Abstract
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.
Highlights
Discrete several transitions from different current fuzzy states lead to event system theory, on modeling and fault di- the same fuzzy state simultaneously, and the possiagnosis, has been successful employed in many areas such as bility of several transitions from one current fuzzy state lead concurrent monitoring and control of complex system
The newly proposed diagnoser approach in the algorithm allows us to deal with the problem of failure diagnosis for fuzzy discrete event system, which many better deal with the problem of fuzziness, impreciseness and uncertainness in fault diagnosis
We have presented the definition of a fuzzy discrete event system and we presented the main advantage of fuzzy automaton, to handle imprecise and uncertain data in non-stationary environment
Summary
A great number of systems can be naturally viewed as dis- membership value This latter can be defined as the possicrete event systems, that why the failure diagnosis problem bility of the transition from current (active) state to the has been investigated via discrete event system approach. The use of fuzzy finite state automaton in fault diagnosis tasks has gained particular attention in the case of fuzzy discrete event dynamic systems (Gerasimos, 2009; Traore, Chatelet, et al, 2014). A fault diagnosis approach based on fuzzy automaton is presented in (Rigatos, 2009; Doostfatemeh & Kremer, 2005) and in this paper the propagation (update) approach of the membership of current state is did by using fuzzy roles min, max functions.
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