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ToolsLi, Qi, Lui, Hairui, Li, Peng, Sikdar, Shirsendu, Wang, Bin, Qian, Zhenghua and Liu, Dianzi (2023) Intelligent structural defect reconstruction using the fusion of multi-frequency and multi-mode acoustic data. IEEE Access, 11. pp. 23935-23945. ISSN 2169-3536
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Abstract
Quantitative detection of defects in structures is always a hot research topic in the field of guided wave inverse scattering. Research studies on how to effectively extract the defect-related information encompassed in the multi-frequency and multi-modes scattered wave signals for reconstructions of defects have been paid attention in recent decades. In this paper, a novel deep learning-based quantitative guided wave inverse scattering technique has been proposed to intelligently realize the end-to-end mapping of the multi-frequency, multi-modes scattered signals to defect profiles with high levels of accuracy and efficiency. Based on the manifold distribution principle, the data patterns of scattered SH-wave signals have been investigated, owing to leveraging the capability of the intelligent encoder-projection-decoder neural network. Following that, the manifold-learning oriented network has been trained using the data generated by the modified boundary element method. Several numerical examples have been examined to demonstrate the correctness and efficiency of the proposed reconstruction approach. It has been concluded that this novel data-driven technique intelligently enables the high-quality solution to inverse scattering problems and provides a valuable insight into the development of practical approaches to quantitative detection using multi-frequency and multi-modal acoustic data from scattered ultrasonic guided waves.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 12061131013, Grant 12211530064, and Grant 12172171; in part by the State Key Laboratory of Mechanics and Control of Mechanical Structures at the Nanjing University of Aeronautics and Astronautics (NUAA) under Grant MCMS-E-0520K02; in part by the Fundamental Research Funds for the Central Universities under Grant NE2020002 and Grant NS2019007; in part by the National Natural Science Foundation of China for Creative Research Groups under Grant 51921003; in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX21_0184; in part by the National Natural Science Foundation of Jiangsu Province under Grant BK20211176; in part by the Interdisciplinary Innovation Fund for Doctoral Students of NUAA under Grant KXKCXJJ202208; and in part by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions. |
| Uncontrolled Keywords: | acoustics,deep learning,feature extraction,guided wave,image reconstruction,inverse problems,inverse scattering problem,multi-frequency,multi-modes,scattering,transducers,multi-frequency,deep learning,inverse scattering problem,multi-modes,engineering(all),materials science(all),electrical and electronic engineering,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2200 |
| 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: | 07 Mar 2023 09:44 |
| Last Modified: | 07 Nov 2024 12:46 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/91404 |
| DOI: | 10.1109/ACCESS.2023.3253644 |
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