Classifier-Ensemble Incremental-Learning Procedure for Nuclear Transient Identification at Different Operational Conditions

Piero Baraldi (1) , Roozbeh Razavi-Far (1) , Enrico Zio (2)

1 Dipartimento di Energia [Milano]
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

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

Domains: Engineering Sciences [physics]; Electric power

Piero Baraldi, Roozbeh Razavi-Far, Enrico Zio. Classifier-Ensemble Incremental-Learning Procedure for Nuclear Transient Identification at Different Operational Conditions. Reliability Engineering and System Safety, 2011, 96 (4), pp.480-488. ⟨10.1016/j.ress.2010.11.005⟩. ⟨hal-00609522⟩