Direct projection to latent variable space for fault detection

Journal of the Franklin Institute

Volumn 351, Issue 3, 2014, Pages 1226-1250

  Hu, Jing ^a,b   Wen, Chenglin ^b,c   Li, Ping ^a   Yuan, Tianqi ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b HENAN UNIVERSITY OF TECHNOLOGY   (China)

^c HANGZHOU DIANZI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84894062861     PISSN: 00160032     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfranklin.2013.10.007     Document Type: Article

References (22)

1. R. Isermannn Fault-Diagnosis Systems An Introduction from Fault Detection to Fault Tolerance 2006 Springer Berlin
2. M.Y. Zhong, S.X. Ding, and J. Lam et al. An LMI approach to design robust fault detection filter for uncertain LTI systems Automatica 39 3 2003 543 550
3. J. Zhang, E.B. Martin, and A.J. Morris Fault detection and diagnosis using multivariate statistical, technique Chem. Eng. Res. Des. 74 1 1996 89 96
4. S.J. Qin Statistical process monitoring basics and beyond J. Chemometrics 17 2003 480 502
5. K. Theodora, N. Paul, and F. John MacGregor Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS J. Process Control 5 4 1995 277 284
6. C.L. Wen, J. Hu, T.Z. Wang, and Z.G. Chen Relative PCA with applications of data compression and fault diagnosis Acta Autom. Sin. 34 9 2008 1128 1139
7. F.N. Zhou, C.L. Wen, T.H. Tang, and Z.G. Chen DCA based multiple faults diagnosis method Acta Autom. Sin. 35 7 2009 971 982
8. A. Hoskuldsson PLS regression methods J. Chemometrics 2 1988 211 288
9. G. Li, S.J. Qin, Y.D. Ji, and D.H. Zhou Total PLS based contribution plots for fault diagnosis Acta Autom. Sin. 35 6 2009 759 765
10. S. Wold, H. Antti, and F. Lindgren et al. Orthogonal signal correction of near-infrared spectra Chemometrics Intell. Lab. Syst. 44 1998 175 185
11. T. Fearn On orthogonal signal correction Chemometrics Intell. Lab. Syst. 50 2000 47 52
12. J. Trygg, and S. Wold Orthogonal projections to latent structures (O-PLS) J. Chemometrics 16 2002 119 128
13. D.H. Zhou, G. Li, and S.J. Qin Total projection to latent structures for process monitoring AIChE J. 56 1 2010 168 178
14. S. Yin, S.X. Ding, P. Zhang, A. Hagahni, A. Naik, Study on modifications of PLS approach for process monitoring, in: Proceedings of 18th IFAC World Congress, 2011.
15. I.T. Jolliffe Principal Component Analysis second edition 2002 Springer 5 6
16. G. Li, S.J. Qin, and D.H. Zhou Geometric properties of partial least squares for process monitoring Automatica 46 1 2010 204 210
17. S.J. Qin, and R. Dunia Determining the number of principal components for best reconstruction J. Process Control 10 2000 245 250
18. X.D. Zhang Matrix Analysis and Applications 2008 Tsinghua University Press Beijing 658 708
19. A. Khan, J. Moyne, and D. Tilury Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares J. Process Control 18 2008 961 974
20. P. Nomikow, and J.F. MacGregor Multivariate SPC charts for monitoring batch process Technometrics 37 1995 41 59
21. D.H. Zhou, G. Li, and Y. Li Data-Based Fault Diagnosis Technology for Industrial Process 2011 Science Press Beijing 186 197
22. J.J. Downs, and E.F. Vogel A plant-wide industrial process control problem Comput. Chem. Eng. 17 3 1993 245 255