Unsupervised cross-domain damage detection and localization for vibration isolators in metro floating-slab track

The steel-spring vibration isolators (SVIs) are critical components of the floating-slab track (FST) in metro system, aiming at ensuring a good performance regarding vibration attenuation. Supervised learning-based models have been recently developed for the SVI damage detection. As they are mostly trained on simulation datasets, the damage detection performance of these models is greatly spoiled on real-life datasets due to the so-called domain shift issue. In this study, a multi-strategy-based domain adaptation methodology is proposed for cross-domain SVI damage detection and localization, where domain adversarial training and feature distribution discrepancy regularization are adopted. The core of the proposed procedure is to extract damage sensitive and domain-invariant features from the dynamic responses of the track system. The main advantage is that only the source domain dataset needs to be well-labeled while label information of recorded acceleration response related to the real structure is not required for model training. Vehicle-slab track coupled dynamic simulations are conducted to build the labeled source dataset, allowing for different operational scenarios and SVI health conditions. Then, modeling uncertainties are introduced concerning model parameters, and background noise is added to the computed responses to mimic real engineering scenarios in the target domain datasets. A noteworthy good detection performance is reported, when the trained network is finally tested on the target dataset. Ablation studies and feature visualization are then reported, to get insights into the reasons why the proposed method proves superior to a traditional CNN and other domain adaption methods. Finally, an experimental dataset collected during field tests is exploited to validate the effectiveness of the proposed domain adaptation method.