Multisubspace principal component analysis with local outlier factor for multimode process monitoring

Industrial and Engineering Chemistry Research

Volumn 53, Issue 42, 2014, Pages 16453-16464

  Song, Bing ^a   Shi, Hongbo ^a   Ma, Yuxin ^a   Wang, Jianping ^a  

^a School of Information Science and Engineering   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ANALYSIS; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; ITERATIVE METHODS; PROCESS CONTROL; PROCESS MONITORING; STATISTICS;

CONTINUOUS STIRRED TANK REACTOR (CSTR) PROCESS; CONVENTIONAL CLUSTERING; LOCAL OUTLIER FACTOR; LOCAL OUTLIER FACTORS; MANUFACTURING STRATEGY; MIXTURE OF GAUSSIANS; MULTIMODE PROCESS MONITORING; NON-GAUSSIAN DISTRIBUTION;

FACTOR ANALYSIS;

EID: 84908210209     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie502344q     Document Type: Article

References (50)

1. Lee, J. M.; Yoo, C. K.; Choi, S. W.; Vanrolleghem, P. A.; Lee, I. B. Nonlinear process monitoring using kernel principal component analysis Chem. Eng. Sci. 2004, 59, 223-234
2. Ge, Z. Q.; Song, Z. H. Mixture Bayesian regularization method of PPCA for multimode process monitoring AIChE J. 2010, 56, 2838-2849
3. Kim, D. S.; Lee, I. B. Process monitoring based on probabilistic PCA Chemom. Intell. Lab. Syst. 2003, 67, 109-123
4. Jin, H. D.; Lee, Y. H.; Lee, G.; Han, C. H. Robust recursive principal component analysis modeling for adaptive monitoring Ind. Eng. Chem. Res. 2006, 45, 696-703
5. Zhao, C. H.; Gao, F. R. Fault-relevant principal component analysis (FPCA) method for multivariate statistical modeling and process monitoring Chemom. Intell. Lab. Syst. 2014, 133, 1-16
6. Tong, C. D.; Palazoglu, A.; Yan, X. F. An adaptive multimode process monitoring strategy based on mode clustering and mode unfolding J. Process Control 2013, 23, 1497-1507
7. Liu, J. L.; Chen, D. S. Operational performance assessment and fault isolation for multimode processes Ind. Eng. Chem. Res. 2010, 49, 3700-3714
8. Ge, Z. Q.; Song, Z. H.; Gao, F. R. Review of recent research on data-based process monitoring Ind. Eng. Chem. Res. 2013, 52, 3543-3562
9. Ge, Z. Q.; Song, Z. H. Online monitoring of nonlinear multiple mode processes based on adaptive local model approach Control Eng. Pract. 2008, 16, 1427-1437
10. Ma, H. H.; Hu, Y.; Shi, H. B. A novel local neighborhood standardization strategy and its application in fault detection of multimode processes Chemom. Intell. Lab. Syst. 2012, 118, 287-300
11. Yu, J.; Qin, S. J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models AIChE J. 2008, 54, 1811-1829
12. Xie, X.; Shi, H. B. Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models Ind. Eng. Chem. Res. 2012, 51, 5497-5505
13. Kano, M.; Hasebe, S.; Hashimoto, I.; Ohno, H. Evolution of multivariate statistical process control: Application of independent component analysis and external analysis Comput. Chem. Eng. 2004, 28, 1157-1166
14. Ma, Y. X.; Shi, H. B. Multimode process monitoring based on aligned mixture factor analysis Ind. Eng. Chem. Res. 2014, 53, 786-799
15. Song, B.; Ma, Y. X.; Shi, H. B. Multimode process monitoring using improved dynamic neighborhood preserving embedding Chemom. Intell. Lab. Syst. 2014, 135, 17-30
16. Ge, Z. Q.; Song, Z. H. Multimode process monitoring based on Bayesian method J. Chemom. 2009, 23, 636-650
17. Wang, F. L.; Tan, S.; Peng, J.; Chang, Y. Q. Process monitoring based on mode identification for multi-mode process with transitions Chemom. Intell. Lab. Syst. 2012, 110, 144-155
18. Ge, Z. Q.; Gao, F. R.; Song, Z. H. Two-dimensional Bayesian monitoring method for nonlinear multimode processes Chem. Eng. Sci. 2011, 66, 5173-5183
19. Liu, J. L.; Chen, D. S. Fault detection and identification using modified Bayesian classification on PCA subspace Ind. Eng. Chem. Res. 2009, 48, 3059-3077
20. Tan, S.; Wang, F. L.; Peng, J.; Chang, Y. Q.; Wang, S. Multimode process monitoring based on mode identification Ind. Eng. Chem. Res. 2012, 51, 374-388
21. Zhao, S. J.; Zhang, J.; Xu, Y. M. Monitoring of processes with multiple operation modes through multiple principle component analysis models Ind. Eng. Chem. Res. 2004, 43, 7025-7035
22. Li, R. F.; Wang, X. Z. Dimension reduction of process dynamic trends using independent component analysis Comput. Chem. Eng. 2002, 26, 467-473
23. Tong, C. D.; Palazoglu, A.; Yan, X. F. Improved ICA for process monitoring based on ensemble learning and Bayesian inference Chemom. Intell. Lab. Syst. 2014, 135, 141-149
