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

TL;DR

This paper introduces an adversarial training approach using variational autoencoders trained solely on simulated data to improve guided wave damage detection, effectively bridging the gap between simulation and real-world data under temperature variations.

Contribution

The study presents a novel adversarial training scheme with variational autoencoders trained only on simulation data, enhancing robustness to environmental variations in damage detection.

Findings

Outperforms existing deep learning methods on experimental data

Demonstrates robustness to temperature variations

Effective in bridging the sim-to-real gap

Abstract

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.