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Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton


Nowadays, determining faults (or critical situations) in nonstationary environment is a challenging task in complex systems such as Nuclear center, or multi-collaboration such as crisis management. A discrete event system or a fuzzy discrete event system approach with a fuzzy role-base may resolve the ambiguity in a fault diagnosis problem especially in the case of multiple faults (or multiple critical situations). The main advantage of fuzzy finite state automaton is that their fuzziness allows them to handle imprecise and uncertain data, which is inherent to real-world phenomena, in the form of fuzzy states and transitions. Thus, most of approaches proposed for fault diagnosis of discrete event systems require a complete and accurate model of the system to be diagnosed. However, in non-stationary environment it is hard or impossible to obtain the complete model of the system. The focus of this work is to propose an evolving fuzzy discrete event system whose an activate degree is associated to each active state and to develop a fuzzy learning diagnosis for incomplete model. Our approach use the fuzzy set of output events of the model as input events of the diagnoser and the output of a fuzzy system should be defuzzified in an appropriate way to be usable by the environment.
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Dates and versions

hal-02563069 , version 1 (05-05-2020)



Moussa Traoré, Eric Chatelet, Eddie Soulier, Hossam A Gabbar. Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton. Annual Conference of the Prognostics and Health Management Society 2014, Sep 2014, Fort Worth, United States. ⟨10.13140/2.1.3681.0568⟩. ⟨hal-02563069⟩
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