When using a single sensor to monitor early microleakage of nuclear power pressure pipeline leakage, there are problems such as low monitoring accuracy and poor early warning reliability due to the limitations of the monitoring range and weak difference between the leakage signals. To address these challenges, this paper proposes a dual channel visible graph convolutional neural network (DCV-GCN). Firstly, the acoustic emission time-series data of each channel are truncated and divided, and the significant frequency bands are selected based on the envelope spectrum. On this basis, the sequence group is averaged to obtain the graph structure sequence. Then, the limited penetrable visibility (LPV) graph construction algorithm is used to calculate the adjacency matrix, and the important nodes is reserved according to the eigenvector centrality. Furthermore, the inverse ratio of the distance from the sensor in each single channel to the center of the crack is used as the fusion weight, and the adjacency matrices are merged after normalization to transform the construction of the graph structure dataset. Finally, the dataset is input into the graph convolutional neural network, and the effectiveness of the method is verified by carefully designing three homalographic cracks. The results show that the proposed method can effectively extract the distinguishing features with similar frequency components and similar leakage rates, and the recognition accuracy of different leakage states can reach 98.56 %. In addition, through ablation experiments and different parameter strategy settings, the operating mechanism is explained, which can provide a reference for monitoring and analysis by industrial technicians.

中文翻译：

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用于管道焊缝同源裂纹微泄漏监测的双通道可见图卷积神经网络


当使用单个传感器监测核电压力管道泄漏早期微泄漏时，由于监测范围的限制和泄漏信号之间的微弱差异，存在监测精度低、预警可靠性差等问题。为了应对这些挑战，该文提出了一种双通道可见图卷积神经网络 （DCV-GCN）。首先，对各信道的声发射时间序列数据进行截断和划分，并根据包络频谱选择有效频段;在此基础上，对序列组进行平均，得到图结构序列。然后，采用有限可穿透能见度 （LPV） 图构建算法计算邻接矩阵，并根据特征向量中心性保留重要节点;此外，将每个单通道中传感器与裂纹中心的距离反比用作融合权重，归一化后合并邻接矩阵，以转换图结构数据集的构建。最后，将数据集输入到图卷积神经网络中，通过精心设计三个同源裂纹验证了该方法的有效性。结果表明，所提方法能够有效提取频率成分相似、泄漏率相似的区分特征，不同泄漏状态的识别准确率可达98.56 %。此外，通过烧蚀实验和不同的参数策略设置，解释了操作机构，可为工业技术人员的监测和分析提供参考。