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

Assessment of the ocean circulation in the Azores region as predicted by a numerical model assimilating altimeter data from Topex/Poseidon and ERS-1 satellites

International audienceTwo years of altimetric data from Topex/Poseidon (October 1992-September 1994) and ERS-1 (October 1992-December 1993) were assimilated into a numerical model of the North Atlantic. The results of these simulations are analysed in the Azores region to assess the performance of our model in this particular region. Maps of instantaneous dynamic topography and transports show that the model performs well in reproducing the velocities and transports of the Azores Front. Drifter data from the Semaphore experiment are also used to study the correlation between the drifter velocities and the corresponding model velocities. Some interesting oceanographic results are also obtained by examining the seasonal and interannual variability of the circulation and the influence of bathymetry on the variability of the Azores Front. Thus, on the basis of our two year experiment, it is possible to confirm the circulation patterns proposed by previous studies regarding the seasonal variations in the origin of the Azores Current. Moreover, it is shown that the Azores Current is quite narrow in the first year of assimilation (1992-1993), but becomes much wider in the second year (1993-1994). The role of the bathymetry appears important in this area since the mesoscale activity is shown to be strongly related to the presence of topographic slopes. Finally, spectral analyses of sea-level changes over time and space are used to identify two types of wave already noticed in other studies: a wave with (300 km)-1 wave number and (120 days)-1 frequency, which is characteristic of mesoscale undulation, and a wave with (600 km)-1 wave number and (250 days)-1 frequency which probably corresponds to a Rossby wave generated in the east of the basin

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

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

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

Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process

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 mutual information between each process variable and the class variable. The performances of this method are evaluated on the data of a benchmark problem: the Tennessee Eastman process. Three kinds of faults are taken into account on this complex process. The objective is to obtain the minimal recognition error rate for these 3 faults. Results are given and compared with results of other authors on the same data

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