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
1. School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, China; 2. School of Civil Engineering, Southeast University, Nanjing 211189,China; 3. Hefei General Machinery Research Institute Co., Ltd., Hefei 230031, China
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.
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. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(1): 107-115.
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