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Weighted Structured Sparse Reconstruction-Based Lamb Wave Imaging Exploiting Multipath Edge Reflections in an

Caibin Xu1, Zhibo Yang2, Mingxi Deng1

  • 1College of Aerospace Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|June 25, 2020
PubMed
Summary

This study introduces a novel Lamb wave imaging method for structural health monitoring. The technique effectively locates damage using sparse reconstruction, even with minimal sensors.

Keywords:
defect detectionimaging algorithmlamb wavestructural health monitoringstructured sparse reconstruction

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Signal Processing

Background:

  • Lamb wave-based structural health monitoring (SHM) enables large-area inspection with fewer sensors.
  • Lamb wave imaging processes received signals to visualize scatterers, aiding in damage detection.

Purpose of the Study:

  • To present an advanced Lamb wave imaging method for SHM.
  • To develop a technique for accurate damage localization using sparse signal reconstruction.

Main Methods:

  • Formulating the imaging problem as a weighted structured sparse reconstruction.
  • Constructing a dictionary using an analytical Lamb wave scattering model and edge reflection prediction.
  • Decomposing experimental scattering signals under weighted structured sparsity constraints.

Main Results:

  • The method successfully generated images with sparse pixel values.
  • Effectiveness was verified through simulations and experiments on an aluminum plate.
  • Accurate localization of scatterers was achieved even with limited sensor data.

Conclusions:

  • The proposed Lamb wave imaging method is effective for SHM.
  • It offers a robust solution for damage localization with sparse sensor networks.
  • This technique enhances the capabilities of current structural health monitoring systems.