Graphical enhancement to support PCA-based process monitoring and fault diagnosis

ISA Transactions

Volumn 43, Issue 4, 2004, Pages 639-653

  Ralston, Patricia ^a   Depuy, Gail ^a   Graham, James H ^a  

^a University of Louisville   (United States)

Author keywords

Confidence limits; MSPC; PCA; PLS_Toolbox

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; MATHEMATICAL MODELS; MATRIX ALGEBRA; SET THEORY; STATISTICS; VECTORS;

CONFIDENCE LIMITS; CORRELATION MATRIX; PROCESS MONITORING;

PRINCIPAL COMPONENT ANALYSIS;

ARTICLE; ARTIFICIAL INTELLIGENCE; COMPARATIVE STUDY; COMPUTER GRAPHICS; COMPUTER INTERFACE; COMPUTER SIMULATION; EQUIPMENT; EQUIPMENT DESIGN; EVALUATION; FEEDBACK SYSTEM; MATHEMATICAL COMPUTING; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MODEL; VALIDATION STUDY;

ARTIFICIAL INTELLIGENCE; COMPUTER GRAPHICS; COMPUTER SIMULATION; EQUIPMENT DESIGN; EQUIPMENT FAILURE; EQUIPMENT FAILURE ANALYSIS; EQUIPMENT SAFETY; FEEDBACK; MODELS, STATISTICAL; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PRINCIPAL COMPONENT ANALYSIS; USER-COMPUTER INTERFACE;

EID: 7544230420     PISSN: 00190578     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0019-0578(07)60174-8     Document Type: Article

References (19)

1. Ralston, P., DePuy, G., and Graham, J., Computer-based monitoring and fault diagnosis: A chemical process case study. ISA Trans. 40, 85-98 (2001).
2. Wise, B. and Gallagher, N., PLS_Toolbox Version 2.01 for use with MATLAB™. Eigenvector Research, Manson, WA, 1998.
3. Kresta, J., MacGregor, J., and Marlin, T., Multivariate statistical monitoring of process operating performance. Can. J. Chem. Eng. 69, 35-47 (1991).
4. Wise, B., Ricker, N., Veltkamp, D., and Kowalski, B., A theoretical basis for the use of principal components models for monitoring multivariate processes. Process Control Qual. 1 (1), 41-51 (1990).
5. Uhl, S., Principal components analysis for process control. Master of Engineering Thesis, University of Louisville, 1999.
6. Newton, A., Using confidence intervals on PCA residuals to detect and diagnose process faults. Master of Engineering Thesis, University of Louisville, 1999.
7. Jolliffe, I., Principal Component Analysis. Springer-Verlag, New York, 1986.
8. Wachs, A. and Lewin, D., Improved PCA methods for process disturbance and failure identification, AIChE J. 45 (8), 1688-1694 (1999).
9. Rotem, Y., Wachs, A., and Lewin, D., Ethylene compressor monitoring using model-based PCA. AIChE J. 46 (9), 1825-1828 (2000).
10. Shenton, M., Using spectroscopy with chemometrics to measure polymer molar mass. Polym. Int. 49 (9), 1007-1013 (2000).
11. Shenton, M., Herman, H., Heywood, R., and Stevens, G., The use of spectroscopy with chemometrics to assess the condition and predict the lifetime of paper and oil used as transformer insulation. Eighth Int. Conf. on Dielectric Mat., Meas. and Appl., Publ. No. 473, (2000), pp. 346-51.
12. Weighell, M., Martin, E., and Morris, A., The statistical monitoring of a complex manufacturing process. Appl. Stat. 28 (3/4), 409-426 (2001).
13. Boehl, D., Solle, D., Toussaint, H., Menge, M., Renemann, G., Lindermann, C., Hitzmann, B., and Scheper, T., Application of fluorescence spectroscopy for on-line bioprocess monitoring and control. Proceedings of the SPIE-The International Society for Optical Engineering (2001), Vol. 4201, pp. 50-57.
14. Krager, U., Chen, Q., Sandoz, D., and McFarlane, R., Extended PLS approach for enhanced condition monitoring of industrial processes. AIChE J. 47 (9), 2076-2091 (2001).
15. Graham, J. and Ralston, P., Intelligent Monitoring and Fault Isolation for Chemical Processes. Proc. 7th Intl. Conf. Intelligent Systems, Melun-La-Rochette, France, 1998, pp. 185-189.
16. Fries, T. and Graham, J., An Agent-Based Approach for Fault Diagnosis in Manufacturing Applications. Proc. Intl. Conf. on Computer Applications in Industry and Engineering, San Antonio, TX, 1997, pp. 80-84.
17. Graham, J. and Ralston, P., Intelligent computer-based monitoring and fault isolation for industrial processes. Int. J. Comput. Their Appl. 9 (3), 147-157 (2002).
18. Karpenko, M. and Sepehri, N., Neural network classifiers applied to condition monitoring of a pneumatic process valve actuator. Eng. Applic. Artif. Intell. 50 (3-4), 273-283 (2002).
19. Dash, S., Rengaswamy, R., and Venkatasubramanian, V., Fuzzy logic based trend classification for fault diagnosis of chemical processes. Comput. Chem. Eng. 27 (3), 347-362 (2003).