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

Abstract

As structural damage patterns and service environments become more complex, digital twin-based structural health monitoring, 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 is proposed for feature enhancement. Additionally, the learning phase of the damage monitoring model based on the feature-mapping convolutional network 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.

Keywords

Guided wave features / Structural digital twin / Interactive transfer / Damage monitoring

References

[1] Y. Wang, Y. Liao, X. Cui, et al., A heterogeneous decision voting-based transfer domain adaptation method for damage localization of CFRP composite structures, Mech. Syst. Signal Process. 224 (2025) 112015.

[2] Y. Liu, X. Hong, B. Zhang, Contact delamination detection of anisotropic composite plates using non-elliptical probability imaging of nonlinear ultrasonic guided waves, Struct. Health Monit. 22 (2023) 276-295.

[3] Y. Liao, Y. Wang, X. Zeng, et al., Multiscale 1-DCNN for damage localization and quantification using guided waves with novel data fusion technique and new self- attention module, IEEE Trans. Ind. Inform. 20 (2024) 492-502.

[4] S. Sikdar, W. Ostachowicz, A. Kundu, Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signals, Compos. Struct. 312 (2023) 116876.

[5] W. Booyse, D.N. Wilke, S. Heyns, Deep digital twins for detection, diagnostics and prognostics, Mech. Syst. Signal Process. 140 (2020) 106612.

[6] H. Dang, M. Tatipamula, H.X. Nguyen, Cloud-based digital twinning for structural health monitoring using deep learning, IEEE Trans. Ind. Inf. 18 (2022) 3820-3830.

[7] T.G. Ritto, F.A. Rochinha, Digital twin, physics-based model, and machine learning applied to damage detection in structures, Mech. Syst. Signal Process. 155 (2021) 107614.

[8] X. Zhou, C. Sbarufatti, M. Giglio, et al., A fuzzy-set-based joint distribution adaptation method for regression and its application to online damage quantification for structural digital twin, Mech. Syst. Signal Process 191 (2023) 110164.

[9] P.W. Loveday, Simulation of piezoelectric excitation of guided waves using waveguide finite elements, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 55 (2008) 2038-2045.

[10] P. Liang, C. Deng, J. Wu, et al., Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network, Measurement 159 (2020) 107768.

[11] M. Aliakbari, M. Mahmoudi, P. Vadasz, et al., Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks, Int. J. Heat. Fluid Flow. 96 (2022) 109002.

[12] I.N. Giannakeas, Z.S. Khodaei, F.M.H. Aliabadi, Effect of detection and localization uncertainty on damage size estimation using guided wave based SHM, Procedia Struct. Integr. 52 (2024) 655-666.

[13] Z. Deng, M. Zhan, X. Yuan, et al., Bayesian model updating utilizing scaled likelihood ratio and BCT-PCA with frequency response function, Mech. Syst. Signal Process. 200 (2023) 110505.

[14] P. Conti, M. Guo, A. Manzoni, et al., Multi-fidelity surrogate modeling using long short-term memory networks, Comput. Methods Appl. Mech. Eng. 404 (2023) 115811.

[15] M. Mitra, S. Gopalakrishnan, Guided wave based structural health monitoring: A review, SMART Mater. Struct. 25 (2016) 053001.

[16] Y. Wang, X. Cui, Q. Liu, et al., An adaptive damage monitoring method based on transfer features mapped for advanced composite structures, Compos Struct. 329 (2024) 117742.