基于声发射信号时频图深度学习的桥梁钢桁架焊接节点损伤程度识别

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (1) : 107-115.

论文

李丹1 2,沈鹏1,贺文宇1,向抒林3

作者信息 +

Identification of damage degree of welded joints in bridge steel trusses based on deep learning of time-frequency maps of AE signals

LI Dan1,2, SHEN Peng1, HE Wenyu1, XIANG Shulin3

Author information +

文章历史 +

摘要

针对桥梁钢桁架疲劳损伤识别难度大、精度低的现状，提出基于声发射信号时频分析与深度学习的钢桁架焊接节点损伤程度识别方法。对桁架节点在桥梁运营状态下产生的声发射信号进行小波变换，表征不同损伤程度信号的时频能量分布模式，然后建立卷积神经网络(convolutional neural network，CNN)模型对时频图进行损伤特征提取，并通过迁移学习思想提升模型的训练效率和学习能力，从而实现桁架焊接节点严重损伤、轻微损伤和噪声工况的准确识别。进一步对模型各卷积层激活区域进行可视化分析，解剖模型的损伤特征学习过程及分类逻辑。某悬索桥中央纵向腹板钢桁架焊接节点现场试验结果表明：相较于利用时域波形进行特征学习的一维卷积神经网络模型，时频图包含了更丰富的损伤信息，所建立的二维卷积神经网络模型对钢桁架焊接节点三种损伤程度的识别准确率超过94%，具有更强鲁棒性和实际应用价值。

Abstract

In view of the difficulty in accurate detection of fatigue damages in bridge steel trusses, this study proposes a method to identify the damage degree of welded truss joints based on time-frequency analysis and deep learning of acoustic emission signals. The acoustic emission signals generated by the truss joints during operation are firstly analyzed by wavelet transform to characterize their energy distribution patterns in the time-frequency domain for different damage degrees. After that, a convolutional neural network (CNN) model is established to extract damage features from the time-frequency diagrams. The training efficiency and learning ability of the model are improved through transfer learning. Accurate identification of severe damage, minor damage, and intact cases of the truss joints can then be realized. Further, the activation areas in each convolution layer of the model are visualized to reveal the damage feature learning process and classification logic. Filed test was carried out on the central longitudinal steel truss web of a suspension bridge. The results showed that compared with the one-dimensional CNN model using time-domain waveforms of acoustic emission signals for feature learning, the two-dimensional CNN, taking time-frequency diagrams that contained more abundant damage information as the input, achieved an accuracy of more than 94% in identifying the three damage degrees of the truss joints. It behaved with higher robustness and potential for practical applications.

导出引用

李丹1 2,沈鹏1,贺文宇1,向抒林3. 基于声发射信号时频图深度学习的桥梁钢桁架焊接节点损伤程度识别[J]. 振动与冲击, 2024, 43(1): 107-115

LI Dan1,2, SHEN Peng1, HE Wenyu1, XIANG Shulin3. Identification of damage degree of welded joints in bridge steel trusses based on deep learning of time-frequency maps of AE signals[J]. Journal of Vibration and Shock, 2024, 43(1): 107-115

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