Signal reconstruction by a GA-optimized ensemble of PCA models - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Nuclear Engineering and Design Année : 2011

Signal reconstruction by a GA-optimized ensemble of PCA models

Résumé

On-line sensor monitoring allows detecting anomalies in sensor operation and reconstructing the correct signals of failed sensors by exploiting the information coming from other measured signals. In field applications, the number of signals to be monitored is often too large to be handled effectively by a single reconstruction model. A more viable approach is that of decomposing the problem by constructing a number of reconstruction models, each one handling an individual group of signals. To apply this approach, two problems must be solved: (1) the optimal definition of the groups of signals and (2) the appropriate combination of the outcomes of the individual models. With respect to the first problem, in this work, Multi-Objective Genetic Algorithms (MOGAs) are devised for finding the optimal groups of signals used for building reconstruction models based on Principal Component Analysis (PCA). With respect to the second problem, a weighted scheme is adopted to combine appropriately the signal predictions of the individual models. The proposed approach is applied to a real case study concerning the reconstruction of 84 signals collected from a Swedish nuclear boiling water reactor.

Domaines

Autre
Fichier principal
Vignette du fichier
ANNO_2011_6.pdf (1.15 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00609542 , version 1 (27-07-2012)

Identifiants

Citer

Piero Baraldi, Enrico Zio, Giulio Gola, Davide Roverso, M. Hoffmann. Signal reconstruction by a GA-optimized ensemble of PCA models. Nuclear Engineering and Design, 2011, 241 (1), pp.301-309. ⟨10.1016/j.nucengdes.2010.10.012⟩. ⟨hal-00609542⟩
138 Consultations
729 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More