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A new procedure based on mutual information for fault diagnosis of industrial systems

Abstract : The purpose of this article is to present a new procedure for industrial process diagnosis. This method is based on bayesian classifiers. A feature selection is done before the classification between the different faults of a process. The feature selection is based on a new result about mutual information that we demonstrate. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these 3 faults. Results are given and compared on the same data with those of other published methods
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https://hal.inria.fr/inria-00517015
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Last modification on : Thursday, May 20, 2021 - 11:46:02 PM
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Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. A new procedure based on mutual information for fault diagnosis of industrial systems. Workshop on Advanced Control and Diagnosis (ACD'06), 2006, Nancy, France. ⟨inria-00517015⟩

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