论文标题
基于多种学习的特征提取结构缺陷重建
Manifold learning-based feature extraction for structural defect reconstruction
论文作者
论文摘要
使用超声波引导波的数据驱动的定量缺陷重建最近在非破坏性测试领域表现出巨大的潜力。在本文中，我们开发了一个有效的基于深度学习的缺陷重建框架，称为NetInv，该框架将反向引导波散射问题重新塑造为数据驱动的监督学习进度，该进度实现了波形域中的反射系数和空间域中的缺陷谱之间的映射。拟议的Netinv比常规重建方法的优势通过了几个示例证明了缺陷重建方法。结果表明，Netinv具有具有显着效率的更高质量的缺陷型，并为使用机器学习的有效数据驱动的结构健康监测和缺陷重建提供了宝贵的见解。
Data-driven quantitative defect reconstructions using ultrasonic guided waves has recently demonstrated great potential in the area of non-destructive testing. In this paper, we develop an efficient deep learning-based defect reconstruction framework, called NetInv, which recasts the inverse guided wave scattering problem as a data-driven supervised learning progress that realizes a mapping between reflection coefficients in wavenumber domain and defect profiles in the spatial domain. The superiorities of the proposed NetInv over conventional reconstruction methods for defect reconstruction have been demonstrated by several examples. Results show that NetInv has the ability to achieve the higher quality of defect profiles with remarkable efficiency and provides valuable insight into the development of effective data driven structural health monitoring and defect reconstruction using machine learning.
