研究人员开发了一种新颖的多保真迁移学习框架，用于使用导波进行结构健康监测。该方法结合了轻量级的基于物理的模拟与卷积自编码器（CAE）以及少量实验数据，以准确诊断板状结构的损伤。该框架在大型合成数据集上进行有效预训练，然后利用有限的真实世界测量数据进行微调，在损伤定位方面显著优于传统的CNN模型。 AI
影响 该框架通过减少对广泛实验数据的依赖，可以实现更高效、更准确的实际应用中的结构健康监测。
排序理由 这是一篇详细介绍损伤诊断新框架的研究论文。
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arXiv:2606.27304v1 Announce Type: new Abstract: Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is o…
Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of lab…
Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of lab…