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Acoustic data-driven framework for structural defect reconstruction: a manifold learning perspective

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Abstract

Data-driven quantitative defect reconstruction using ultrasonic guided waves has recently demonstrated great potential in the area of non-destructive testing (NDT) and structural health monitoring (SHM). In this paper, a novel deep learning-based framework, called Deep-guide, has been proposed to convert the inverse guided wave scattering problem into a data-driven manifold learning progress for defect reconstruction. The architecture of Deep-guide network consists of the efficient encoder-projection-decoder blocks to automatically realize the end-to-end mapping of noisy guided wave reflection coefficients in the wavenumber domain to defect profiles in the spatial domain by the manifold distribution principle and intelligent learning. Toward this, results by the modified boundary element method for efficient calculations of scattering fields of guided waves have been generated as acoustic emission signals of the Deep-guide to facilitate the training and extract the features homeomorphically. The correctness, robustness, and efficiency of the proposed framework have been demonstrated throughout several examples and experimental tests of circular defects. It has been noted that Deep-guide has the ability to achieve the high-quality defect reconstructions and provides valuable insights into the development of effective data-driven techniques for structural health monitoring and complex defect reconstructions.

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Data availability

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (12061131013, 12211530064 and 12172171), the State Key Laboratory of Mechanics and Control of Mechanical Structures at NUAA (No. MCMS-E-0520K02), the Fundamental Research Funds for the Central Universities (Nos. NE2020002 and NS2019007), the National Natural Science Foundation of China for Creative Research Groups (No. 51921003), Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX21_0184), the National Natural Science Foundation of Jiangsu Province (No. BK20211176), the Interdisciplinary Innovation Fund for Doctoral Students of Nanjing University of Aeronautics and Astronautics [No. KXKCXJJ202208] and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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Authors and Affiliations

  1. State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Qi Li, Peng Li, Bin Wang & Zhenghua Qian

  2. College of Engineering, Ocean University of China, Qingdao, 266100, China

    Fushun Liu

  3. School of Engineering, University of East Anglia, Norwich, NR14 7TH, UK

    Dianzi Liu

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  1. Qi Li
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Correspondence to Zhenghua Qian or Dianzi Liu.

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Li, Q., Liu, F., Li, P. et al. Acoustic data-driven framework for structural defect reconstruction: a manifold learning perspective. Engineering with Computers 40, 2401–2424 (2024). https://doi.org/10.1007/s00366-023-01931-7

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