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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-01162866 , version 1 (11-06-2015)

Identifiants

  • HAL Id : hal-01162866 , version 1

Citer

Audine Subias, Louise Travé-Massuyès, Euriell Le Corronc. Learning chronicles signing multiple scenario instances. 25th International Workshop on Principles of Diagnosis - DX'14, Sep 2014, Graz, Austria. ⟨hal-01162866⟩
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