Online monitoring of multivariate processes using higher-order cumulants analysis

Industrial and Engineering Chemistry Research

Volumn 53, Issue 11, 2014, Pages 4328-4338

  Wang, Youqing ^a   Fan, Jicong ^a   Yao, Yuan ^b  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^b NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

MULTIVARIANT ANALYSIS;

CONTRIBUTION ANALYSIS; CONVENTIONAL APPROACH; HIGHER-ORDER STATISTICS; INDEPENDENT COMPONENTS; INDEPENDENT COMPONENTS ANALYSIS; MULTIVARIATE PROCESS; NON-GAUSSIAN DISTRIBUTION; STATISTICAL INFORMATION;

INDEPENDENT COMPONENT ANALYSIS;

EID: 84896500286     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie401834e     Document Type: Article

References (45)

1. Venkatasubramanian, V.; Rengaswamy, R.; Kavuri, S. N.; Yin, K. A review of process fault detection and diagnosis Part III: Process history based methods Comput. Chem. Eng. 2003, 27, 327-346
2. Qin, S. J. Statistical process monitoring: basics and beyond J. Chemom. 2003, 17, 480-502
3. Zhou, D. H.; Li, G.; Qin, S. J. Total projection to latent structures for process monitoring AIChE J. 2011, 56, 168-178
4. Yu, J.; Qin, S. J. Multiway Gaussian mixture model based multiphase batch process monitoring Ind. Eng. Chem. Res. 2009, 48, 8585-8594
5. Li, G.; Alcala, C. F.; Qin, S. J.; Zhou, D. H. Generalized reconstruction-based contributions for output-relevant fault diagnosis with application to the Tennessee Eastman process IEEE Trans. Control Syst. Technol. 2011, 19, 1114-1127
6. Wold, S.; Esbensen, K.; Geladi, P. Principal component analysis Chemom. Intell. Lab. Syst. 1987, 2, 37-52
7. Nomikos, P.; MacGregor, J. F. Monitoring batch processes using multiway principal component analysis AIChE J. 1994, 40, 1361-1375
8. Ku, W.; Storer, R. H.; Georgakis, C. Disturbance detection and isolation by dynamic principal component analysis Chemom. Intell. Lab. Syst. 1995, 30, 179-196
9. Chen, J.; Liao, C.-M. Dynamic process fault monitoring based on neural network and PCA J. Process Control 2002, 12, 277-289
10. 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
11. Alcala, C. F.; Qin, S. J. Reconstruction-based contribution for process monitoring with kernel principal component analysis Ind. Eng. Chem. Res. 2010, 49, 7849-7857
12. Cheng, C. Y.; Hsu, C. C.; Chen, M. C. Adaptive kernel principal component analysis (KPCA) for monitoring small disturbances of nonlinear processes Ind. Eng. Chem. Res. 2010, 49, 2254-2262
13. Shams, M. A. B.; Budman, H. M.; Duever, T. A. Fault detection, identification and diagnosis using CUSUM based PCA Chem. Eng. Sci. 2011, 66, 4488-4498
14. Zhang, Y.; Ma, C. Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS Chem. Eng. Sci. 2011, 66, 64-72
15. Comon, P. Independent component analysis, a new concept Signal Process. 1994, 36, 287-314
16. Hyvärinen, A. Fast and robust fixed-point algorithms for independent component analysis IEEE Trans. Neural Networks 1999, 10, 626-634
17. Hyvärinen, A.; Oja, E. Independent component analysis: Algorithms and applications Neural Networks 2000, 13, 411-430
18. Lee, J. M.; Yoo, C. K.; Lee, I. B. Statistical process monitoring with independent component analysis J. Process Control 2004, 14, 467-485
19. Lee, J. M.; Qin, S. J.; Lee, I. B. Fault detection and diagnosis of multivariate process based on modified independent component analysis AIChE J. 2006, 52, 3501-3514
20. Wang, J.; Zhang, Y.; Cao, H.; Zhu, W. Dimension reduction method of independent component analysis for process monitoring based on minimum mean square error J. Process Control 2012, 22, 477-487
