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Communication Dans Un Congrès Année : 2010

Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions.

Résumé

Condition-based maintenance is nowadays considered as a key-process in maintenance strategies and prognostics appears to be a very promising activity as it should permit to not engage inopportune spending. Various approaches have been developed and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets since a lot of methods rely on probability theory and/or on artificial neural networks. This step is thus time-consuming and generally made in batch mode which can be restrictive in practical application when few data are available. A method for prognostics is proposed to face up this problem of lack of information and missing prior knowledge. The approach is based on the integration of three complementary modules and aims at predicting the failure mode early while the system can switch between several functioning modes. The three modules are: 1) observation selection based on information theory and Choquet Integral, 2) prediction relying on an evolving real-time neuro-fuzzy system and 3) classification into one of the possible functioning modes using an evidential Markovian classifier based on Dempster-Shafer theory. Experiments concern the prediction of an engine health based on more than twenty observations.
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Dates et versions

hal-00459310 , version 1 (23-02-2010)

Identifiants

  • HAL Id : hal-00459310 , version 1

Citer

Emmanuel Ramasso, Rafael Gouriveau. Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions.. IEEE Prognostics & Systems Health Management Conference, PHM'2010., Jan 2010, Macau, China. 9 p. ⟨hal-00459310⟩
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