Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms

Journal of the Taiwan Institute of Chemical Engineers

Volumn 41, Issue 4, 2010, Pages 475-481

  Jeng, Jyh Cheng ^a  

^a NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Adaptive process monitoring; Fault detection; Moving window PCA; Rank one matrix update; Recursive PCA

Indexed keywords

ADAPTIVE PROCESS; ADAPTIVE PROCESS MONITORING; CONTROL LIMITS; EFFICIENT ALGORITHM; IN-PROCESS MONITORING; MOVING WINDOW; PCA ALGORITHMS; PCA MODEL; RANK-ONE MATRIX; SIMULATION RESULT;

ALGORITHMS; COVARIANCE MATRIX; FAULT DETECTION; PROCESS CONTROL; PROCESS MONITORING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77954659382     PISSN: 18761070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jtice.2010.03.015     Document Type: Article

References (18)

1. Champagne B. Adaptive Eigendecomposition of Data Covariance Matrices Based on First-order Perturbations. IEEE Trans. Signal Process. 1994, 42:2758.
2. Choi S.W., Martin E.B., Morris A.J., Lee I.B. Adaptive Multivariate Statistical Process Control for Monitoring Time-Varying Processes. Ind. Eng. Chem. Res. 2006, 45:3108.
3. Erdogus D., Rao Y.N., Peddaneni H., Hegde A., Principe J.C. Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation. EURASIP J. Appl. Signal Process. 2004, 12:2034.
4. He X.B., Yang Y.P. Variable MWPCA for Adaptive Process Monitoring. Ind. Eng. Chem. Res. 2008, 47:419.
5. Jackson J.E. A User's Guide to Principal Components 1991, John-Wiley & Sons Inc., New York, USA.
6. Jackson J.E., Mudholkar G. Control Procedures for Residuals Associated with Principal Component Analysis. Technometrics 1979, 21:341.
7. Jin H., Lee Y.H., Lee G., Han C. Robust Recursive Principal Component Analysis Modeling for Adaptive Monitoring. Ind. Eng. Chem. Res. 2006, 45:696.
8. Kourti T., MacGregor J.F. Process Analysis, Monitoring and Diagnosis Using Multivariate Projection Methods. Chemom. Intell. Lab. Syst. 1995, 28:3.
9. Li W., Yue H.H., Valle-Cervantes S., Qin S.J. Recursive PCA for Adaptive Process Monitoring. J. Process Control 2000, 10:471.
10. Liu X., Kruger U., Littler T., Xie L. Moving Window Kernel PCA for Adaptive Monitoring of Nonlinear Processes. Chemom. Intell. Lab. Syst. 2009, 96:132.
11. MacGregor J.F., Kourti T. Statistical Process Control of Multivariate Process. Control Eng. Pract. 1995, 3:403.
12. Malinowski F.R. Factor Analysis in Chemistry 1991, Wiley-Interscience, New York, USA.
13. Nomikos P., MacGregor J.F. Multivariate SPC Charts for Monitoring Batch Processes. Technometrics 1995, 37:41.
14. Raich A.C., Cinar A. Multivariate Statistical Methods for Monitoring Continuous Processes: Assessment of Discrimination Power of Disturbance Models Diagnosis of Multiple Disturbances. Chemom. Intell. Lab. Syst. 1995, 30:37.
15. Wang X., Kruger U., Irwin G.W. Process Monitoring Approach Using Fast Moving Window PCA. Ind. Eng. Chem. Res. 2005, 44:5691.
16. Wise B.M., Gallagher N.B. The Process Chemometrics Approach to Process Monitoring and Fault Detection. J. Process Control 1996, 6:329.
17. Wold S. Exponentially Weighted Moving Principal Component Analysis and Project to Latent Structures. Chemom. Intell. Lab. Syst. 1994, 23:149.
18. Yoon S., MacGregor J.F. Fault Diagnosis with Multivariate Statistical Models Part I: Using Steady State Fault Signatures. J. Process Control 2001, 11:387.