.png){kind=icon}
返回
Acoustic data-driven framework for structural defect reconstruction: a manifold learning perspective
.png){kind=icon}
PRE
AI
DOI：10.1007/s00366-023-01931-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data-driven quantitative defect reconstruction using ultrasonic guided waves has recently demonstrated great potential in the area of non-destructive testing (NDT) and structural health monitoring (SHM). In this paper, a novel deep learning-based framework, called Deep-guide, has been proposed to convert the inverse guided wave scattering problem into a data-driven manifold learning progress for defect reconstruction. The architecture of Deep-guide network consists of the efficient encoder-projection-decoder blocks to automatically realize the end-to-end mapping of noisy guided wave reflection coefficients in the wavenumber domain to defect profiles in the spatial domain by the manifold distribution principle and intelligent learning. Toward this, results by the modified boundary element method for efficient calculations of scattering fields of guided waves have been generated as acoustic emission signals of the Deep-guide to facilitate the training and extract the features homeomorphically. The correctness, robustness, and efficiency of the proposed framework have been demonstrated throughout several examples and experimental tests of circular defects. It has been noted that Deep-guide has the ability to achieve the high-quality defect reconstructions and provides valuable insights into the development of effective data-driven techniques for structural health monitoring and complex defect reconstructions.
Keyword:
Manifold learning
Acoustic data-driven
Guided wave
Defect reconstruction
期刊
IF:
4.9
论文数:
2.7K
被引数:
9.3K
机构
引用论文
分享收藏Recent Advances in LPSO-Containing Wrought Magnesium Alloys: Relationships Between Processing, Microstructure, and Mechanical Properties.png)
JOM
JOMIF0
PREAI
分享收藏DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problemMEDICAL IMAGE ANALYSIS
OAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAI分享收藏Modelling of nonlinear crack-wave interactions for damage detection based on ultrasound-A review
PREAI
分享收藏分享收藏Solving inverse problems in stochastic models using deep neural networks and adversarial training使用深度神经网络和对抗训练解决随机模型中的逆问题OAAI
OAAI分享收藏
查看更多内容