Error Prediction for a Nuclear Power Plant Fault-Diagnostic Advisor Using Neural Networks
- 1 November 1994
- journal article
- research article
- Published by Taylor & Francis in Nuclear Technology
- Vol. 108 (2) , 283-297
- https://doi.org/10.13182/nt94-a35035
Abstract
The objective of this research is to develop a fault-diagnostic advisor for nuclear power plant transients that is based on artificial neural networks. A method is described that provides an error bound and therefore a figure of merit for the diagnosis provided by this advisor. The data used in the development of the advisor contain ten simulated anomalies for the San Onofre Nuclear Power Generating Station. The stacked generalization approach is used with two different partitioning schemes. The results of these partitioning schemes are compared. It is shown that the advisor is capable of recognizing all ten anomalies while providing estimated error bounds on each of its diagnoses.Keywords
This publication has 12 references indexed in Scilit:
- Neural network recognition of nuclear power plant transientsPublished by Office of Scientific and Technical Information (OSTI) ,1993
- Abnormal Event Identification in Nuclear Power Plants Using a Neural Network and Knowledge ProcessingNuclear Technology, 1993
- Nuclear Power Plant Status Diagnostics Using an Artificial Neural NetworkNuclear Technology, 1992
- Stacked generalizationNeural Networks, 1992
- Analysis of chaotic population dynamics using artificial neural networksChaos, Solitons, and Fractals, 1991
- Approximation theory and feedforward networksNeural Networks, 1991
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990
- A benchmark for how well neural nets generalizeBiological Cybernetics, 1989
- Parallel Distributed ProcessingPublished by MIT Press ,1986
- The Predictive Sample Reuse Method with ApplicationsJournal of the American Statistical Association, 1975