Title
Ensemble of neural networks for detection and classification of faults in nuclear power systems
Document Type
Conference Proceeding
Publication Date
1-1-2012
Publication Title
World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf.
Volume
7
First Page
1202
Last Page
1207
Abstract
In this work, an ensemble of neural networks is built by an algorithm called Learn++.NC and applied for fault detection and diagnosis. The algorithm is capable of incrementally learning new classes of faults, while retaining the previously acquired knowledge. The detection of new classes in subsequent data is achieved by thresholding the normalized weighted average of outputs (NWAO) of the neural networks in the ensemble. The unknown classes detected remain unlabeled until their correct labels can be assigned. The proposed method is applied to the identification of simulated faults in the feedwater system of a Boiling Water Reactor (BWR).
DOI
10.1142/9789814417747_0193
ISBN
9789814417730
Recommended Citation
Razavi-Far, Roozbeh; Baraldi, Piero; and Zio, Enrico. (2012). Ensemble of neural networks for detection and classification of faults in nuclear power systems. World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf., 7, 1202-1207.
https://scholar.uwindsor.ca/electricalengpub/173