The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer’s flange region, and the stringer’s cap region. Covering the stringer’s regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.

超声导波换能器阵列可以解决现代飞机结构典型的加筋蒙皮到纵梁复合板中结构损伤的检测和定位。然而，这部分的几何和材料复杂性使得利用基于物理学的波散射概念变得非常困难。此应用程序使用基于卷积神经网络 (CNN) 的数据驱动深度学习 (DL) 方法。DL 技术根据学习到的训练数据自动选择最敏感的波浪特征。此外，网络的泛化能力允许检测可能与训练场景不同的损伤。本文介绍了为此应用程序设计的特定 1D-CNN 算法，它展示了其对加强复合材料试验板关键区域的损伤成像的能力，尤其是蒙皮区域、纵梁的翼缘区域和纵梁的盖帽区域。从仅位于皮肤上的导波换能器覆盖纵梁区域是针对这种复杂结构提出的 SHM 方法的一个特别有吸引力的特征。