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Full Wavefield Analysis and Damage Imaging Through Compressive Sensing in Lamb Wave Inspections
Summary
This study introduces a compressive sensing technique for faster structural health monitoring. It reduces acoustic data acquisition time by up to 80% for accurate damage detection and localization.
Area of Science:
- Engineering
- Materials Science
- Signal Processing
Background:
- Structural health monitoring (SHM) faces challenges with large acoustic wavefield datasets.
- Extracting damage information from SHM data is time-consuming.
Purpose of the Study:
- To develop a compressive sensing algorithm for efficient damage detection and localization.
- To reduce data acquisition time in SHM without compromising accuracy.
Main Methods:
- Utilizes compressive sensing to exploit wavefield sparsity in various domains (wave atoms, curvelets, Fourier exponentials).
- Reconstructs full wavefield and infers damage location by comparing reconstructions from different domains.
- Applies the technique to aluminum, polymer, and composite plates with defects.
Main Results:
- Achieves significant reduction in acquisition time (up to 80% of Nyquist grid).
- Demonstrates high performance in defect detection and localization across different structural components.
- Validates the technique's applicability to various materials and defect types.
Conclusions:
- The proposed compressive sensing method enhances SHM efficiency.
- It enables rapid and accurate structural damage assessment.
- The technique is versatile for diverse structural components and defect scenarios.
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