Trans. Ind. Electron. vol. 61, no. 11, pp. 6438-6445, Nov. 2014.
19. Y. Zhang, H. Zhou, S. J. Qin, and T. Chai, "Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares," IEEE Trans. Ind. Informat., vol. 6, no. 1, pp. 3-10, Feb. 2010.
20. M. Grbovic, W. Li, P. Xu, A. K. Usadi, L. Song, and S. Vucetic, "Decentralized fault detection and diagnosis via sparse PCA based decomposition and maximum entropy decision fusion," J. Process Control, vol. 22, no. 4, pp. 738-750, 2012.
21. Z. Ge and Z. Song, "Distributed PCA model for plant-wide process monitoring," Ind. Eng. Chem. Res., vol. 52, no. 5, pp. 1947-1957, 2013.
22. C. Tong, Y. Song, and X. Yan, "Distributed statistical process monitoring based on four-subspace construction and Bayesian inference," Ind. Eng. Chem. Res., vol. 52, no. 29, pp. 9897-9907, 2013.
23. C. F. Alcala and S. J. Qin, "Reconstruction-based contribution for process monitoring," Automatica, vol. 45, no. 7, pp. 1593-1600, 2009.
24. G. Rong, S. Liu, and J. Shao, "Fault diagnosis by locality preserving discriminant analysis and its kernel variation," Comput. Chem. Eng., vol. 49, pp. 105-113, 2013.
25. M. Kano, S. Hasebe, I. Hashimoto, and H. Ohno, "Statistical process monitoring based on dissimilarity of process data," AIChE J., vol. 48, no. 6, pp. 1231-1240, 2002.
26. J. M. Lee, C. K. Yoo, and I. B. Lee, "Statistical monitoring of dynamic processes based on dynamic independent component analysis," Chem. Eng. Sci., vol. 59, no. 14, pp. 2995-3006, 2004.
27. R. Ferrari, T. Parisini, and M.M. Polycarpou, "Distributed fault diagnosis with overlapping decompositions: An adaptive approximation approach," IEEE Trans. Autom. Control, vol. 54, no. 4, pp. 794-799, Apr. 2009.
28. K. Ghosh, Y. S. Ng, and R. Srinivasan, "Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods," Comput. Chem. Eng., vol. 35, no. 2, pp. 342-355, 2011.
29. A. Singhal and D. E. Seborg, "Pattern matching in multivariate time series databases using a moving-window approach," Ind. Eng. Chem. Res., vol. 41, no. 16, pp. 3822-3838, 2002.
30. C. Zhao, F. Wang, and Y. Zhang, "Nonlinear process monitoring based on kernel dissimilarity analysis," Control Eng. Pract., vol. 17, no. 1, pp. 221-230, 2009.
31. K. Fujiwara, M Kano, and S. Hasebe, "Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design," Chemom. Intell. Lab. Syst., vol. 20, no. 4, pp. 371-378, 2012.
32. L. H. Chiang, E. L. Russell, and R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems. Berlin, Germany: Springer-Verlag, 2001.
33. 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.
34. J. Wang and P. Q. He, "Multivariate statistical process monitoring based on statistics pattern analysis," Ind. Eng. Chem. Res., vol. 49, no. 17, pp. 7858-7869, 2010.
35. M. A. Bin Shams, H. M. Budman, and T. A. Duever, "Fault detection, identification and diagnosis using CUSUM based PCA," Chem. Eng. Sci., vol. 66, no. 20, pp. 4488-4498, 2011.