Document Type
Article
Publication Date
4-1-2011
Publication Title
Reliability Engineering and System Safety
Volume
96
Issue
4
First Page
480
Keywords
Bagging, BWR nuclear power plant, Classification, Ensemble, Fuzzy C Means (FCM) clustering, Incremental learning, Transient identification
Last Page
488
Abstract
An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels. © 2010 Elsevier Ltd. All rights reserved.
DOI
10.1016/j.ress.2010.11.005
ISSN
09518320
Recommended Citation
Baraldi, Piero; Razavi-Far, Roozbeh; and Zio, Enrico. (2011). Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions. Reliability Engineering and System Safety, 96 (4), 480-488.
https://scholar.uwindsor.ca/electricalengpub/176