Comprehensive subspace decomposition and isolation of principal reconstruction directions for online fault diagnosis

Journal of Process Control

Volumn 23, Issue 10, 2013, Pages 1515-1527

  Zhao, Chunhui ^a,b   Sun, Youxian ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Fault diagnosis; Fault reconstruction; Relative fault deviations; Subspace decomposition

Indexed keywords

FAULT RECONSTRUCTION; MODEL ALGORITHMS; ON-LINE FAULT DIAGNOSIS; PRINCIPAL COMPONENTS; PROCESS INFORMATION; RELATIVE FAULT DEVIATIONS; SUBSPACE DECOMPOSITION; TENNESSEE EASTMAN PROCESS;

ALGORITHMS; FAILURE ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84887356740     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2013.09.019     Document Type: Article

References (32)

1. C.C. Hsu, and C.T. Su An adaptive forecast-based chart for non-Gaussian processes monitoring: with application to equipment malfunctions detection in a thermal power plant IEEE Trans. Control Syst. Technol. 19 2011 1245 1250
2. P.A. Samara, G.N. Fouskitakis, and J.S. Sakellariou A statistical method for the detection of sensor abrupt faults in aircraft control systems IEEE Trans. Control Syst. Technol. 16 2008 789 798
3. Z. Bin, C. Sconyers, and C. Byington A probabilistic fault detection approach: application to bearing fault detection IEEE Trans. Ind. Electron. 58 2011 2011 2018
4. S.J. Qin Statistical process monitoring: basics and beyond J. Chem. 17 2003 480 502
5. C. Qian, and U. Kruger Analysis of extended partial least squares for monitoring large-scale processes IEEE Trans. Control Syst. Technol. 13 2005 807 813 (Pubitemid 41410784)
6. B. Ayhan, M.Y. Chow, and M.H. Song Multiple discriminant analysis and neural-network-based monolith and partition fault-detection schemes for broken rotor bar in induction motors IEEE Trans. Ind. Electron. 53 2006 1298 1308 (Pubitemid 44263740)
7. U. Kruger, S. Kumar, and T. Littler Improved principal component monitoring using the local approach Automatica 43 2007 1532 1542 (Pubitemid 47198208)
8. C.F. Alcala, and S.J. Qin Reconstruction-based contribution for process monitoring Automatica 45 2009 1593 1600
9. R. Muradore, and P. Fiorini A PLS-based statistical approach for fault detection and isolation of robotic manipulators IEEE Trans. Ind. Electron. 59 2012 3167 3175
10. E.N.M. Sprange, H.J. Ramaker, J. Westerhuis, and A. Smilde Critical evaluation of approaches for on-line batch process monitoring Chem. Eng. Sci. 57 2002 3979 3991 (Pubitemid 35302099)
11. J.E. Jackson A User's Guide for Principal Components 2005 John Wiley & Sons, Inc. Hoboken, NJ
12. A.J. Burnham, R. Viveros, and J.F. MacGregor Frameworks for latent variable multivariate regression J. Chem. 10 1996 31 45 (Pubitemid 126469517)
13. B.S. Dayal, and J.F. MacGregor Improved PLS algorithms J. Chem. 11 1997 73 85 (Pubitemid 127477120)
14. A. Raich, and A. Cinar Statistical process monitoring and disturbance diagnosis in multivariable continuous processes AIChE J. 42 1996 995 1009 (Pubitemid 126468139)
15. L.H. Chiang, E.L. Russell, and R.D. Braatz Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least-squares, and principal component analysis Chemom. Intell. Lab. Syst. 50 2000 243 252 (Pubitemid 30136027)
16. L.H. Chiang, M.E. Kotanchek, and A.K. Kordon Fault diagnosis based on fisher discriminant analysis and support vector machines Comput. Chem. Eng. 28 2004 1389 1401 (Pubitemid 38609090)
17. S. Yoon, and J. MacGregor Fault diagnosis with multivariate statistical models part I: using steady state fault signatures J. Process Control 11 2001 387 400 (Pubitemid 32408863)
18. S.J. Qin, and W. Li Detection, identification, and reconstruction of faulty sensors with maximized sensitivity AIChE J. 45 1999 1963 1976 (Pubitemid 29446531)
19. S.J. Qin, and W. Li Detection and identification of faulty sensors in dynamic processes AIChE J. 47 2001 1581 1593 (Pubitemid 36858841)
20. J. Gertler, W. Li, and Y. Huang Isolation enhanced principal component analysis AIChE J. 45 1999 323 334 (Pubitemid 29136617)
21. J.A. Westerhuis, S.P. Gurden, and A.K. Smilde Generalized contribution plots in multivariate statistical process monitoring Chemom. Intell. Lab. Syst. 51 2000 95 114 (Pubitemid 30251933)
22. S.W. Choi, and I.B. Lee Multiblock PLS-based localized process diagnosis J. Process Control. 15 2005 295 306
23. C.H. Zhao, F.L. Wang, and F.R. Gao Enhanced process comprehension and statistical analysis for slow-varying batch processes Ind. Eng. Chem. Res. 47 2008 9996 10008
24. C.F. Alcala, and S.J. Qin Reconstruction-based contribution for process monitoring Automatica 45 2009 593 1600
25. L. Gang, C.F. Alcala, and S.J. Qin Generalized reconstruction-based contributions for output-relevant fault diagnosis with application to the Tennessee Eastman process IEEE Trans. Control. Syst. Technol. 19 2011 1114 1127
26. R. Dunia, and S.J. Qin Subspace approach to multidimensional fault identification and reconstruction AIChE J. 44 1998 1813 1831 (Pubitemid 28540264)
27. H.H. Yue, and S.J. Qin Reconstruction-based fault identification using a combined index Ind. Eng. Chem. Res. 40 2001 4403 4414 (Pubitemid 33009817)
28. G. Li, S.J. Qin, and D.H. Zhou Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models Ind. Eng. Chem. Res. 49 2010 9175 9183
29. C.A. Lowry, and D.C. Montgomery A review of multivariate control charts IIE Trans. 27 1995 800 810
30. P. Nomikos, and J.F. MacGregor Multivariate SPC charts for monitoring batch processes Technometrics 37 1995 41 59
31. B.W. Silverman Density Estimation for Statistics and Data Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) 1986 Chapman and Hall/CRC Boca Raton, Florida
32. J.J. Downs, and E.F. Vogel A plant-wide industrial process control problem Comput. Chem. Eng. 17 1993 245 255