Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear ComponentsReport as inadecuate




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1 Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec LGI - Laboratoire Génie Industriel - EA 2606, SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec 2 Department of Biostatistics Department of Biostatistics, University of Oslo 3 EDF R&D - EDF Division Recherche et Développement Clamart

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.





Author: Jie Liu - Valeria Vitelli - Redouane Seraoui - Enrico Zio -

Source: https://hal.archives-ouvertes.fr/



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