Digital twin-driven structural damage monitoring via multilevel Lamb wave enhancement and transfer learning

As structural damage patterns and service environments become more complex, digital twin-based structural health monitoring (SHM) with its unique advantages can compensate for the limitations of data-driven methods regarding data dependency and model interpretability. However, it still faces challenges in modeling complexity, simulation accuracy, and discrepancies between real and virtual features. This study proposes a balanced fidelity digital twin for structural damage monitoring based on Lamb wave multilevel feature enhancement and adaptive space interaction. Firstly, multilevel refined features are extracted from few-shot guided wave signals obtained in physical and digital space, and the adversarial synthetic balancing algorithm (ASBA) is proposed for feature enhancement. Additionally, the learning phase of the damage monitoring model based on the feature mapping convolutional network (F-MCN) is driven by virtual samples of readily accessible balanced fidelity in digital space. To reduce the feature distributional difference between the two spaces, an interactive transfer approach is introduced to establish a shared feature digital twin space. Overall, this study provides a feasible technique to enhance the accessibility and generalizability of digital twins for real engineering structures.