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Characterization of defects using ultrasonic arrays: a dynamic classifier approach

机译：使用超声阵列表征缺陷：动态分类器方法

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摘要

In the field of nondestructive evaluation, accurate characterization of defects is required for the assessment of defect severity. Defect characterization is studied in this paper through the use of the ultrasonic scattering matrix, which can be extracted from the array measurements. Defects that have different shapes are classified into different defect classes, and this essentially allows us to distinguish between crack-like defects and volumetric voids. Principal component analysis (PCA) is used for feature extraction, and several representational principal component subsets are found through exhaustive searching in which quadratic discriminant analysis (QDA) and support vector machine (SVM) are used as the pattern classifiers. Instead of choosing a single optimal classifier, the best classifier is dynamically selected for different measurements by estimating the local classifier accuracy. The proposed approach is validated in simulation and experiments. In simulation, the depths (lengths of the minor axes) of 4441 out of 4636 test samples are measured accurately, and the measurement errors (with respect to the defect size) are below 10%. Arbitrarily shaped rough volumetric defects are identified as ellipses, which are reasonably good matches in shape to the original defects. Experimentally, six subwavelength scatterers are characterized and sized to within 0.14λ.
机译：在无损评估领域，需要对缺陷进行准确的表征以评估缺陷的严重程度。本文通过使用超声散射矩阵研究缺陷表征，可以从阵列测量中提取出超声散射矩阵。具有不同形状的缺陷被分类为不同的缺陷类别，这实际上使我们能够区分裂纹状缺陷和体积空隙。主成分分析（PCA）用于特征提取，通过穷举搜索找到了几个代表性的主成分子集，其中将二次判别分析（QDA）和支持向量机（SVM）用作模式分类器。通过选择局部分类器的准确性，可以动态选择最佳分类器以用于不同的测量，而不是选择单个最佳分类器。仿真和实验验证了该方法的有效性。在仿真中，精确测量了4636个测试样品中4441个的深度（短轴长度），并且测量误差（相对于缺陷尺寸）小于10％。任意形状的粗大体积缺陷被标识为椭圆形，其形状与原始缺陷相当好。实验上，六个亚波长散射体的特征和尺寸在0.14λ之内。

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