Comprehensive subspace decomposition with analysis of between-mode relative changes for multimode process monitoring

Industrial and Engineering Chemistry Research

Volumn 54, Issue 12, 2015, Pages 3154-3166

  Zhao, Chunhui ^a,b   Wang, Wei ^a   Qin, Yan ^a   Gao, Furong ^a,c  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^b School of Information Science and Engineering   (China)

^c HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; FAULT DETECTION; PROCESS CONTROL; PROCESS MONITORING;

MULTI-MODE PROCESS; MULTIMODE ANALYSIS; MULTIMODE PROCESS MONITORING; PROCESS CHARACTERISTICS; PROCESS UNDERSTANDING; RESIDUAL SUBSPACES; STATISTICAL CHARACTERISTICS; SUBSPACE DECOMPOSITION;

MONITORING;

EID: 84926306551     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie504380c     Document Type: Article

References (35)

1. Hsu, C. C.; Su, C. T. An Adaptive Forecast-Based Chart for Non-Gaussian Processes Monitoring: with Application to Equipment Malfunctions Detection in a Thermal Power Plant IEEE Trans. Control Syst. Technol. 2011, 19, 1245-1250
2. Zhao, C. H.; Sun, Y. X. Multi-space Total Projection to Latent Structures and Its Application to Online Process Monitoring IEEE Trans. Control Syst. Technol. 2014, 22, 868-883
3. Subrahmanya, N.; Shin, Y. C. A Data-Based Framework for Fault Detection and Diagnostics of Non-linear Systems with Partial State Measurement Eng. Appl. Artif. Intell. 2013, 26, 446-455
4. He, B.; Zhang, J.; Chen, T.; Yang, X. H. Penalized Reconstruction-Based Multivariate Contribution Analysis for Fault Isolation Ind. Eng. Chem. Res. 2013, 52, 7784-7794
5. Van. Den. Kerkhof, P.; Vanlaer, J.; Gins, G.; Impe, J. F. M. V. Analysis of Smearing-out in Contribution Plot Based Fault Isolation for Statistical Process Control Chem. Eng. Sci. 2013, 104, 285-293
6. Bin, Z.; Sconyers, C.; Byington, C.; Patrick, R.; Orchard, M. E.; Vachtsevanos, G. A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection IEEE Trans. Ind. Electron. 2011, 58, 2011-2018
7. Zhao, C. H.; Lu, N. Y. Statistical Monitoring and Quality Analysis for Batch Processes; Science Press: Beijing, 2014.
8. Kruger, U.; Kumar, S.; Littler, T. Improved Principal Component Monitoring Using the Local Approach Automatica 2007, 43, 1532-1542
9. Muradore, R.; Fiorini, P. A PLS-Based Statistical Approach for Fault Detection and Isolation of Robotic Manipulators IEEE Trans. Ind. Electron. 2012, 59, 3167-3175
10. Liu, J. L.; Chen, D.-S. Fault Isolation Using Modified Contribution Plots Comput. Chem. Eng. 2014, 61, 9-19
11. Raich, A.; Cinar, A. Statistical Process Monitoring and Disturbance Diagnosis in Multivariable Continuous Processes AIChE J. 1996, 42, 995-1009
12. Jackson, J. E. A User's Guide for Principal Components; Wiley: New York, 2005.
13. Wold, S.; Esbensen, K.; Geladi, P. Principal Component Analysis. Chemom Intell. Lab. Syst. 1987, 2, 37-52
14. Burnham, A. J.; Viveros, R.; MacGregor, J. F. Frameworks for Latent Variable Multivariate Regression J. Chem. 1996, 10, 31-45
15. Dayal, B. S.; MacGregor, J. F. Improved PLS Algorithms J. Chem. 1997, 11, 73-85
16. Hwang, D. H.; Han, C. H. Real-Time Monitoring for a Process with Multiple Operation Modes Control Eng. Pract. 1999, 7, 891-902
17. Lane, S.; Martin, E. B.; Kooijmans, R.; Morris, A. J. Performance Monitoring of a Multi-product Semi-batch Process J. Process Control 2011, 11, 1-11
18. Yoo, C. K.; Villez, K.; Lee, I.; Rosen, C.; Vanrolleghem, P. A. Multi-model Statistical Process Monitoring and Diagnosis of a Sequencing Batch Reactor Biotechnol. Bioeng. 2007, 96, 687-701
19. Zhao, S. J.; Zhang, J.; Xu, Y. M. Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models Ind. Eng. Chem. Res. 2004, 43, 7025-7035
20. Zhao, S. J.; Zhang, J.; Xu, Y. M. Performance Monitoring of Processes with Multiple Operating Modes through Multiple PLS Models J. Process Control 2006, 16, 763-772
21. Yu, J.; Qin, S. J. Multimode Process Monitoring with Bayesian Inference-Based Finite Gaussian Mixture Models AIChE J. 2008, 54, 1811-1829
22. Yu, J. A New Fault Diagnosis Method of Multimode Processes Using Bayesian Inference Based Gaussian Mixture Contribution Decomposition Eng. Appl. Artif. Intel. 2013, 26, 456-466
23. Lee, Y. H.; Jin, H. D.; Han, C. H. On-line Process State Classification for Adaptive Monitoring Ind. Eng. Chem. Res. 2006, 45, 3095-3107
24. Jin, H. D.; Lee, Y. H.; Lee, G.; Han, C. H. Robust Recursive Principal Component Analysis Modeling for Adaptive Monitoring Ind. Eng. Chem. Res. 2006, 45, 696-703
25. Zhao, C. H.; Gao, F. R.; Niu, D. P.; Wang, F. L. A Two-Step Basis Vector Extraction Strategy for Multiset Variable Correlation Analysis Chemom. Intell. Lab. Syst. 2011, 107, 147-154
26. Zhao, C. H.; Yao, Y.; Gao, F. R.; Wang, F. L. Statistical Analysis and Online Monitoring for Multimode Processes with between-Mode Transitions Chem. Eng. Sci. 2010, 65, 5961-5975
27. Zhang, Y. W.; Wang, C.; Lu, R. Q. Modeling and Monitoring of Multimode Process Based on Subspace Separation Chem. Eng. Res. Des. 2013, 91, 831-842
28. Zhao, C. H. Concurrent Phase Partition and between-Mode Statistical Analysis for Multimode and Multiphase Batch Process Monitoring AIChE J. 2014, 60, 559-573
29. Lowry, C. A.; Montgomery, D. C. A Review of Multivariate Control Charts IIE Trans. 1995, 27, 800-810
30. Box, G. E. Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in One-way Classification Ann. Math. Stat. 1954, 25, 290-302
31. Jackson, J. E.; Mudholkar, G. S. Control Procedures for Residuals Associated with Principal Component Analysis Technometrics 1979, 21, 341-349
32. Downs, J. J.; Vogel, E. F. A Plant-wide Industrial Process Control Problem Comput. Chem. Eng. 1993, 17, 245-255
33. Ricker, N. L. Optimal Steady-State Operation of the Tennessee Eastman Challenge Process Comput. Chem. Eng. 1995, 19, 949-959
34. Duda, R. O.; Hart, P. E. Pattern Classification and Scene Analysis; Wiley: New York, 1973.
35. Martínez, A. M.; Kak, A. C. PCA versus LDA IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23, 228-233