Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures

AIChE Journal

Volumn 59, Issue 2, 2013, Pages 496-504

  Qin, S Joe ^a   Zheng, Yingying ^a  

^a University of Southern California   (United States)

Author keywords

Concurrent projection to latent structures; Input relevant fault detection; Output relevant fault detection; Process monitoring; Quality monitoring

Indexed keywords

CO-VARIATIONS; FAULT MONITORING; INPUT AND OUTPUTS; INPUT SPACE; INPUT-OUTPUT; MONITORING METHODS; PROCESS FAULTS; PROJECTION TO LATENT STRUCTURES; QUALITY MONITORING; RESIDUAL SUBSPACES; TENNESSEE EASTMAN CHALLENGE;

BIOTECHNOLOGY; CHEMISTRY;

PROCESS MONITORING;

EID: 84872859706     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.13959     Document Type: Article

References (27)

1. Kresta J, MacGregor JF, Marlin TE. Multivariable statistical monitoring of process operating performance. In: AIChE Annual Meeting, San Francisco, CA, 1989: Paper 164a.
2. Kresta J, MacGregor JF, Marlin TE. Multivariate statistical monitoring of processes. Can J Chem Eng. 1991;69(1):35-47.
3. Wise B, Ricker L, Veltkamp D. Upset and sensor failure detection in multivariate processes. In: AIChE Annual Meeting, San Francisco, CA, 1989: Paper 164b.
4. Wise BM, Ricker NL. Recent advances in multivariate statistical process control: improving robustness and sensitivity. In: Proceedings of the IFAC. ADCHEM Symposium. Toulouse, France, Oct. 14-16, 1991:125-130.
5. Jackson J. A User's Guide to Principal Components. New York: Wiley-Interscience, 1991.
6. MacGregor JF, Jaeckle C, Kiparissides C, Koutoudi M. Process monitoring and diagnosis by multiblock PLS methods. AIChE J. 1994;40:826-828.
7. Miller P, Swanson R, Heckler C. Contribution plots: the missing link in multivariate quality control. In: Fall Conference of the ASQC and ASA, Milwaukee, WI, 1993.
8. Dunia R, Qin SJ, Edgar TF, McAvoy TJ. Identification of faulty sensors using principal component analysis. AIChE J. 1996;42:2797-2812.
9. Raich A, Cinar A. Statistical process monitoring and disturbance diagnosis in multivariate continuous processes. AIChE J. 1996;42:995-1009.
10. Dunia R, Qin SJ. Joint diagnosis of process and sensor faults using principal component analysis. Control Eng Pract. 1998;6:457-469.
11. Yue H, Qin S. Fault reconstruction and identification for industrial processes. In: AIChE Annual Meeting, Miami, FL, 1998.
12. Qin S, Li W. Detection, Identification and reconstruction of faulty sensors with maximized sensitivity. AIChE J. 1999;45:1963-1976.
13. Dunia R, Qin SJ. Subspace approach to multidimensional fault identification and reconstruction. AIChE J. 1998;44:1813-1831.
14. Wise B, Gallagher N. The process chemometrics approach to process monitoring and fault detection. J Process Control. 1996;6:329-348.
15. Qin SJ. Statistical process monitoring: basics and beyond. J Chemom. 2003;17:480-502.
16. Chiang L, Russell E, Braatz R. Fault Detection and Diagnosis in Industrial Systems. Advanced Textbooks in Control and Signal Processing. London, Great Britain: Springer-Verlag, 2001.
17. Li G, Qin SJ, Zhou D. Geometric properties of partial least squares for process monitoring. Automatica. 2010;46:204-210.
18. Zhou D, Li G, Qin SJ. Total projection to latent structures for process monitoring. AIChE J. 2010;56:168-178.
19. Geladi P, Kowalski BR. Partial least-squares regression: a tutorial. Anal Chim Acta. 1986;185:1-17.
20. Hoskuldsson A. PLS regression methods. J Chemom. 1988;2:211-228.
21. de Jong S. SIMPLS: an alternative approach to partial least squares regression. Chemom Intell Lab Syst. 1993;18:251-263.
22. Trygg J, Wold S. Orthogonal projections to latent structures (O-PLS). J Chemom. 2002;16(3):119-128.
23. Wold S, Antti H, Lindgren F, Öhman J. Orthogonal signal correction of near-infrared spectra. Chemom Intell Lab Syst. 1998;44(1-2):175-185.
24. Jackson JE, Mudholkar G. Control procedures for residuals associated with principal component analysis. Technometrics. 1979;21:341-349.
25. Box G. Some theorems on quadratic forms applied in the study of analysis of variance problems, I. Effect of inequality of variance in the one-WAY classification. Ann Math Stat. 1954;25:290-302.
26. Downs JJ, Vogel EF. A plant-wide industrial process control problem. Comput Chem Eng. 1993;17:245-255.
27. Lyman PR, Georgakis C. Plant-wide control of the Tennessee Eastman problem. Comput Chem Eng. 1995;19:321-331.