Fault detection and classification for a process with multiple production grades

Industrial and Engineering Chemistry Research

Volumn 47, Issue 21, 2008, Pages 8250-8262

  Liu, Jialin ^a  

^a FORTUNE INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE MATRIX; FAULT DETECTION; RECURSIVE FUNCTIONS; THERMOPLASTICS; TIME VARYING SYSTEMS;

CHALLENGING PROBLEMS; CLUSTER CENTERS; CLUSTER PARAMETERS; CONTROL LIMITS; FAULT DETECTION AND CLASSIFICATIONS; FUZZY C-MEANS; INDUSTRIAL PROCESSES; LINE CLASSIFICATIONS; MARKET DEMANDS; MODEL UPDATES; OPERATING CONDITIONS; OUT-LIERS; PCA SUBSPACES; PLANT DATUMS; POLYETHYLENE PROCESSES; PRINCIPAL COMPONENTS; PROCESS MEASUREMENTS; RECURSIVE FORMULAS; REFERENCE DATUMS; SUB SPACES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 56749095428     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie0710014     Document Type: Article

References (13)

1. Marsili-Libelli, S.; Müller, A. Adaptive Fuzzy Pattern Recognition in the Anaerobic Digestion Process. Pattern Recogn. Lett. 1996, 17,651.
2. Teppola, P.; Mujunen, S.; Minkkinen, P. Adaptive Fuzzy c-Means Clustering in Process Monitoring. Chemom. Intell. Syst. 1999, 45, 23.
3. Chu, Y.; Qin, S. J.; Han, C. Fault Detection and Operation Mode Identification Based on Pattern Classification with Variable Selection. Ind. Eng. Chem. Res. 2004, 43, 1701.
4. Wise, B. M.; Gallagher, N. B. The Process Chemometrics Approach to Process Monitoring and Fault Detection. J. Process Control 1996, 6, 329.
5. Kourti, T.; Lee, J.; MacGregor, J. F. Experiences with Industrial Applications of Projection Methods for Multivariate Statistical Process Control. Comput. Chem. Eng. 1996, 20, S745.
6. Qin, S. J. Statistical Process Monitoring: Basics and Beyond. J. Chemom. 2003, 17, 480.
7. Li, W.; Yue, H. H.; Valle-Cervantes, S.; Qin, S. J. Recursive PCA for Adaptive Process Monitoring. J. Process Control 2000, 10, 471.
8. Choi, S. W.; Martin, E. B.; Morris, A. J.; Lee, I. Adaptive Multivariate Statistical Process Control for Monitoring Time-varying Processes. Ind. Eng. Chem. Res. 2006, 45, 3108.
9. Krishnapuram, R.; Keller, J. M. A Possiblistic Approach to Clustering. IEEE Trans. Fuzzy Syst. 1993, 1, 98.
10. Krishnapuram, R.; Keller, J. M. The Possibilistic c-Means Algorithm: Insights and Recommendations. IEEE Trans. Fuzzy Syst. 1996, 4, 385.
11. Liu, J. Process Monitoring Using Bayesian Classification on PCA Subspace. Ind. Eng. Chem. Res. 2004, 43, 7815.
12. Gustafson, D.; Kessel, W. Fuzzy Clustering with a Fuzzy Covariance Matrix. In Proceedings of IEEE Conference on Decision and Control, San Diego, CA, USA; Institute of Electrical and Electronics Engineers: New York, 1979; p 761.
13. Theodoridis, S.; Koutroumbas, K. Pattern Recognition; Academic Press: New York, 1999.