A method for measuring the robustness of diagnostic models for predicting the break size during LOCA
A method for measuring the robustness of diagnostic models for predicting the break size during LOCA
复制标题
一种测量用于预测 LOCA 期间断裂尺寸的诊断模型稳健性的方法
DOI：
10.36001/phmconf.2017.v9i1.2186
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
T. V. Santhosh
中科院分区：
文献类型：
--
作者：
Xiange Tian;V. Becerra;N. Bausch;G. Vinod;T. V. Santhosh
由于冷却系统的健康对核反应堆的稳定性至关重要，核反应堆冷却剂损失事故（LOCA）的诊断在核电站（NPP）状态监测中引起了广泛关注。多层感知器（MLP）神经网络通常应用于 LOCA 诊断。用于训练这些模型的数据由多个时间序列数据集组成，每个数据集都有不同的断裂尺寸，以及 LOCA 之后反应堆冷却剂系统中不同可测量变量的瞬态行为。为神经网络选择一个合适的架构来提供稳健的结果非常重要，因为即使对于训练数据集中未包含的中断大小，预测的中断大小也被认为是准确的。本文的目的是提出一种简单的方法来测量诊断模型的稳健性，以预测冷却剂损失事故期间的破裂尺寸。基于留一法和诊断模型产生的均方误差提出了鲁棒性度量。使用此指标可以比较不同诊断模型的稳健性。给定从核反应堆冷却剂系统的高保真度模拟中获得的数据，获得了四种不同的诊断模型，并对它们的特性进行了比较和讨论。这些模型包括具有一个隐藏层的全连接多层感知器、具有两个隐藏层的全连接多层感知器、具有使用最佳脑外科医生算法修剪的一个隐藏层的多层感知器、一组数据处理方法（GMDH）神经网络和基于自适应网络的模糊推理系统（ANFIS）。
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).