24. Zhao, C. H.; Wang, F. L.; Mao, Z. Z.; Lu, N. Y.; Jia, M. X. Adaptive monitoring based on independent component analysis for multiphase batch processes with limited modeling data Ind. Eng. Chem. Res. 2008, 47, 3104-3113
25. Ge, Z. Q.; Song, Z. H. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors Ind. Eng. Chem. Res. 2007, 46, 2054-2063
26. Zhao, C. H.; Gao, F. R.; Wang, F. L. Nonlinear batch process monitoring using phase-based kernel-independent component analysis-principal component analysis (KICA-PCA) Ind. Eng. Chem. Res. 2009, 48, 9163-9174
27. Ma, Y. X.; Shi, H. B.; Ma, H. H.; Wang, M. L. Dynamic process monitoring using adaptive local outlier factor Chemom. Intell. Lab. Syst. 2013, 127, 89-101
28. Lee, J.; Kang, B.; Kang, S. H. Integrating independent component analysis and local outlier factor for plant-wide process monitoring J. Process Control 2011, 21, 1011-1021
29. Ma, H. H.; Hu, Y.; Shi, H. B. Fault detection and identification based on the neighborhood standardized local outlier factor method Ind. Eng. Chem. Res. 2013, 52, 2389-2402
30. Qin, S. J.; Dunia, R. Determining the number of principal components for best reconstruction J. Process Control 2000, 10, 245-250
31. Tamura, M.; Tsujita, S. A study on the number of principal components and sensitivity of fault detection using PCA Comput. Chem. Eng. 2007, 31, 1035-1046
32. Jolliffe, I. T. A note on the use of principal components in regression Appl. Statist. 1982, 300-303
33. Togkalidou, T.; Braatz, R. D.; Johnson, B. K.; Davidson, O.; Andrews, A. Experimental design and inferential modeling in pharmaceutical crystallization AIChE J. 2001, 47, 160-168
34. Jiang, Q. C.; Yan, X. F.; Zhao, W. X. Fault detection and diagnosis in chemical processes using sensitive principal component analysis Ind. Eng. Chem. Res. 2013, 52, 1635-1644
35. Tong, C. D.; Song, Y.; Yan, X. F. Distributed statistical process monitoring based on four-subspace construction and Bayesian inference Ind. Eng. Chem. Res. 2013, 52, 9897-9907
36. Breunig, M. M.; Kriegel, H.; Ng, R. T.; Sander, J. LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD International Conference on Mangement of Data; Chen, W.; Naughton, J. F.; Bernstein, P. A., Eds.; ACM: New York, 2000; pp 93-104.
37. Ge, Z. Q. Improved two-level monitoring system for plant-wide processes Chemom. Intell. Lab. Syst. 2014, 132, 141-151
38. Sebzalli, Y. M.; Wang, X. Z. Knowledge discovery from process operational data using PCA and fuzzy clustering Eng. Appl. Artif. Intel. 2001, 14, 607-616
39. Chen, W. C.; Wang, M. S. A fuzzy c-means clustering-based fragile watermarking scheme for image authentication Expert Syst. Appl. 2009, 36, 1300-1307
40. Liu, J. L. Fault detection and classification for a process with multiple production grades Ind. Eng. Chem. Res. 2008, 47, 8250-8262
41. Beaver, S.; Palazoglu, A.; Romagnoli, J. A. Cluster analysis for autocorrelated and cyclic chemical process data Ind. Eng. Chem. Res. 2007, 46, 3610-3622
42. Ku, W. F.; Storer, R. H.; Georgakis, C. Disturbance detection and isolation by dynamic principal component analysis Chemom. Intell. Lab. Syst. 1995, 30, 179-196
43. Ge, Z. Q.; Zhang, M. G.; Song, Z. H. Nonlinear process monitoring based on linear subspace and Bayesian inference J. Process Control 2010, 20, 676-688
44. Zhao, C. H. Concurrent phase partition and between-mode statistical analysis for multimode and multiphase batch process monitoring AIChE J. 2014, 60, 559-573
45. Zhao, C. H.; Yao, Y.; Gao, F. R.; Wang, F. L. Statistical analysis and online monitoring for multimode processes with between-mode transitions Chem. Eng. Sci. 2010, 65, 5961-5975
46. Ricker, N. L. Optimal steady-state operation of the Tennessee Eastman challenge process Comput. Chem. Eng. 1995, 19, 949-959
47. Downs, J. J.; Vogel, E. F. A plant-wide industrial process control problem Comput. Chem. Eng. 1993, 17, 245-255
48. Wang, L.; Shi, H. B. Improved kernel PLS-based fault detection approach for nonlinear chemical processes Chin. J. Chem. Eng. 2014, 22, 657-663
49. Yoon, S.; MacGregor, J. F. Fault diagnosis with multivariate statistical models part I: Using steady state fault signatures J. Process Control 2001, 11, 387-400
50. Ma, Y. X.; Shi, H. B.; Wang, M. L. Adaptive local outlier probability for dynamic process monitoring Chin. J. Chem. Eng. 2014, 22, 820-827