论文标题
通过对差异自动编码器的对抗训练，在指导波损伤检测中缩小SIM到真实的差距
Closing the sim-to-real gap in guided wave damage detection with adversarial training of variational auto-encoders
论文作者
论文摘要
引导波测试是一种监测基础设施结构完整性的流行方法。我们专注于通常采用信号处理技术的损伤检测的主要任务。检测性能受波传播模型与实验波数据之间的不匹配的影响。外部变化（例如温度）也很难建模，也会影响性能。虽然深度学习模型可能是一种替代检测方法，但通常缺乏现实世界中的培训数据集。在这项工作中，我们仅在具有波浪物理引导的对抗分量的模拟数据上训练一组变异自动编码器来应对这一挑战。我们建立了一个具有不均匀温度变化的实验，以测试方法的鲁棒性。我们将我们的方案与现有的深度学习检测方案进行比较，并在实验数据上观察到卓越的性能。
Guided wave testing is a popular approach for monitoring the structural integrity of infrastructures. We focus on the primary task of damage detection, where signal processing techniques are commonly employed. The detection performance is affected by a mismatch between the wave propagation model and experimental wave data. External variations, such as temperature, which are difficult to model, also affect the performance. While deep learning models can be an alternative detection method, there is often a lack of real-world training datasets. In this work, we counter this challenge by training an ensemble of variational autoencoders only on simulation data with a wave physics-guided adversarial component. We set up an experiment with non-uniform temperature variations to test the robustness of the methods. We compare our scheme with existing deep learning detection schemes and observe superior performance on experimental data.
