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Genetic algorithm-based wrapper approach for grouping condition monitoring signals of nuclear power plant components

机译：基于遗传算法的包装方法对核电厂组件状态监测信号进行分组

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摘要

Equipment condition monitoring of nuclear power plants requires to optimally group the usually very large number of signals and to develop for each identified group a separate condition monitoring model. In this paper we propose an approach to optimally group the signals. We use a Genetic Algorithm (GA) for the optimization of the groups; the decision variables of the optimization problem relate to the composition of the groups (i.e., which signals they contain) and the objective function (fitness) driving the search for the optimal grouping is constructed in terms of quantitative indicators of the performances of the condition monitoring models themselves: in this sense, the GA search engine is a wrapper around the condition monitoring models. A real case study is considered, concerning the condition monitoring of the Reactor Coolant Pump (RCP) of a Pressurized Water Reactor (PWR). The optimization results are evaluated with respect to the accuracy and robustness of the monitored signals estimates. The condition monitoring models built on the groups found by the proposed approach outperform the model which uses all available signals, whereas they perform similarly to the models built on groups based on signal correlation. However, these latter do not guarantee the robustness of the reconstruction in case of abnormal conditions and require to a priori fix characteristics of the groups, such as the desired minimum correlation value in a group.
机译：核电厂的设备状态监视需要对通常非常大量的信号进行最佳分组，并为每个已识别的组开发单独的状态监视模型。在本文中，我们提出了一种对信号进行最佳分组的方法。我们使用遗传算法（GA）来优化组。优化问题的决策变量与组的组成（即，它们包含哪些信号）有关，并根据状态监测性能的量化指标构建驱动最优组搜索的目标函数（适应性）自行建模：从这个意义上讲，GA搜索引擎是状态监控模型的包装器。考虑了一个实际案例研究，涉及压水堆（PWR）的反应堆冷却剂泵（RCP）的状态监控。关于监视信号估计的准确性和鲁棒性，评估优化结果。基于所提出的方法发现的基于组的状态监视模型优于使用所有可用信号的模型，而它们的性能类似于基于信号相关性基于组的模型。但是，后者在异常情况下不能保证重建的鲁棒性，并且需要事先确定组的特性，例如组中所需的最小相关值。

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