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Communication Dans Un Congrès Année : 2010

A neuro-fuzzy self built system for prognostics : a way to ensure good prediction accuracy by balancing complexity and generalization.

Résumé

In maintenance field, prognostics is recognized as a key feature as the prediction of the remaining useful life of a system allows avoiding inopportune maintenance spending. However, it can be a non trivial task to develop and implement effective prognostics models including the inherent uncertainty of prognostics. Moreover, there is no systematic way to construct a prognostics tool since the user can make some assumptions: choice of a structure, initialization of parameters... This last problem is addressed in the paper: how to build a prognostics system with no human intervention, neither a priori knowledge? The proposition is based on the use of a neuro-fuzzy predictor whose architecture is partially determined thanks to a statistical approach based on the Akaike information criterion. It consists in using a cost function in the learning phase in order to automatically generate an accurate prediction system that reaches a compromise between complexity and generalization capability. The proposition is illustrated and discussed.
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Dates et versions

hal-00459285 , version 1 (23-02-2010)

Identifiants

  • HAL Id : hal-00459285 , version 1

Citer

Mohamed El-Koujok, Rafael Gouriveau, Noureddine Zerhouni. A neuro-fuzzy self built system for prognostics : a way to ensure good prediction accuracy by balancing complexity and generalization.. IEEE Prognostics & System Health Management Conference, PHM'2010., Jan 2010, Macau, China. 8 p. ⟨hal-00459285⟩
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