Evaluation of relevance of stochastic parameters on Hidden Markov Models

Bernard Roblès (1) , Manuel Avila (1) , Florent Duculty (1) , Pascal Vrignat (1) , Frédéric Kratz (1)

1 PRISME - Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique

Prediction of physical particular phenomenon is based on knowledge of the phenomenon. This knowledge helps us to conceptualize this phenomenon around different models. Hidden Markov Models (HMM) can be used for modeling complex processes. This kind of models is used as tool for fault diagnosis systems. Nowadays, industrial robots living in stochastic environment need faults detection to prevent any breakdown. In this paper, we wish to evaluate relevance of Hidden Markov Models parameters, without a priori knowledges. After a brief introduction of Hidden Markov Model, we present the most used selection criteria of models in current literature and some methods to evaluate relevance of stochastic events resulting from Hidden Markov Models. We support our study by an example of simulated industrial process by using synthetic model of Vrignat's study (Vrignat 2010). Therefore, we evaluate output parameters of the various tested models on this process, for finally come up with the most relevant model.

Domaines: Automatique

Bernard Roblès, Manuel Avila, Florent Duculty, Pascal Vrignat, Frédéric Kratz. Evaluation of relevance of stochastic parameters on Hidden Markov Models. European Safety and Reliability Conference, Sep 2011, Troyes, France. pp.71. ⟨hal-00626646⟩