167 research outputs found
Bayesian network for the characterization of faults in a multivariate process
The main objective of this paper is to present a new method of detection and characterization with a bayesian network. For that, a combination of two original works is made. The first one is the work of Li et al. [1] who proposed a causal decomposition of the T² statistic. The second one is our previous work on the detection of fault with bayesian networks [2], [3], notably on the modelization of multivariate control charts in a bayesian network. Thus, in the context of multivariate processes, we propose an original network structure allowing deciding if a fault is appeared in the process. More, this structure permits the identification of the variables that are responsible (root causes) of the fault. A particular interest of the method is the fact that the detection and the identification can be made with a unique tool: a bayesian network
Nouvelles composantes informatives pour le diagnostic de fautes des procédés industriels par classification supervisée
Cet article présente une méthode pour le diagnostic des procédés industriels. Nous sommes intéressés par le diagnostic de fautes perçue comme une classification supervisée. L\u27intérêt de la méthode proposée est de prendre en compte de nouvelles composantes (et donc de nouvelles informations) dans le classifieur. Ces nouvelles composantes sont des probabilités extraites d\u27un réseau Bayésien comparant les données de fautes aux données normales de fonctionnement. Les performances de cette méthode sont évaluées sur les données d\u27un problème benchmark : le Tennessee Eastman Process. Trois types de fautes sont prises en comptes sur ce procédé complexe. Nous montrons sur cet exemple que l\u27addition de ces nouvelles composantes permet de faire décroître le taux de mauvaises classifications
Procedure based on mutual information and bayesian networks for the fault diagnosis of industrial systems
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 minimal recognition error rate objective
Fault detection with bayesian network
The purpose of this chapter is to present a method for the fault detection in multivariate process, with a bayesian network. In this context, the detection is viewed as a classification task like the discriminant analysis, which can be transposed in a bayesian network. We prove mathematically the equivalence between the usual detection methods that are the multivariate control charts (Hotelling\u27s T², MEWMA) and the quadratic discriminant analysis (in a bayesian network). So, this makes possible the fault detection with a bayesian network. An application on the Tennessee Eastman Process is given in order to demonstrate the approach
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