cet outil permet d'obtenir l'architecture de la famille et de générer les variantes en appliquant quatre types d'opérateurs. Nous l'illustrons avec la modélisation d'éléments d'une cuisine intégrée.

This article presents the results of an analysis simulations of the aerodynamic parameters of an electric sedan vehicle fitted with wind turbines. The model consists of an electric sedan vehicle on which are mounted two SAVONIUS horizontal axis turbines, with four gear systems and four generators needed to convert the maximum energy of the wind blown by the displacement of the vehicle of the wind into electrical energy. The whole consists of a single vehicle and two wind turbine models designed by the SOLIDWORKS 2014 software. The analysis of the digital fluid dynamics was performed by using the turbulence model k-ɛ implemented in SOLIDWORKS FLOW SIMULATION module.

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The prediction of several failure modes of an industrial equipment requires the development of prediction systems with several interdependent parameters. The integration of future maintenance actions with this type of prediction system is a major asset for maintenance decision making. This is even more relevant in the event that after having predicted the future occurrence of several failure modes, the maintenance department does not have the necessary resources to correct all the predicted failure modes at once. In this case it becomes necessary to know how much longer the equipment will work if future partial maintenance actions that do not correct all failure modes are implemented. It is to contribute to the resolution of this problem that we propose an architecture integrating the future maintenance actions to the prediction of several interdependent parameters. This architecture is based on the association of Proportional Integral Derivative regulators to Neuro-Fuzzy systems taking into account the four previous instants to predict the next instant. An application is made with accuracies of the order of 70% for the prediction of the phenomena of fouling of the coolers and of the order of 90% for the prediction of the phenomena of clogging of the filters of the ATLAS COPCO compressor, this with Central Processing Unit values not exceeding one minute.