Dynamic Weighting Ensembles for Incremental Learning and Diagnosing New Concept Class Faults in Nuclear Power Systems

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

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

Key requirements for the practical implementation of empirical diagnostic systems are the capabilities of incremental learning of new information that becomes available, detecting novel concept classes and diagnosing unknown faults in dynamic applications. In this paper, a dynamic weighting ensembles algorithm, called Learn++.NC, is adopted for fault diagnosis. The algorithm is specially designed for efficient incremental learning of multiple new concept classes and is based on the dynamically weighted consult and vote (DW-CAV) mechanism to combine the classifiers of the ensemble. The detection of unseen classes in subsequent data is based on thresholding the normalized weighted average of outputs (NWAO) of the base classifiers in the ensemble. The detected unknown classes are classified as unlabeled until their correct labels can be assigned. The proposed diagnostic system is applied to the identification of simulated faults in the feedwater system of a boiling water reactor (BWR).

Mots clés: Dynamic weighting ensembles; fault detection and classification; incremental learning; nuclear power systems

Domaines: Autre

Roozbeh Razavi-Far, Piero Baraldi, Enrico Zio. Dynamic Weighting Ensembles for Incremental Learning and Diagnosing New Concept Class Faults in Nuclear Power Systems. IEEE Transactions on Nuclear Science, 2012, 59 (5), pp.2520 - 2530. ⟨10.1109/TNS.2012.2209125⟩. ⟨hal-00777671⟩