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Réseau bayésien dynamique hybride : application à la modélisation de la fiabilité de systèmes à espaces d'états discrets

Abstract : Reliability analysis is an integral part of system design and operation, especially for systems running critical applications. Recent works have shown the interest of using Bayesian Networks in the field of reliability, for modeling the degradation of a system. The Graphical Duration Models are a specific case of Bayesian Networks, which make it possible to overcome the Markovian property of dynamic Bayesian Networks. They adapt to systems whose sojourn-time in each state is not necessarily exponentially distributed, which is the case for most industrial applications. Previous works, however, have shown limitations in these models in terms of storage capacity and computing time, due to the discrete nature of the sojourn time variable. A solution might be to allow the sojourn time variable to be continuous. According to expert opinion, sojourn time variables follow a Weibull distribution in many systems. The goal of this thesis is to integrate sojour time variables following a Weibull distribution in a Graphical Duration Model by proposing a new approach. After a presentation of the Bayesian networks, and more particularly graphical duration models, and their limitations, this report focus on presenting the new model allowing the modeling of the degradation process. This new model is called Weibull Hybrid Graphical Duration Model. An original algorithm allowing inference in such a network has been deployed. Various so built databases allowed to learn on one hand a Graphical Duration Model, and on an other hand a Graphical Duration Model Hybrid - Weibull, in order to compare them, in term of learning quality, of inference quality, of compute time, and of storage space
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Submitted on : Tuesday, December 3, 2019 - 10:26:40 AM
Last modification on : Tuesday, December 8, 2020 - 10:20:25 AM
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  • HAL Id : tel-02390519, version 1



Florence Petiet. Réseau bayésien dynamique hybride : application à la modélisation de la fiabilité de systèmes à espaces d'états discrets. Automatique. Université Paris-Est, 2019. Français. ⟨NNT : 2019PESC2014⟩. ⟨tel-02390519⟩



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