A method for measuring the robustness of diagnostic models for predicting the break size during LOCA

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Published Oct 2, 2017
Xiange Tian Victor Becerra Nils Bausch Gopika Vinod T.V. Santhosh

Abstract

The diagnosis of loss of coolant accidents (LOCA) in nuclear reactors has attracted a great deal of attention in condition monitoring of nuclear power plants (NPPs) because the health of cooling system is crucial to the stability of the nuclear reactor. Multi-layer perceptron (MLP) neural networks have commonly been applied to LOCA diagnosis. The data used for training these models consists of a number of time-series data sets, each for a different break size, with the transient behavior of different measurable variables in the coolant system of the reactor following a LOCA. It is important to select a suitable architecture for the neural network that delivers robust results, in that the predicted break size is deemed to be accurate even for a break size that is not included in the training data sets. The objective of this paper is to present a simple method for measuring the robustness of diagnostic models for predicting the break size during the loss of coolant accidents. A robustness metric is proposed based on the leave-one-out approach and the mean squared error resulting from a diagnostics model. Using this metric it becomes possible to compare the robustness of different diagnostic models. Given data obtained from a high fidelity simulation of the coolant system of a nuclear reactor, four different diagnostic models are obtained and their properties compared and discussed. These models include a fully connected multi-layer perceptron with one hidden layer, a fully connected multi-layer perceptron with two hidden layers, a multi-layer perceptron with one hidden layer that is pruned using the optimal brain surgeon algorithm, a group method of data handling (GMDH) neural network, and an adaptive network based fuzzy inference system (ANFIS).

How to Cite

Tian, X., Becerra, V., Bausch, N., Vinod, G., & Santhosh, T. (2017). A method for measuring the robustness of diagnostic models for predicting the break size during LOCA. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2186
Abstract 163 | PDF Downloads 95

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Keywords

Loss of coolant accident, Nuclear power plant, Multilayer Perception, Group method of data handling, Optimal brain surgeon

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Technical Research Papers