Closing the sim-to-real gap in guided wave damage detection with adversarial training of variational auto-encoders
This addresses the problem of structural health monitoring for infrastructure, where real-world training data is scarce, but it is incremental as it builds on existing deep learning and adversarial training approaches.
The paper tackled the sim-to-real gap in guided wave damage detection by training an ensemble of variational autoencoders with adversarial components on simulation data, achieving superior performance on experimental data compared to existing deep learning methods.
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.