Bayesian based fault diagnosis : application to an electrical motor

Amine Mechraoui (1) , Kamal Medjaher (2) , Noureddine Zerhouni (2)

1 GIPSA-SA-IGA - GIPSA - Signal et Automatique pour le Diagnostic et la Surveillance
2 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)

In the literature, several fault diagnosis methods, qualitative as well quantitative, are proposed. The main objective of these methods is in one hand, to allow detection, isolation and identification of faults ; and in the other hand to insure safety, reliability and availability of systems. This paper presents a diagnosis method based on the use of a new and suitable mathematical tool : bayesian networks. Their learning and inference capabilities allow to model complex processes by taking into account the uncertainty and the incompleteness of the provided knowledge. Furthermore, the graphical representation of causal relations existing between variables, events or physical phenomena makes bayesian networks easy to use and leads to models which can be understandable by even a non specialist of the modeled domain.

Mots clés: probabilities.; inference; bayesian networks; probabilities; Diagnosis; fault isolation

Domaines: Automatique / Robotique

Amine Mechraoui, Kamal Medjaher, Noureddine Zerhouni. Bayesian based fault diagnosis : application to an electrical motor. IFAC WC 2008 - 17th IFAC World Congress, Jul 2008, Séoul, South Korea. 6 p. ⟨hal-00298240⟩