Fault diagnosis of nonlinear process based on KCPLS reconstruction

Chemometrics and Intelligent Laboratory Systems

Volumn 140, Issue , 2015, Pages 49-60

  Zhang, Yingwei ^a   Sun, Rongrong ^a   Fan, Yunpeng ^a  

^a STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

Author keywords

Fault diagnosis; Fault reconstruction; Fault relevant direction; Kernel concurrent projection to latent structure (KCPLS)

Indexed keywords

PENICILLIN DERIVATIVE;

ALGORITHM; ARTICLE; FAULT DIAGNOSIS; FERMENTATION; INTERMETHOD COMPARISON; KERNEL CONCURRENT PROJECTION TO LATENT STRUCTURE RECONSTRUCTION; NONLINEAR SYSTEM; PREDICTION; PROCESS MONITORING; QUALITY CONTROL PROCEDURES; STATISTICAL ANALYSIS;

EID: 84912047533     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2014.10.002     Document Type: Article

References (24)

1. Gertler J., Cao J. PCA-based fault diagnosis in the presence of control and dynamics. AICHE J. 2004, 50(2):388-402.
2. Hsu C.C., Su C.T. An adaptive forecast-based chart for non-Gaussian process monitoring: with application to equipment malfunctions detection in a thermal power plant. IEEE Trans. Control Syst. Technol. 2011, Vol.19(No.5):1245-1250.
3. Chen T., Zhang J. On-line multivariate statistical monitoring of batch processes using Gaussian mixture model, SCIE. Comput. Chem. Eng. 2010, 34(4):500-507.
4. Wise B., Gallagher N. The process chemometrics approach to process monitoring and fault detection. J. Process Control 1996, 6(6):329-348.
5. Qin S.J. Statistic process monitoring: basics and beyond. J. Chemom. 2003, 17:480-502.
6. Choi S.W., Lee I.B. Multiblock PLS-based localized process diagnosis, SCIE. J. Process Control 2005, Vol. 15(No.3):295-306.
7. Zhou D., Li G., Qin S.J. Total projection to latent structures for process monitoring. AICHE J. 2010, 56(1):168-178.
8. Kim K., Lee J.M., Lee I.B. A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction, SCIE. Chemom. Intell. Lab. Syst. 2005, 50(No.1-2):22-30.
9. Zhang Y., Ma C. Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS, SCIE. Chem. Eng. Sci. 2011, 66(1):64-72.
10. Yue H., Qin S.J. Reconstruction based fault identification using a combined index. Ind. Eng. Chem. Res. 2001, 40(20):4403-4414.
11. Xu C., Wang C., Ji F. KPLS-RWBFNN model for MFL 2D defect profile reconstruction, SCIE. Nondestruct. Test. Eval. 2013, 28(1):82-97.
12. Wu W., Liu Z., He X. Learning-based super resolution using kernel partial least squares, SCIE. Image Vision Comput. 2011, 29(6):394-406.
13. Qin S.J., Li W. Detection, identification and reconstruction of faulty sensors with maximized sensitivity. AICHE J. 1999, 45(9):1963-1976.
14. Qian C., Kruger U. Analysis of extended partial least squares for monitoring large-scale processes. IEEE Control Syst. Technol. 2005, 13(5):807-813.
15. Zhang Y., Wang Z. Fault reconstruction algorithm based on fault-relevant KPCA. Chinese Control and Decision Conference 2013, 4319-4323.
16. Qin S.J., Zheng Y. Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures. AICHE J. 2013, 59(2):496-504.
17. Lyman P.R., Georgakis C. Plant-wide control of Tennessee Eastman problem. Comput. Chem. Eng. 1995, 19(3):321-331.
18. Li G., Joe Qin S., Zhou D. Geometric properties of partialleast squares for process monitoring. Automatica 2010, 46(1):204-210.
19. Yue H., Qin S.J. Fault reconstruction and identification for industrial process. AIChE Annual Meeting, Miami, FL 1998.
20. Zhang Y., Hu Z. Multivariate process monitoring and analysis based on multi-scale KPLS, SCIE. Chem. Eng. Res. Des. 2011, 89(12A):2667-2678.
21. MacGregor J.F., Jaeckle C., Kiparissides C., Koutoudi M. Process monitoring and diagnosis by multiblock PLS method. AICHE J. 1994, 40:826-828.
22. Carlos F., Alcala S. Joe Qin, reconstruction-based contribution for process monitoring with kernel principal feature analysis. Ind. Eng. Chem. Res. 2010, 49(17):7849-7857.
23. Qin S.J., Dunia R. Determining the number of principal features for best reconstruction. J. Process Control 2000, 10(2):245-250.
24. Li G., Qin S.J., Zhou D. Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models. Ind. Eng. Chem. Res. 2010, 49(No. 19):9175-9183.