Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas.
Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas.
复制标题
使用散射矩阵进行超声缺陷表征：贝叶斯反演和机器学习模式的性能比较研究。
DOI：
10.1109/tuffc.2021.3084798
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Bai L
中科院分区：
文献类型：
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作者：
Bai L
在超声无损评价中，准确的缺陷表征是理想的，因为它可以提供关于缺陷类型和几何形状的定量信息。对于使用超声阵列的缺陷表征，如果缺陷相对较大，则高分辨率图像可以提供尺寸和类型信息。然而，对于与波长相当的小缺陷，基于图像的表征的性能变差。另一种方法是从阵列数据中提取远场散射系数矩阵，并将其用于表征。可以基于散射矩阵数据库来执行缺陷表征，该散射矩阵数据库由具有变化参数的理想化缺陷的散射矩阵组成。在这篇文章中，表征小的表面断裂缺口的问题进行了研究，使用两种不同的方法。第一种方法是基于一个通用的相干噪声模型的介绍，它进行表征贝叶斯框架内。第二种方法依赖于基于散射矩阵数据库的监督机器学习（ML）模式，该模式用作训练集以拟合用于表征任务的ML模型。结果表明，卷积神经网络（CNN）可以实现最好的表征精度之间所考虑的ML方法，他们给出了类似的表征不确定性的贝叶斯方法，如果一个缺口是有利的方向。这两种方法的性能不同的不利方向的缺口，ML的方法往往会得到更高的方差和更低的偏差的结果。
Accurate defect characterization is desirable in the ultrasonic nondestructive evaluation as it can provide quantitative information about the defect type and geometry. For defect characterization using ultrasonic arrays, high-resolution images can provide the size and type information if a defect is relatively large. However, the performance of image-based characterization becomes poor for small defects that are comparable to the wavelength. An alternative approach is to extract the far-field scattering coefficient matrix from the array data and use it for characterization. Defect characterization can be performed based on a scattering matrix database that consists of the scattering matrices of idealized defects with varying parameters. In this article, the problem of characterizing small surface-breaking notches is studied using two different approaches. The first approach is based on the introduction of a general coherent noise model, and it performs characterization within the Bayesian framework. The second approach relies on a supervised machine learning (ML) schema based on a scattering matrix database, which is used as the training set to fit the ML model exploited for the characterization task. It is shown that convolutional neural networks (CNNs) can achieve the best characterization accuracy among the considered ML approaches, and they give similar characterization uncertainty to that of the Bayesian approach if a notch is favorably oriented. The performance of both approaches varied for unfavorably oriented notches, and the ML approach tends to give results with higher variance and lower biases.