Ultrasonic arrays are increasingly used for inspection of the structural components in non-destructive testing (NDT) applications. The ultrasonic array data can be processed to form high-resolution images for detection and localization of defects. Alternatively, the scattering matrix can be extracted from the full matrix of array data and used for defect characterization if the defect size is small (i.e., comparable to an ultrasonic wavelength). This paper studies the dimensionality reduction problem of scattering matrix databases. In particular, we focus on accurate characterization of inclined defects for which previous approaches based on principal component analysis (PCA) yielded high characterization uncertainty. We propose a supervised approach based on locality preserving projection (LPP) and introduce noise constraints to the objective function of LPP. In simulation, the proposed approach is shown to produce a well-resolved defect manifold for 45°ellipses. Characterization results obtained using the simulated noisy measurements of four 60°ellipses confirm the performance improvement of LPP over PCA. In experiments, three 60°ellipses and two surface-breaking cracks have been characterized. On average, the root-mean-square (RMS) sizing error given by the LPP approach is 39.0% lower compared to PCA for the ellipses and 11.1% lower for the surface-breaking cracks.

超声波阵列越来越多地用于检查无损检测 (NDT) 应用中的结构部件。可以处理超声波阵列数据以形成用于缺陷检测和定位的高分辨率图像。或者，可以从阵列数据的完整矩阵中提取散射矩阵，如果缺陷尺寸很小（即，可与超声波波长相媲美）。本文研究了散射矩阵数据库的降维问题。特别是，我们专注于倾斜缺陷的准确表征，之前基于主成分分析 (PCA) 的方法产生了高表征不确定性。我们提出了一种基于局部保持投影 (LPP) 的监督方法，并将噪声约束引入 LPP 的目标函数。在仿真中，所提出的方法被证明可以为 45°椭圆生成一个解析良好的缺陷流形。使用四个 60°椭圆的模拟噪声测量获得的表征结果证实了 LPP 相对于 PCA 的性能改进。在实验中，已经表征了三个 60°椭圆和两个表面断裂裂纹。一般，