A novel combination method for on-line process monitoring and fault diagnosis

IEEE International Symposium on Industrial Electronics

Volumn IV, Issue , 2005, Pages 1715-1719

  Xu, Zhao ^a   Huihe, Shao ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Fault diagnosis; Multiple support vector machines; Principal component analysis; Process monitoring

Indexed keywords

BENCHMARKING; CONDITION MONITORING; ONLINE SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL METHODS;

FAULT DIAGNOSIS; MULTIPLE SUPPORT VECTOR MACHINES; PROCESS MONITORING; PRODUCT QUALITY; SUPPORT VECTOR MACHINE (SVM);

FAULT TOLERANT COMPUTER SYSTEMS;

EID: 33748340444     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIE.2005.1529191     Document Type: Conference Paper

References (12)

1. Yoon. S., MacGregor.J. F. Statistical and causal model-based approaches to fault detection and isolation. AIChE J. 2000, 46, 1813-1824.
2. Ningyun Lu, Furong Gao. Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis. Ind.Eng.Chem.Res.2003, 42, 4198-4207.
3. MacGregor, J. Statistical process control of multivariate processes. Control Eng. Pract. 1995, 3, 403-414.
4. Nomikos. P, MacGregor. J. F. Monitoring batch processes using multiway principal component analysis. AIChE J. 1994, 40, 1361-1373.
5. Dunia, R; Qin.S.J. Subspace approach to multidimensional fault identification and reconstruction. AIChE J. 1998 44(8), 1813-1831
6. Gertler. J, Li. W, Huang. Y, & McAvoy. T. Isolation enhanced principal component analysis. American Institute of Chemical Engineering Journal, 1999 45(2), 323-334.
7. Weilu Lin, Yu Qian. Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis. Com & Chem Eng 2000, 24, 423-429
8. Leo H. Chiang, Evan L. Russell, Richard D. Braatz Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chem & Inte Lab Sys 2000,50,243-252.
9. V.N. Vapnik. An overview of statistical Learning Theory IEEE Tram. Neural Network, 1999, 10, pp 988-999
10. Mart Hearst. Support Vector Machines, IEEE Intelligent Systems, July/August 1998, 18-28.
11. Downs, J. H, Vogel. E. F. A plant-wide industrial process control problem. Comput. Chem. Eng. 1993, 17, 245-255.
12. McAvoy. T, J. Ye, N. Base level control for the Tennessee Eastman problem. Comput. Chem. Eng. 1994, 18, 383-413.