» Download Article

Authors
Riccardo Muradore; Fabrizio Bezzo

Identifier
10.3182/20060329-3-AU-2901.00041

Index Terms
PLS,subspace identification methods

Abstract
A novel algorithm is presented for the design of inferential estimators for process monitoring and control. The algorithm aims at integrating Partial Least Squares (PLS) techniques and Subspace Identification Methods (SIM) to exploit the main advantages of both methodologies. In particular, the algorithm will retain the PLS computational robustness in dealing with large sets of correlated inputs and outputs, whilst profiting by the SIM dynamic description of the system being investigated.

References

[1] Geladi, P. and B. R. Kowalski (1986). Partial
    least-squares regression: a tutorial. Analytica
    Chimica Acta 185, 1-17.
[2] Helland, I. S. (1988). On the structure of partial
    least squares regression. Commun. Statist. B-Simulation
     Comput. 17(2), 581-607.
[3] Helland, I. S. (1990). Partial least squares regression
     and statistical models. Scand. J. Statist.
    17, 97-114.
[4] Ku, W., R. H. Storer and C. Georgakis (1995). Disturbance
     detection and isolation by dynamicic
     principal component analysis. Chemometrics
     and Intelligent Laboratory Systems
    30, 179-196.
[5] Lakshminarayanan, S., S. L. Shah and K. Nadakumar
     (1997). Modeling and control of multivariable
     processes: dynamic pls approach.
    AIChE J. 43(9), 2307-2322.
[6] Lindquist, A. and G. Picci (1996). Canonical correlation
     analysis, approximate covariance extension,
     and identification of stationary time
    series. Automatica 32(5), 709-733.
[7] Negiz, A. and A. Çinar (1997). Statistical
    monitoring of multivariable dynamic processes
     with state-space models. AIChE J.
    43(8), 2002-2020.
[8] Shi, R. and J. F. MacGregor (2000). Modeling
    of dynamic systems using latent variable
    and subspace methods. J. of Chemometrics
    14, 423-439.
[9] Treasure, R. J., U. Kruger and J. E. Cooper (2004).
    Dynamic multivariate statistical process control
     using subspace identification. J. Process
    Control 14, 279-292.
[10] Van Overschee, P. and B. De Moor (1993). Subspace
      algorithms for the stochastic identification
      problem. Automatica 29(3), 649-660.
[11] Van Overschee, P. and B. De Moor (1994). N4sid:
     Subspace algorithms for the identification
     of combined deterministic-stochastic systems.
     Automatica 30(1), 75-93.
[12] Wang, J. and S. J. Qin (2002). A new subspace
     identification approach based on principal
     component analysis. Journal of Process Control
      12, 841-855.