Détection de Fautes par Réseaux Bayésiens dans les Procédés Multivariés

L\u27objectif de cet article est de présenter une méthode permettant la détection de fautes d\u27un procédé multivarié, au moyen d\u27un réseau bayésien. Pour ce faire, la détection est assimilée à une tâche de classification telle que l\u27analyse discriminante, cette dernière étant aisément transposable en réseau bayésien. Nous prouvons mathématiquement, dans cet article, l\u27équivalence entre les méthodes de détection usuelles que sont les cartes de contrôle multivariées (cartes T2 de Hotelling et MEWMA) et l\u27analyse discriminante quadratique (modélisée par réseau bayésien), rendant ainsi possible la détection de fautes directement par le biais du réseau bayésien

Fault detection with Conditional Gaussian Network

The main interest of this paper is to illustrate a new representation of the Principal Component Analysis (PCA) for fault detection under a Conditional Gaussian Network (CGN), a special case of Bayesian networks. PCA and its associated quadratic statistics such as T2 and SPE are integrated under a sole CGN. The proposed framework projects a new observation into an orthogonal space and gives probabilities on the state of the system. It could do so even when some data in the sample test are missing. This paper also gives the probabilities thresholds to use in order to match quadratic statistics decisions. The proposed network is validated and compared to the standard PCA scheme for fault detection on the Tennessee Eastman Process and the Hot Forming Process

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

Fault detection of univariate non-Gaussian data with Bayesian network

The purpose of this article is to present a new method for fault detection with Bayesian network. The interest of this method is to propose a new structure of Bayesian network allowing to detect a fault in the case of a non-Gaussian signal. For that, a structure based on Gaussian mixture model is proposed. This particular structure allows to take into account the non-normality of the data. The effectiveness of the method is illustrated on a simple process corrupted by different faults

Distance rejection in a bayesian network for fault diagnosis of industrial systems

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 are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults and to obtain sufficient results in rejection of new types of fault