Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear Components - CentraleSupélec Access content directly
Conference Papers Year : 2014

Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear Components

Valeria Vitelli
  • Function : Author
  • PersonId : 945760
Redouane Seraoui
  • Function : Author
  • PersonId : 963305

Abstract

Combining different physical and / or statistical predictive algorithms for Nuclear Power Plant (NPP) components into an ensemble can improve the robustness and accuracy of the prediction. In this paper, an ensemble approach is proposed for prediction of time series data based on a modified Probabilistic Support Vector Regression (PSVR) algorithm. We propose a modified Radial Basis Function (RBF) as kernel function to tackle time series data and two strategies to build diverse sub-models of the ensemble. A simple but effective strategy is used to combine the results from sub-models built with PSVR, giving the ensemble prediction results. A real case study on a power production component is presented.
Fichier principal
Vignette du fichier
PHME_HAL.pdf (478.17 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01108176 , version 1 (24-01-2015)

Identifiers

  • HAL Id : hal-01108176 , version 1

Cite

Jie Liu, Valeria Vitelli, Redouane Seraoui, Enrico Zio. Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear Components. FLINS 2014, Aug 2014, João Pessoa, Brazil. ⟨hal-01108176⟩
126 View
190 Download

Share

Gmail Facebook X LinkedIn More