这篇论文用轻量仿真数据预训练CAE再迁移到小样本实验数据，做到了损伤定位R²>0.93、尺寸R²>0.99，比CNN方案准很多，很值结构健康监测方向的人看看。
本研究提出结合轻量物理仿真、卷积自编码器(CAE)深度特征学习、前馈神经网络和少量实验数据的多保真迁移学习框架。在板状结构损伤定位与尺寸评估中，CAE迁移学习相比CNN方案定位精度显著更优。模型在损伤定位任务上R²超过0.93，损伤尺寸评估R²超过0.99。未见损伤场景下仍保持高预测准确度，实现计算效率与实用性的平衡。
A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets
Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of labelled experimental data and the high computational cost of generating large-scale high-fidelity simulation datasets. This study presents a multifidelity transfer learning framework that integrates lightweight physics-based simulations, convolutional autoencoder (CAE)-based deep feature learning, a feed-forward neural network, and limited experimental measurements for accurate damage localisation and sizing in plate-like structures instrumented with piezoelectric transducers. A computationally efficient one-dimensional time-domain spectral element model is employed to generate a large synthetic dataset for pretraining, while transfer learning adapts the model to experimental domains using only a small amount of labelled data. The CAE-based transfer learning framework significantly outperforms its CNN-based counterpart in damage localisation accuracy. The model achieves excellent predictive performance with $R^2$ scores exceeding 0.93 for damage localisation and 0.99 for damage sizing. Its generalisation capability is demonstrated on previously unseen data, showing high prediction accuracy for damage scenarios not represented during pretraining or fine-tuning. The results establish the proposed framework as an accurate, computationally efficient, and practically viable solution for real-world GWSHM applications.