Fault Diagnosis in Fuzzy Discrete Event System: Incomplete Models and Learning

M. Traore (1) , Eric Chatelet (1) , Eddie Soulier (2) , Hossam A Gabbar (3)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 Tech-CICO - TECHnologies pour la Coopération, l’Interaction et les COnnaissances dans les collectifs
3 UOIT = Ontario Tech University - University of Ontario Institute of Technology

Nowadays, determining faults in non-stationary environment and that can deal with the problems of fuzziness imprecise-ness and subjectivity is a challenging task in complex systems such as nuclear center, or wind turbines, etc. Our objective in this paper is to develop models based on fuzzy finite state automaton with fuzzy variables describing the industrial process in order to detect anomalies in real time and possibly in anticipation. A diagnosis method has for goal to alert actors responsible for managing operations and resources, able to adapt to the emergence of new procedures or improvisation in the case of unexpected situations. The diagnoser module use the outputs events and membership values of each active state of the model as input events.

Domaines: Performance et fiabilité [cs.PF]

M. Traore, Eric Chatelet, Eddie Soulier, Hossam A Gabbar. Fault Diagnosis in Fuzzy Discrete Event System: Incomplete Models and Learning. International Journal of Prognostics and Health Management, 2015. ⟨hal-02317757⟩