21. Lee, J.-M.; Yoo, C.; Lee, I.-B. Statistical monitoring of dynamic processes based on dynamic independent component analysis Chem. Eng. Sci. 2004, 59, 2995-3006
22. 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, 526-536
23. Ge, Z.; Yang, C.; Song, Z.; Wang, H. Robust Online Monitoring for Multimode Processes Based on Nonlinear External Analysis Ind. Eng. Chem. Res. 2008, 47, 4775-4783
24. Zhang, Y. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM Chem. Eng. Sci. 2009, 64, 801-811
25. Zhao, C.; Gao, F.; Wang, F. Nonlinear batch process monitoring using phase-based kernel-independent component analysis-principal component analysis (KICA-PCA) Ind. Eng. Chem. Res. 2009, 48, 9163-9174
26. Stefatos, G.; Hamza, A. B. Dynamic independent component analysis approach for fault detection and diagnosis Expert Syst. Appl. 2010, 37, 8606-8617
27. Rashid, M. M.; Yu, J. Hidden Markov model based adaptive independent component analysis approach for complex chemical process monitoring and fault detection Ind. Eng. Chem. Res. 2012, 51, 5506-5514
28. Yang, Y.; Chen, Y.; Chen, X.; Liu, X. Multivariate industrial process monitoring based on the integration method of canonical variate analysis and independent component analysis Chemom. Intell. Lab. Syst. 2012, 116, 94-101
29. Rashid, M. M.; Yu, J. A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-Gaussian dynamic process monitoring Chemom. Intell. Lab. Syst. 2012, 115, 44-58
30. Fan, J.; Qin, S. J.; Wang, Y. Online monitoring of nonlinear multivariate industrial process using filtering KICA-PCA Control Eng. Pract. 2014, 22, 205-216
31. Fan, J.; Wang, Y. Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis Inf. Sci. 2014, 259, 369-379
32. Fan, J.; Wang, Y.; Qin, S. J. Combined indices for ICA and their applications to multivariate process fault diagnosis (in Chinese) Acta Autom. Sin. 2013, 39, 494-501
33. Alcala, C. F.; Qin, S. J. Analysis and generalization of fault diagnosis methods for process monitoring J. Process Control 2011, 21, 322-330
34. Mendel, J. M. Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications IEEE Trans. Signal Process. 1991, 79, 278-305
35. Giannakis, G. B.; Mendel, J. M. Cumulant-based order determination of non-Gaussian ARMA models IEEE Trans. Acoust., Speech, Signal Process. 1990, 38, 1411-1423
36. Giannakis, G. B.; Tsatsanis, M. K. Signal detection and classification using matched filtering and higher order statistics IEEE Trans. Acoust., Speech, Signal Process. 1990, 38, 1284-1296
37. Sadler, B. M.; Giannakis, G. B.; Lii, K.-S. Estimation and detection in non-Gaussian noise using higher order statistics IEEE Trans. Signal Process. 1994, 42, 2729-2741
38. Choudhury, M. A. A. S.; Shah, S. L.; Thornhill, N. F. Diagnosis of poor control-loop performance using higher-order statistics Automatica 2004, 40, 1719-1728
39. Downs, J. J.; Vogel, E. F. A plant-wide industrial process control problem Comput. Chem. Eng. 1993, 17, 245-255
40. Lyman, P. R.; Georgakis, C. Plant-wide control of the Tennessee Eastman problem Comput. Chem. Eng. 1995, 19, 321-331
41. Chiang, L. H.; Russell, E.; Braatz, R. D. Fault detection and diagnosis in industrial systems; Springer: London, 2001.
42. Valle, S.; Li, W.; Qin, S. J. Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods Ind. Eng. Chem. Res. 1999, 38, 4389-4401
43. Martin, E. B.; Morris, A. J. Non-parametric confidence bounds for process performance monitoring charts J. Process Control 1996, 6, 349-358
44. Downs, J. J.; Vogel, E. F. A plant-wide industrial process control problem Comput. Chem. Eng. 1993, 17, 245-255
45. Lyman, P. R.; Georgakis, C. Plant-wide control of the Tennessee Eastman problem Comput. Chem. Eng. 1995, 19, 321-331