Data validation with unknown variance matrix

Didier Maquin (1) , Shankar Narasimhan (2) , José Ragot (1)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 Department of Chemical Engineering

The data validation consists in obtaining an estimation of the true values of process variables that respect the balance equations. Generally, the procedure needs the knowledge of the variance of the measurement errors; unfortunately, in most situations, we only have a rough estimation of this variance and therefore the data validation procedure gives results depending on this poor estimation. A pioneer work of Almasy and Mah (1984) presents a solution to this problem based on the analysis of the constraint residuals. Darouach et al. (1989) developed a slighty different approach based on a maximum likelihhod estimator. Here we present a direct method that simultaneously estimates the variances of the measurement errors and reconciles the data with respect to the balance equations. Some numerical results illustrate the efficiency of the proposed method.

Keywords: Data validation; Variance estimation; Balance equations; Analytical redundancy

Domains: Automatic

Didier Maquin, Shankar Narasimhan, José Ragot. Data validation with unknown variance matrix. 9th European Symposium on Computer Aided Process Engineering, ESCAPE'99, May 1999, Budapest, Hungary. pp.CDROM. ⟨hal-00283924⟩