Réseaux de neurones récurrents à fonctions de base radiales. Application à la surveillance dynamique

Monitoring of industrial equipments requires the processing of a certain number of signals sensors. The aim is to detect any deviation by generating alarms. The diagnosis function has to locate then the fail and to identify the cause of the failure. Radial Basis Function (RBF) neural network seems to be a powerful tool for this kind of processing. In order to consider the dynamic of a monitoring proce ss, we propose a new architecture of dynamic radial basis function (RRBF - Recurrent Rad ial Basis Function) neural network. We demonstrate also the advantages of the RRBF for dyn amic monitoring problems like earlier detection of degradation and false alarms. MOTS-CLES : Surveillance, Detection, Diagnostic, Res eaux de neurone temporel, RFR - Reseaux de neurones a fonctions de base radiales.

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