Advantage of using decorrelated residuals in dynamic principal component analysis for monitoring large-scale systems

Industrial and Engineering Chemistry Research

Volumn 52, Issue 38, 2013, Pages 13685-13698

  Rato, Tiago J ^a   Reis, Marco S ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CURRENT MONITORING; DE CORRELATIONS; DETECTION ABILITY; DYNAMIC PRINCIPAL COMPONENT ANALYSIS; FAULT MAGNITUDES; MONITORING APPROACH;

LARGE SCALE SYSTEMS; PRINCIPAL COMPONENT ANALYSIS;

MONITORING;

EID: 84884943728     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie3035306     Document Type: Article

References (53)

1. Shewhart, W. A. Economic Control of Quality of Manufactured Product; 50th Anniversary Commemorative Reissue; ASQC Quality Press: Milwaukee, WI, 1980.
2. Roberts, S. W. Control Charts Tests Based on Geometric Moving Averages Technometrics 1959, 1 (3) 239-250
3. Page, E. S. Continuous Inspection Schemes Biometrics 1954, 41 (1-2) 100-115
4. Hotelling, H. Multivariate quality control, illustrated by the air testing of sample bombsights. In Selected Techniques of Statistical Analysis; Eisenhart, C.; Hastay, M. W.; Wallis, W. A., Eds.; McGraw-Hill: New York, 1947.
5. Lowry, C. A.; Woodall, W. H.; Champ, C. W.; Rigdon, S. E. A Multivariate Exponentially Weighted Moving Average Control Chart Technometrics 1992, 34 (1) 46-53
6. Jackson, J. E. Quality Control Methods for Several Related Variables Technometrics 1959, 1 (4) 359-377
7. Jackson, J. E.; Mudholkar, G. S. Control Procedures for Residuals Associated with Principal Component Analysis Technometrics 1979, 21 (3) 341-349
8. Kourti, T.; MacGregor, J. F. Process Analysis, Monitoring and Diagnosis, Using Multivariate Projection Methods Chemom. Intell. Lab. Syst. 1995, 28, 3-21
9. Kresta, J. V.; MacGregor, J. F.; Marlin, T. E. Multivariate Statistical Monitoring of Process Operating Performance Can. J. Chem. Eng. 1991, 69, 35-47
10. MacGregor, J. F.; Jaeckle, C.; Kiparissides, C.; Koutoudi, M. Process Monitoring and Diagnosis by Multiblock PLS Methods AIChE J. 1994, 40, 5
11. MacGregor, J. F.; Kourti, T. Statistical Process Control of Multivariate Processes Control Eng. Pract. 1995, 3 (3) 403-414
12. Vasilopoulos, A. V.; Stamboulis, A. P. Modification of Control Chart Limits in the Presence of Data Correlation J. Qual. Technol. 1978, 10 (1) 20-30
13. Lu, C.-W.; Reynolds, M. R., Jr. Cusum Charts For Monitoring An Autocorrelated Process J. Qual. Technol. 2001, 33 (3) 316-334
14. Vermaat, M. B.; Does, R. J. M. M.; Bisgaard, S. EWMA Control Chart Limits for First- and Second-Order Autoregressive Processes Qual. Reliab. Eng. Int. 2008, 24, 573-584
15. Harris, T. J.; Ross, W. H. Statistical Process Control Procedures for Correlated Observations Can. J. Chem. Eng. 1991, 69, 48-57
16. Montgomery, D. C.; Mastrangelo, C. M. Some Statistical Process Control Methods for Autocorrelated Data J. Qual. Technol. 1991, 23 (3) 179-193
17. Negiz, A.; Çinar, A. Statistical Monitoring of Multivariable Dynamic Processes with State-Space Models AIChE J. 1997, 43 (8) 2002-2020
18. Simoglou, A.; Martin, E. B.; Morris, A. J. Dynamic Multivariate Statistical Process Control Using Partial Least Squares and Canonical Variate Analysis Comput. Chem. Eng. 1999, 23 (Suppl.) S277-S280
19. Treasure, R. J.; Kruger, U.; Cooper, J. E. Dynamic Multivariate Statistical Process Control Using Subspace Identification J. Process Control 2004, 14, 279-292
20. Ku, W.; Storer, R. H.; Georgakis, C. Disturbance Detection and Isolation by Dynamic Principal Component Analysis Chemom. Intell. Lab. Syst. 1995, 30, 179-196
21. Komulainen, T.; Sourander, M.; Jämsä-Jounela, S.-L. An Online Application of Dynamic PLS to a Dearomatization Process Comput. Chem. Eng. 2004, 28, 2611-2619
22. Bakshi, B. R. Multiscale PCA with Application to Multivariate Statistical Process Control AIChE J. 1998, 44 (7) 1596-1610
23. Reis, M. S.; Bakshi, B. R.; Saraiva, P. M. Multiscale Statistical Process Control Using Wavelet Packets AIChE J. 2008, 54 (9) 2366-2378
24. Reis, M. S.; Saraiva, P. M. Multiscale Statistical Process Control with Multiresolution Data AIChE J. 2006, 52 (6) 2107-2119
