Optimisation et maîtrise statistique des processus versus approche par ondelettes

Dans cet article nous présentons trois nouvelles contributions portant sur l\u27utilisation des ondelettes dans le contexte de l\u27optimisation et la maîtrise des processus en qualité. Nous démontrons que la transformée en ondelettes discrètes orthogonales, en utilisant l\u27ondelette Haar, est équivalente aux cartes de contrôle Xbar-R, utilisées en qualité pour le suivi simultané de la moyenne et de la dispersion. Ensuite, nous présentons l\u27équivalence entre le rapport de vraisemblance pour détecter l\u27instant du changement d\u27une moyenne, et la transformée en ondelettes continues. Enfin, nous montrons que la transformation en ondelettes discrètes, en utilisant l\u27ondelette Haar, est exactement un schema d\u27un plan d\u27expériences complet de type 2^k

Sertraline and Phenytoin Drug Interaction in a Geriatric Patient

This report presents the case of a 78-year-old man residing in a nursing home who presented with a 2-month history of increasing lethargy and confusion. These symptoms coincided with the initiation of sertraline in the patient. Among other medications, he was also taking phenytoin. The medical team concluded that the cause of the patient’s lethargy and confusion was a drug interaction between sertraline and phenytoin. Phenytoin was held, while the sertraline was slowly tapered to discontinuation. The patient’s symptoms resolved soon thereafter. Future research is needed to better guide clinicians in appropriate selection, dosing, and monitoring of selective serotonin reuptake inhibitors with concomitant phenytoin use.Key words: phenytoin, sertraline, SSRIs, drug interactio

Evaluation of the Operational Performance in Implementing Lean Manufacturing and Six Sigma

International audienc

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

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