Article Dans Une Revue Computers & Chemical Engineering Année : 1999

Data validation with unknown variance matrix

Résumé

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 pionner 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 likelihood 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.

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hal-00146897 , version 1 (11-12-2024)

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Didier Maquin, Shankar Narasimhan, José Ragot. Data validation with unknown variance matrix. Computers & Chemical Engineering, 1999, 23, pp.S609-S616. ⟨10.1016/S0098-1354(99)80150-1⟩. ⟨hal-00146897⟩
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