Prognostic by classification of predictions combining similarity-based estimation and belief functions - AGPIG Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Prognostic by classification of predictions combining similarity-based estimation and belief functions

Résumé

Forecasting the future states of a complex system is of paramount importance in many industrial applications covered in the community of Prognostics and Health Management (PHM). Practically, states can be either continuous (the value of a signal) or discrete (functioning modes). For each case, specific techniques exist. In this paper, we propose an approach called EVIPRO-KNN based on case-based reasoning and belief functions that jointly estimates the future values of the continuous signal and of the future discrete modes. A real datasets is used in order to assess the performance in estimating future break-down of a real system where the combination of both strategies provide the best prediction accuracies, up to 90%
Fichier principal
Vignette du fichier
Evidential_pronostic_belief_functions_ramasso.pdf (156.85 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00719965 , version 1 (23-07-2012)

Identifiants

  • HAL Id : hal-00719965 , version 1

Citer

Emmanuel Ramasso, Michèle Rombaut, Noureddine Zerhouni. Prognostic by classification of predictions combining similarity-based estimation and belief functions. Belief 2012 - 2nd International Conference on Belief Functions, May 2012, Compiègne, France. pp.1-8. ⟨hal-00719965⟩
160 Consultations
100 Téléchargements

Partager

Gmail Facebook X LinkedIn More