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Physics-Augmented Spatial-Temporal graph convolutional network for damage localization using Ultrasonic guided waves
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DOI：10.1016/j.ymssp.2024.111738.png)
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Abstract
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Structural health monitoring technology based on ultrasonic guided waves (UGW) is a potential application for damage detection in plate-like composite structures. This paper introduces a novel physics-augmented spatial-temporal graph convolutional network (P-STGCN) for the damage detection task in composite plate. Firstly, the graph topology containing edge weight attributes is constructed based on the spatial correlation of the sensors and the UGW scattering principle, which strengthens the expression of the data spatial information of the data. Then, a spatial feature extractor and a temporal feature extractor are constructed based on graph convolutional network and convolutional neural network, respectively. Further, the spatial-temporal fusion method is proposed for the first time in the field of UGW damage detection, which enables the perception of multi-level damage information. In addition, a sample compensation attention module based on wavefield scattering energy is designed to compensate for the samples' spatial distribution. Finally, a dataset was constructed in a composite plate containing only four sensors to evaluate the model's performance. The results show that the average localization error is 10.36 mm. Compared with other deep learning localization methods, the P-STGCN model is superior in both localization accuracy and computational resources.
Keywords:
Composite structures
Ultrasonic guided wave
Damage localization
Graph convolutional networks
Convolutional neural networks
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8.9
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1.3W
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6.6W
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