The reliability of thimble tubes plays a critical role for maintaining the safety of a nuclear power plant. The defect depth needs to be quantified and predicted to support the operational decision-making. This paper presents a method to quantify the defects on thimble tube wall based on the analyzation of eddy current testing (ECT) data. Then, a method using artificial neural network (ANN) to predict the detect depth is studied. The tubes are divided into 2 shapes and four regions according to their positions and the data of each region and each shape is expanded by mean interpolation. A prediction model based on ANN is constructed for each shape in each region. The experimental results show that the model can predict the signal of the next year according to the signal of the previous three years with mean absolute percentage error less than 16%.

中文翻译：

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使用人工神经网络检测和预测顶针管缺陷

顶针管的可靠性对于维持核电站的安全起着至关重要的作用。需要量化和预测缺陷深度以支持运营决策。本文提出了一种基于涡流检测（ECT）数据分析来量化顶针管壁缺陷的方法。然后，研究了一种利用人工神经网络（ANN）预测检测深度的方法。根据管子的位置将管子分为2个形状和4个区域，并通过平均插值对每个区域和每个形状的数据进行扩展。针对每个区域的每个形状构建基于人工神经网络的预测模型。实验结果表明，该模型能够根据前三年的信号预测下一年的信号，平均绝对百分比误差小于16%。