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Multi-source dynamic adaptive domain generalization network for crack detection under unknown temperature environment
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DOI：10.1016/j.measurement.2024.115588.png)
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摘要
En 中文
The dispersion characteristics of Lamb waves are more complex under variable temperature environments, leading to deviation in signal distribution, which significantly reduces the performance of damage detection models. Domain adaptation methods are often applied to damage detection problems with differences in data distribution, but this method requires obtaining the distribution information of the target temperature domain in advance and cannot achieve online damage monitoring under unknown temperature environments. Therefore, this article proposes a multi-source dynamic adaptive generalization network (MDAGN) for crack detection under unknown temperature environments. This network can extend the damage detection model to damage detection tasks under unknown temperatures. The main idea is to optimize the discriminative boundaries of the feature space while mining the generalized damage features and specificity in the temperature domain. Design specific regressors for each source temperature domain to maintain the specificity of the temperature domain, and then fuse the damage detection results of multiple regressors through similarity measurement, enabling the model to extrapolate to temperatures outside the distribution and achieve domain generalization. This article collected structural damage data under unknown temperature environments to verify the effectiveness of this method. The results indicate that this method can effectively monitor cracks under unknown temperature environments, establishing an effective framework for structural damage monitoring under unknown working conditions.
Keyword:
Lamb wave
Domain generalization
Deep learning
Unknown Temperature
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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