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

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (1) : 107-115.

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PDF(3591 KB)
Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (1) : 107-115.

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
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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.

Key words

Steel trusses / welded joints / damage degree / acoustic emission / time-frequency analysis / deep learning

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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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