Encyclopedia of Systems and Control

2015 Edition
| Editors: John Baillieul, Tariq Samad

Multiscale Multivariate Statistical Process Control

Reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5058-9_250

Abstract

Dynamic processes, both continuous and batch, are characterised by autocorrelated measurements which are allied to the effects of process dynamics and disturbances. The common multivariate statistical process control (MSPC) approaches have been to use principal component analysis (PCA) or projection to latent structures (PLS) to build a model that captures the simultaneous correlations amongst the variables, but that ignores the serial correlation in the data during normal operations. Under such conditions it is difficult to perform efficient fault detection and diagnosis. An alternative approach to account for the process dynamics in MSPC is to use multiresolution analysis (MRA) by way of wavelet decomposition. Here, the individual measurements are decomposed into different scales (or frequencies) and the signals in each decomposed scale are then used for MSP which provides an indirect way of handling process dynamics.

Keywords

Multiresolution analysis Partial least squares (PLS) Principal component analysis (PCA) Projection to latent structures (PLS) Wavelet transform 
This is a preview of subscription content, log in to check access

Bibliography

  1. Alawi A, Zhang J, Morris AJ (2007) Multiscale Multiblock Batch Monitoring: Sensor and Process Drift and Degradation. DOI: 10.1021/op400337x April 25 2014Google Scholar
  2. Aradhye HB, Bakshi BR, Strauss A, Davis JF (2003) Multiscale SPC using wavelets: theoretical analysis and properties. AIChE J 49(4):939–958Google Scholar
  3. Bakshi BR (1998) Multiscale PCA with application to multivariate statistical process monitoring. AIChE J 44:1596–1610Google Scholar
  4. Birol G, Undey C, Cinar AA (2002) Modular simulation package for fed-batch fermentation: penicillin production. Comput Chem Eng 26:1553–1565Google Scholar
  5. Choi SW, Morris J, Lee I-B (2008) Nonlinear multiscale modelling for fault detection and identification. Chem Eng Sci 63:2252–2266Google Scholar
  6. Daubechies I (1992) Ten lectures on wavelets. SIAM, PhiladelphiaGoogle Scholar
  7. Ganesan R, Das TT, Venkataraman V (2004) Wavelet-based multiscale statistical process monitoring: a literature review. IIE Trans 36:787–806Google Scholar
  8. Kourti T, MacGregor JF (1996) Multivariate SPC methods for process and product monitoring. J Qual Technol 28:409–428Google Scholar
  9. Lee J-M, Yoo C-K, Lee I-B (2004) Fault detection of batch processes using multiway Kernel principal component analysis. Comput Chem Eng 28(9):1837–1847MathSciNetGoogle Scholar
  10. Lee HW, Lee MW, Park JM (2009) Multi-scale extension of PLS algorithm for advanced on-line process monitoring. Chemom Intell Lab Syst 98:201–212Google Scholar
  11. Liu Z, Cai W, Shao X (2009) A weighted multiscale regression for multivariate calibration of near infrared spectra. Analyst 134:261–266Google Scholar
  12. Lu N, Wang F, Gao F (2003) Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis. Ind Eng Chem Res 42:4198–4207Google Scholar
  13. Mallat SG (1998) Multiresolution approximations and wavelet orthonormal bases. Trans Am Math Soc 315:69–87MathSciNetGoogle Scholar
  14. Misra MH, Yue H, Qin SJ, Ling C (2002) Multivariate process monitoring and fault diagnosis by multi-scale PCA. Comp Chem Eng 26:1281–1293Google Scholar
  15. Qin SJ (2003) Statistical process monitoring: basics and beyond. J Chemom 17:480–502Google Scholar
  16. Qin SJ (2012) Survey on data-driven industrial process monitoring and diagnosis. Annu Rev Control 36:220–234Google Scholar
  17. Reis MS, Saraiva PM (2006) Multiscale statistical process control with multiresolution data. AIChE J 52:2107–2119Google Scholar
  18. Shao R, Jia F, Martin EB, Morris AJ (1999) Wavelets and nonlinear principal components analysis for process monitoring. Control Eng Pract 7:865–879Google Scholar
  19. Shao X-G, Leung AK-M, Chau F-T (2004) Wavelet: a new trend in chemistry. Acc Chem Res 36:276–283Google Scholar
  20. Teppola P, Minkkinen P (2000) Wavelet-PLS regression models for both exploratory data analysis and process monitoring. J Chemom 14:383–399Google Scholar
  21. Yoon S, MacGregor JF (2004) Principal-component analysis of multiscale data for process monitoring and fault diagnosis. AIChE J 50(11):2891–2903Google Scholar
  22. Zhang Y, Ma C (2011) Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS. Chem Eng Sci 66:64–72Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.School of Chemical Engineering and Advanced MaterialsCentre for Process Analytics and Control Technology, Newcastle UniversityNewcastle Upon TyneUK