Fault diagnosis using the hybrid method of signed digraph and partial least squares with time delay: The pulp mill process

Industrial and Engineering Chemistry Research

Volumn 45, Issue 26, 2006, Pages 9061-9074

  Lee, Gibaek ^a   Tosukhowong, Thidarat ^b   Lee, Jay H ^b   Han, Chonghun ^c  

^a Chungju National University   (South Korea)

^b Georgia Institute of Technology   (United States)

^c Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

FAULT DETECTION; PARTIAL LEAST SQUARES; PULP MILL PROCESS;

LEAST SQUARES APPROXIMATIONS; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; PROCESS ENGINEERING;

PAPER AND PULP MILLS;

LEAST SQUARES APPROXIMATIONS; PAPER AND PULP MILLS; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; PROCESS ENGINEERING;

PAPER MILLS; PROCESS CONTROL; PULP MILLS;

EID: 33846218118     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie060793j     Document Type: Article

References (19)

1. Venkatasubramanian, V.; Rengaswamy, R.; Yin, K.; Kavuri, S. N. A Review of Process Fault Detection and Diagnosis Part I: Quantitative Model-based Methods. Comput. Chem. Eng. 2003, 27, 293-311.
2. Malthouse, E. C.; Tamhane, A. C.; Mah, R. S. H. Nonlinear Partial Least Squares. Comput. Chem. Eng. 1997, 21, 875-890.
3. Kazantzis, N.; Kravaris, C.; Wright, R. A. Nonlinear Observer Design for Process Monitoring. Ind. Eng. Chem. Res. 2000, 39, 408-419.
4. Han, Z.; Li, W.; Shah, S. L. Fault Detection and Isolation in the Presence of Process Uncertainties. Control Eng. Pract. 2005, 13, 587-599.
5. Richard, J. P. Time-Delay Systems: an Overview of Some Recent Advances and Open Problems. Automatica 2003, 39, 1667-1694.
6. Jiang, C.; Zhou, D. H. Fault Detection and Identification for Uncertain Linear Time-Delay Systems. Comput. Chem. Eng. 2005, 30, 228-242.
7. Lee, G.; Song, S.-O.; Yoon, E. S. Multiple-Fault Diagnosis Based on System Decomposition and Dynamic PLS. Ind. Eng. Chem. Res. 2003, 42, 6145-6154.
8. Downs, J. J.; Vogel, E. F. A Plant-wide Industrial Process Control Problem. Comput. Chem. Eng. 1993, 17, 245-255.
9. Chiang, L. H.; Russell, E. L.; Braatz, R. D. Fault Detection and Diagnosis in Industrial Systems; Springer: London, 2001.
10. Chen, G.; McAvoy, T. J. Predictive On-Line Monitoring of Continuous Processes. J. Process Control 1998, 8, 409-420.
11. Castro, J. J. Modeling and Control of a Pulp Mill Process, Ph.D. Dissertation, University of Delaware, Newark, DE, 2002.
12. Castro, J. J.; Doyle, F. J., III A Pulp Mill Benchmark Problem for Control: Problem Description. J. Process Control 2004, 14, 17-29.
13. Seborg, D. E.; Edgar, T. F.; Mellichamp, D. A. Process Dynamics and Control; Wiley: New York, 1989.
14. Ahmed, S.; Huang, B.; Shah, S. L. Parameter and Delay Estimation of Continuous-Time Models Using a Linear Filter. J. Process Control 2006, 16, 323-331.
15. Lee, G.; Han, C.; Yoon, E. S. Multiple-Fault Diagnosis of the Tennessee Eastman Process Based on System Decomposition and Dynamic PLS. Ind. Eng. Chem. Res. 2004, 43, 8037-8048.
16. http://www.chemengr.ucsb.edu/~ceweb/faculty/doyle/docs/benchmarks/mill.
17. Qin, S. J.; Li, W. Detection, Identification, and Reconstruction of Faulty Sensors with Maximized Sensitivity. AIChE J. 1999, 45, 1963-1976.
18. Ku, W.; Storer, R. H.; Georgakis, C. Disturbance Detection and Isolation by Dynamic Principal Component Analysis. Chemometrics Intell. Lab. Syst. 1995, 30, 179-196.
19. Qin, S. J.; Li, W. Detection and Identification of Faulty Sensors in Dynamic Processes. AIChE J. 2001, 47, 1581-1593.