aLASQUO/ISTIA, University of Angers, 62, Avenue Notre Dame du Lac, 49000 Angers, France
Abstract:
This paper presents a fault diagnosis procedure based on discriminant analysis and mutual information. In order to obtain good classification performances, a selection of important features is done with a new developed algorithm based on the mutual information between variables. The application of the new fault diagnosis procedure on a benchmark problem, the Tennessee Eastman Process, shows better results than other well known published methods.