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

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Li, Qi, Liu, Fushun, Li, Peng, Wang, Bin, Qian, Zhenghua and Liu, Dianzi (2024) Acoustic data-driven framework for structural defect reconstruction: A manifold learning perspective. Engineering with Computers, 40 (4). 2401–2424. ISSN 0177-0667

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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. Towards 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.

Item Type: Article
Additional Information: Funding Information: 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).
Uncontrolled Keywords: acoustic data-driven,defect reconstruction,guided wave,manifold learning,software,modelling and simulation,engineering(all),computer science applications ,/dk/atira/pure/subjectarea/asjc/1700/1712
Faculty \ School: Faculty of Science > School of Engineering (former - to 2024)
UEA Research Groups: Faculty of Science > Research Groups > Sustainable Energy
Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling
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Depositing User: LivePure Connector
Date Deposited: 01 Dec 2023 03:27
Last Modified: 07 Nov 2024 12:47
URI: https://ueaeprints.uea.ac.uk/id/eprint/93846
DOI: 10.1007/s00366-023-01931-7

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