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Communication Dans Un Congrès Année : 2014

Learning chronicles signing multiple scenario instances

Résumé

Chronicle recognition is an efficient and robust method for fault diagnosis. The knowledge about the underlying system is gathered in a set of chronicles, then the occurrence of a fault is diagnosed by analyzing the flow of observations and matching this flow with a set of available chronicles. The chronicle approach is very efficient as it relies on the direct association of the symptom, which is in this case a complex temporal pattern, to a situation. Another advantage comes from the efficiency of recognition engines which make chronicles suitable for one-line operation. However, there is a real bottleneck for obtaining the chronicles. In this paper, we consider the problem of learning the chronicles. Because a given situation often results in several admissible event sequences, our contribution targets an extension to multiple event sequences of a chronicle discovery algorithm tailored for one single event sequence. The concepts and algorithms are illustrated with representative and easy to understand examples.
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Dates et versions

hal-02087826 , version 1 (02-04-2019)

Identifiants

  • HAL Id : hal-02087826 , version 1

Citer

Audine Subias, Louise Travé-Massuyès, Euriell Le Corronc. Learning chronicles signing multiple scenario instances. 19th World Congress of the International Federation of Automatic Control (IFAC), Aug 2014, Cape Town, South Africa. Paper ThC15.2. ⟨hal-02087826⟩
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