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The purpose of this article is to present a method for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised classification task. The interest of the proposed method is to take into account new features (and so new informations) in the classifier. These new features are probabilities extracted from a Bayesian network comparing the faulty observations to the...
The purpose of this article is to present a method for industrial process diagnosis with Bayesian network. The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a bayesian network in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault...
The aim of this paper is to present a new method for process diagnosis using a Bayesian network. The mutual information between each variable of the system and the class variable is computed to identify the important variables. To illustrate the performances of this method, we use the Tennessee Eastman Process. For this complex process (51 variables), we take into account three kinds of faults with...
The purpose of this article is to present and evaluate the performance of a new procedure for industrial process diagnosis. This method is based on the use of a Bayesian network as a classifier. But, as the classification performances are not very efficient in the space described by all variables of the process, an identification of important variables is made. This feature selection is made by computing...
The Naive Bayesian Network (NBN) classifier is an optimal classifier (in the sense of minimal classification error rate) in the case of independent descriptors or variables. The presence of dependencies between variables generally reduce his efficiency. In this article, we are proposing a new classification method named Naive Bayesian Network in the Space of Discriminants Factors (NBNSDF) which is...
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