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Guided wave testing is a popular approach for monitoring the structuralintegrity of infrastructures. We focus on the primary task of damage detection,where signal processing techniques are commonly employed. The detectionperformance is affected by a mismatch between the wave propagation model andexperimental wave data. External variations, such as temperature, which aredifficult to model, also affect the performance. While deep learning models canbe an alternative detection method, there is often a lack of real-worldtraining datasets. In this work, we counter this challenge by training anensemble of variational autoencoders only on simulation data with a wavephysics-guided adversarial component. We set up an experiment with non-uniformtemperature variations to test the robustness of the methods. We compare ourscheme with existing deep learning detection schemes and observe superiorperformance on experimental data.

Closing the sim-to-real gap in guided wave damage detection with adversarial training of variational auto-encoders