Abstract The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.

中文翻译：

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一种表征核电站蒸汽发生器退化的半监督方法

摘要 随着信息技术的快速发展，核电厂的数字化为开发新的状态维护方法打开了大门。在这项工作中，提出了一种半监督方法，用于使用在电厂运行瞬变期间收集的测量值来表征核电厂组件的退化水平。它基于从监测信号中提取的选定特征的融合。特征选择被表述为一个多目标优化问题。目标是最大化特征单调性和趋势性，以及最大化特征值与用于评估组件退化的无损测试结果之间的相关性的新度量。对帕累托最优集的特征进行归一化，将组件退化程度定义为所得值的中值。开发的方法应用于从压水反应堆的蒸汽发生器收集的真实数据。它被证明能够识别退化水平，其误差与临时无损测试获得的误差相当。