Monitoringand fault diagnosis using fisher discrimnant analysis

Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

Volumn 2, Issue , 2007, Pages 1100-1105

  Tang, Xiao Chu ^a   Li, Yuan ^a  

^a SHENYANG UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

Author keywords

Fault diagnosis; Fisher discriminant analysis; Principal component analysis; Process monitoring

Indexed keywords

DATA ACQUISITION; DISCRIMINANT ANALYSIS; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS; PROCESS MONITORING;

DISCRIMINATING DATA; FISHER DISCRIMINANT ANALYSIS;

FAILURE ANALYSIS;

EID: 37849022170     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2007.4370308     Document Type: Conference Paper

References (17)

1. Benveniste, A., M. Basseville, G. Mousgakides, "The Asymptotic Local Approach to Change Detection and Model Validation," IEEE Trans. Automat. Contr., 32(7), 583, 1987.
2. Gertler, J., "Survey of Model-Based Failure Detection and Isolation in Complex Plants," IEEE Contr. Syst. Mag., 12, 3, 1998.
3. Isermann, R., "Process Fault detection Based on Modeling and Estimation Methods- A Survey,"Automatica 20, 387, 1984.
4. Fank, P., "Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge-Based Redundancy-A Survey and Some New Results," Automatica, 26, 459, 1990.
5. P. Fasolo and D. E. Seborg. SQC approach to monitoring and fault detection in HVAC control systems. In Proc. Of the American Control Conf., 3055-3059, 1994.
6. D. C. Montgomery. Introduction to Statistical Quality Control. John Wiley and Sons, New York, 1985.
7. B. A. Ogunnaike and W. H. Ray. Process Dynamics, Modeling, and Control. Oxford University Press, New York, 1994.
8. J. F. MacGregor and T. Kourti. Statistical process control of multivariate processes. Control Engineering Practice, 3:403-414, 1995.
9. MacGregor, J. F., T. E. Marlin, J. Kresta, and B. Skagerberg. Multivariate statistical methods in process analysis and control.presented at CPC-4, South Padre Island, Texas, February 18-22, 1991.
10. Jackson, J. E. Principal components and factor analysis: Part 1-Principal components. Journal of Quality Technology, 12: 201-213, 1980.
11. G. Locher, B. Bakshi, G. Stephanopoulos, and K. Schugerl. A method for anautomated rule extraction from raw process data: A case study. Automatisierungstechnik, 44: 138-145, 1996.
12. T. F. Petti, J. Klein, and P. S. Dhurjati. Diagnostic model processor Using deep knowledge for process fault diagnosis. AIChE J., 36: 565-575, 1990.
13. M. J. Piovoso, K. A. Kosanovich, and R. K. Pearson. Monitoring process performance in real time. In Proc. Of the American Control Conf., 2359 - 2363, 1992.
14. Qin, S. J, "Statistical Process Monitoring :Basics and Beyond," J. Chemom., 17,480 ,2003.
15. Q. Peter He and S. Joe Qin, A New Fault Diagnosis Method Using Fault Direction in Fisher Discrimnant Analysis, American Institue of Chemical Engineers 2005.
16. Duda, R. O., P. E.. Hart, and D. G. Stork, Pattern Classification, 2nd ed.,Wiley, New York ,2001.
17. Chiang, L. H., E. L. Tussell, 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, 243, 2000.