Etude de la pertinence des paramètres stochastiques sur des modèles de Markov cachés
Bernard de Vries Robles, KRATZ, Frédéric · theses.fr (ABES) · 2013
As part of preventive maintenance, many companies are trying to improve the decision support of their experts. This thesis aims to assist our industrial partners in improving their maintenance operations (production of pastries, aluminum smelter and glass manufacturing plant). To model industrial processes, different topologies of Hidden Markov Models have been used, with a view to finding the best topology by studying the relevance of the model outputs (also called signatures). This thesis should make it possible to select a model framework (a framework includes : a topology, a learning & decoding algorithm and a distribution) by assessing the signature given by different synthetic models. To evaluate this « signature », the following widely-used criteria have been applied : Shannon Entropy, Maximum likelihood, Akaike Information Criterion, Bayesian Information Criterion and Statistical tests.