25. Russel, E. L.; Chiang, L. H.; Braatz, R. D. Fault Detection in Industrial Processes Using Canonical Variate Analysis and Dynamic Principal Component Analysis Chemom. Intell. Lab. Syst. 2000, 51, 81-93
26. Kaspar, M. H.; Ray, W. H. Dynamic PLS Modelling for Process Control Chem. Eng. Sci. 1993, 48 (20) 3447-3461
27. Lakshminarayanan, S.; Shah, S. L.; Nandakumar, K. Modeling and Control of Multivariable Processes: Dynamic PLS Approach AIChE J. 1997, 43 (9) 2307-2322
28. Ljung, L. System Identification: Theory for the User, 2 nd ed.; Prentice Hall: Upper Saddle River, NJ, 1999.
29. Box, G. E. P.; Jenkins, G. M.; Reinsel, G. C. Time Series Analysis: Forecasting and Control, 3 rd ed.; Prentice Hall: Upper Saddle River, NJ, 1994.
30. Rato, T. J.; Reis, M. S. Defining the Structure of DPCA Models and Its Impact on Process Monitoring and Prediction Activities Chemom. Intell. Lab. Syst. 2013, 125 (15) 74-86
31. Helland, I. S. On the Structure of Partial Least Squares Regression Commun. Stat.-Simul. 1988, 17 (2) 581
32. Höskuldsson, A. PLS Regression Methods J. Chemom. 1988, 2, 211-228
33. Martens, H.; Naes, T. Multivariate Calibration. Wiley: Chichester, U.K., 1989.
34. Wold, S.; Sjöström, M.; Eriksson, L. PLS Regression: A Basic Tool of Chemometrics Chemom. Intell. Lab. Syst. 2001, 58, 109-130
35. Geladi, P.; Kowalski, B. R. Partial Least-Squares Regression: A Tutorial Anal. Chim. Acta 1986, 185, 1-17
36. Jackson, J. E. A User's Guide to Principal Components; Wiley: New York, 1991.
37. Nelson, P. R. C.; Taylor, P. A.; MacGregor, J. F. Missing Data Methods in PCA and PLS: Score Calculations with Incomplete Observations Chemom. Intell. Lab. Syst. 1996, 35, 45-65
38. Little, R. J. A.; Rubin, D. B. Statistical Analysis with Missing Data, 2 nd ed.; Wiley: New York, 2002.
39. Schneider, T.; Neumaier, A. Algorithm 808: ARFIT-A Matlab Package for the Estimation of Parameters and Eigenmodes of Multivariate Autoregressive Models ACM Trans. Math. Software 2001, 27 (1) 58-65
40. Wood, R. K.; Berry, M. W. Terminal Composition Control of a Binary Distillation Column Chem. Eng. Sci. 1973, 28 (9) 1707-1717
41. Lakshminarayanan, S.; Shah, S. L.; Nandakumar, K. Modeling and Control of Multivariable Processes: Dynamic PLS Approach AIChE J. 1997, 43 (9) 2307-2322
42. Wachs, A.; Lewin, D. R. Improved PCA Methods for Process Disturbance and Failure Identification AIChE J. 1999, 45 (8) 1688-1700
43. Lin, W.; Qian, Y.; Li, X. Nonlinear Dynamic Principal Component Analysis for on-Line Process Monitoring and Diagnosis Comput. Chem. Eng. 2000, 24, 423-429
44. Negiz, A.; Çinar, A. PLS, Balanced, and Canonical Variate Realization Techniques for Identifying VARMA Models in State Space Chemom. Intell. Lab. Syst. 1997, 38, 209-221
45. Shi, R.; MacGregor, J. F. Modeling of Dynamic Systems Using Latent Variable and Subspace Methods J. Chemom. 2000, 14, 423-439
46. Overschee, P. V.; De Moor, B. A Unifying Theorem for Three Subspace System Identification Algorithms Automatica 1995, 31 (12) 1853-1864
47. Ricker, N. L. The Use of Biased Least-Squares Estimators for Parameters in Discrete-Time Pulse-Response Models Ind. Eng. Chem. Res. 1988, 27, 343-350
48. Sharmin, R.; Sundararaj, U.; Shah, S.; Griend, L. V.; Sun, Y.-J. Inferential Sensors for Estimation of Polymer Quality Parameters: Industrial Application of a PLS-Based Soft-Sensor for a LDPE Plant Chem. Eng. Sci. 2006, 61, 6372-6384
49. Arteaga, F.; Ferrer, A. Dealing with Missing Data in MSPC: Several Methods, Different Interpretations, Some Examples J. Chemom. 2002, 16, 408-418
50. Tracy, N. D.; Young, J. C.; Mason, R. L. Multivariate Control Charts for Individual Observations J. Qual. Technol. 1992, 24 (2) 88-95
51. Qin, S. J. Statistical Process Monitoring: Basics and beyond J. Chemom. 2003, 17, 480-502
52. Cinar, A.; Palazoglu, A.; Kayihan, F. Chemical Process Performance Evaluation; CRC Press: Boca Raton, FL, 2007.
53. Nomikos, P.; MacGregor, J. F. Monitoring Batch Processes Using Multiway Principal Component Analysis AIChE J. 1994, 40 (8) 1361-1375