TIME DELAY ESTIMATION IN LARGE DATABASES

In data-based modelling, the number and quality of measurements are significant. There may be a great number of variables with different kinds of interactions and delays compared to each other. The methods for delay estimation are widely studied in Control Engineering, but the studies are mainly limited to the two-variable case – i.e. estimating the delay between the manipulated and the controlled variable in the feedback control loop. The situation is greatly changed when dealing with the high number of variables grouped in several groups for modelling or monitoring purposes. The aim of this study is to evaluate time delays using genetic algorithms (GA) and principal component analysis (PCA). Test results origin from two applications: one from the chemical processes and the other from the paper industry. This study is a part of Production 2010-project financed by the National Technology Agency of Finland.