Intelligent structural defect reconstruction using the fusion of multi-frequency and multi-mode acoustic data

Qi Li, Hairui Lui, Peng Li, Shirsendu Sikdar, Bin Wang, Zhenghua Qian, Dianzi Liu

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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.
Original languageEnglish
Pages (from-to)23935-23945
Number of pages11
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 6 Mar 2023

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

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