 Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditionsPiero Baraldi, Roozbeh Razavi-Far and Enrico Zio Reliability Engineering and System Safety, 2011, vol. 96, issue 4, 480-488 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. Keywords: Classification; Fuzzy C Means (FCM) clustering; Bagging; Ensemble; Incremental learning; BWR nuclear power plant; Transient identification (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:96:y:2011:i:4:p:480-488 DOI: 10.1016/j.ress.2010.11.005 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
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