In this paper, to increase the performance of the sparse reconstruction method in real complex engineering structures an adaptive dictionary learning framework is proposed which updates the dictionary matrix, to allow improved compatibility with the complex structure. This proposed framework was developed by combining analytical modeling with training data sets and learning methods. An experimental evaluation of the proposed dictionary learning framework was performed on an anisotropic composite plate with a stiffener. In this experimental evaluation, a moving magnet was used as the artificial damage to capture the training data set, and both artificial damage in several locations and real impact damage was used for detection and location of the target damage. The obtained results confirmed the concept of the proposed dictionary learning framework for the improved health monitoring of complex structures.

在本文中，为了提高稀疏重建方法在实际复杂工程结构中的性能，提出了一种自适应字典学习框架，该框架更新字典矩阵，以提高与复杂结构的兼容性。这个提议的框架是通过将分析建模与训练数据集和学习方法相结合而开发的。在带有加强筋的各向异性复合板上对所提出的字典学习框架进行了实验评估。在本次实验评估中，使用移动磁铁作为人工损伤来捕获训练数据集，同时使用多个位置的人工损伤和真实的冲击损伤来检测和定位目标损伤。