Distributed statistical process monitoring based on four-subspace construction and Bayesian inference

Industrial and Engineering Chemistry Research

Volumn 52, Issue 29, 2013, Pages 9897-9907

  Tong, Chudong ^a   Song, Yu ^a   Yan, Xuefeng ^a  

^a China   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; FAULT DETECTION; INFERENCE ENGINES; MULTIVARIANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL PROCESS CONTROL;

BAYESIAN INFERENCE; DATA SPACE; DIMENSION REDUCTION; DIMENSIONALITY REDUCTION; LOW-DIMENSIONAL REPRESENTATION; MONITORING MODELS; MONITORING PERFORMANCE; MULTIVARIATE STATISTICAL PROCESS MONITORING; PROCESS VARIABLES; STATISTICAL PROCESS MONITORING;

PROCESS MONITORING;

EID: 84880858673     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie400544q     Document Type: Article

References (40)

1. Qin, S. J. Statistical process monitoring: Basics and beyond J. Chemom. 2003, 17, 480
2. Chiang, L. H.; Russell, E. L.; Braatz, R. D. Fault Detection and Diagnosis in Industrial Systems; Springer-Verlag: London, 2001.
3. MacGregor, J.; Kourti, T. Statistical process control of multivariate processes Control Eng. Pract. 1995, 3, 403
4. Kano, M.; Nagao, K.; Hasebe, S.; Hashimoto, I.; Ohno, H. A new multivariate statistical process monitoring method using principal component analysis Comput. Chem. Eng. 2001, 25, 1103
5. Wang, X.; Kruger, U.; Lennox, B. Recursive partial least squares algorithms for monitoring complex industrial processess Control Eng. Pract. 2003, 11, 613
6. Kruger, U.; Dimitriadis, G. Diagnosis of process faults in chemical systems using a local partial least squares approach AIChE J. 2008, 54, 2581
7. Ge, Z.; Zhang, M.; Song, Z. Nonlinear process monitoring based on linear subspace and Bayesian inference J. Process Control 2010, 20, 676
8. Lee, J. M.; Yoo, C. K.; Lee, I. B. Statistical process monitoring with independent component analysis J. Process Control 2004, 14, 467
9. 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
10. Lee, J. M.; Qin, S. J.; Lee, I. B. Fault detection of non-linear processes using kernel independent component analysis Can. J. Chem. Eng. 2007, 85, 526
11. Shao, J. D.; Rong, G.; Lee, J. M. Generalized orthogonal locality preserving projections for nonlinear fault detection and diagnosis Chemom. Intel. Lab. Syst. 2009, 96, 75
12. Lee, J. M.; Yoo, C. K.; Lee, I. B. Fault detection of batch processes using multiway kernel principal component analysis Chem. Eng. Sci. 2004, 28, 1837
13. Zhang, Y.; Hu, Z. On-line batch process monitoring using hierarchical kernel partial least squares Chem. Eng. Res. Des. 2011, 89, 2078
14. Cheng, C.; Chiu, M. S. A new data-based methodology for nonlinear process modeling Chem. Eng. Sci. 2004, 59, 2801
15. Ge, Z.; Song, Z. A comparative study of just-in-time-learning based methods for online soft sensor modeling Chemom. Intel. Lab. Syst. 2010, 104, 306
16. Bhagwat, A.; Srinivasan, R.; Krishnaswamy, P. R. Multi-linear model-based fault detection during process transitions Chem. Eng. Sci. 2003, 58, 1649
17. Kresta, J.; MacGregor, J.; Marlin, T. Multivariate statistical monitoring of process operating performance Can. J. Chem. Eng. 1991, 69, 35
18. Cho, J.H.; Lee, J. M.; Choi, S. W.; Lee, D.; Lee, I. B. Fault identification for process monitoring using kernel principal component analysis Chem. Eng. Sci. 2005, 60, 279-288
19. Qin, S. J.; Valle, S.; Piovoso, M. J. On unifying multiblock analysis with application to decentralized process monitoring J. Chemom. 2001, 15, 715
20. Cherry, G. A.; Qin, S. J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis IEEE Trans. Semicond. Manuf. 2006, 19, 159
21. Zhang, Y.; Zhou, H.; Qin, S. J.; Chai, T. Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares IEEE Trans. Ind. Inf. 2010, 6, 3
22. Grbovic, M.; Li, W.; Xu, P.; Usadi, A. K.; Song, L.; Vucetic, S. Decentralized fault detection and diagnosis via sparse PCA-based decomposition and maximum entropy decision fusion J. Process Control 2012, 22, 738
23. Zhang, Y.; Ma, C. Decentralized fault diagnosis using multiblock kernel independent component analysis Chem. Eng. Res. Des. 2012, 90, 667
24. Yu, J. Localized fisher discriminant analysis-based complex chemical process monitoring AIChE J. 2011, 57, 1817
25. Zhang, M.; Ge, Z.; Song, Z.; Fu, R. Global-local structure analysis model and its application for fault detection and identification Ind. Eng. Chem. Res. 2011, 50, 6837
26. Yu, J. Local and global principal component analysis for process monitoring J. Process Control. 2012, 22, 1358
27. Rusinov, L. A.; Rudakova, I. V.; Remizova, O. A.; Kurkina, V. V. Fault diagnosis in chemical processes with application of hierarchical neural networks Chemom. Intel. Lab. Syst. 2009, 97, 98
28. Zhang, Y. Enhanced statistical analysis of nonlinear processes using KPCA, KICA, and SVM Chem. Eng. Sci. 2009, 64, 801
29. Bishop, C. M. Pattern Recognition and Machine Learning; Springer: London, 2006.
30. Sun, W.; Chen, L. Multivariate Statistical Analysis; Higher Education Press: Beijing, 1994.
31. Wang, H.; Song, Z.; Li, P. Improved PCA with application to process monitoring and fault diagnosis J. Chem. Ind. Eng. (China) 2001, 52, 471
32. Arbel, A.; Rinard, I. H.; Shinnar, R. Dynamics and control of fluidized catalytic crackers. 3. Designing the control system: Choice of manipulated and measured variables for partial control Ind. Eng. Chem. Res. 1996, 35, 2215
33. Tyréus, B. D. Dominant variables for partial control. 1. A thermodynamic method for their identification Ind. Eng. Chem. Res. 1999, 38, 1432
34. Srinivasan, R.; Qian, M. State-specific key variables for monitoring multi-state processes Chem. Eng. Res. Des. 2007, 85, 1630
35. Ghosh, K.; Ng, Y. S.; Srinivasan, R. Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods Comput. Chem. Eng. 2011, 35, 342
36. Downs, J. J.; Vogel, E. F. A plant-wide industrial process control problem Comput. Chem. Eng. 1993, 17, 245
37. Singhal, A.; Seborg, D. E. Evaluation of a pattern matching method for the Tennessee Eastman challenge process J. Process Control 2006, 16, 601
38. Russell, E. L.; Chiang, L. H.; Braatz, R. D. Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis Chemom. Intel. Lab. Syst. 2000, 51, 81
39. Wang, J.; He, Q. P. Multivariate statistical process monitoring based on statistics pattern analysis Ind. Eng. Chem. Res. 2010, 49, 7858
40. Lyman, P. R.; Georgakist, C. Plant-wide control of the Tennessee Eastman problem Comput. Chem. Eng. 1995, 19, 321