Deep learning algorithm can effectively obtain damage information using labeled samples, and has become a promising feature extraction tool for ultrasonic guided wave detection. But it is difficult to apply the monitoring expertise of structure A to structure B in most cases due to the differences in the dispersion and receiving modes of different waveguides. For multi-structure monitoring at the system level, how to transfer a trained structural health monitoring model to another different structure remains a major challenge. In this article, a cross-structure ultrasonic guided wave structural health monitoring method based on distribution adaptation deep transfer learning is proposed to solve the feature generalization problem in different monitoring structures. First, the joint distribution adaptation method is employed to adapt both the marginal distribution and conditional distribution of the guided wave signals from different structures. Second, convolutional long short-term memory network is constructed to learn the mapping relationship from adapted training samples in source domain. Batch normalization layer is implemented to balance the input tensors of each sample to the same distribution. Finally, the multi-sensor damage indexes are utilized to visually present the damage by probability imaging. The experimental results show that proposed method can utilize the single-sensor monitoring data in one structure to implement the multi-sensor damage monitoring in another structure and achieve the damage imaging visualization. The imaging performance is significantly superior to the existing principal component analysis, transfer component analysis, and other state-of-art comparison methods.

深度学习算法可以利用标记样本有效获取损伤信息，成为超声导波检测中很有前景的特征提取工具。但是由于不同波导的色散和接收模式的差异，在大多数情况下很难将结构A的监测专业知识应用于结构B。对于系统级的多结构监测，如何将经过训练的结构健康监测模型转移到另一个不同的结构仍然是一个重大挑战。本文提出了一种基于分布自适应深度迁移学习的跨结构超声导波结构健康监测方法，以解决不同监测结构中的特征泛化问题。第一的，采用联合分布自适应方法对来自不同结构的导波信号的边际分布和条件分布进行自适应。其次，构建卷积长短期记忆网络以从源域中的自适应训练样本中学习映射关系。实现批量归一化层以将每个样本的输入张量平衡到相同的分布。最后，利用多传感器损伤指标，通过概率成像直观呈现损伤。实验结果表明，该方法可以利用一种结构中的单传感器监测数据，实现另一种结构中的多传感器损伤监测，实现损伤成像的可视化。