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

Moussa Traore, Eric Chatelet, Eddie Soulier, and Hossam A. Gabbar
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Sponsored by the Champagne-Ardenne region and the French ministry of higher education and research
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ijphm_15_020.pdf173.53 KBJuly 30, 2015 - 2:14pm

Nowadays, determining faults in non-stationary environment and that can deal with the problems of fuzziness impreciseness 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.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
020
Page Count: 
12
Submission Keywords: 
diagnosis
prediction
fuzzy automaton
Crisis Management
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Submitted by: 
  
 
 
 

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