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Adaptive signal decomposition and dispersion removal based on the matching pursuit algorithm using dispersion-based
1Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
Ultrasonics
|February 15, 2020
Summary
This study introduces a new method for precise damage detection in plate structures using sparse sensor networks. The technique effectively removes wave dispersion, significantly improving damage imaging accuracy and spatial resolution.
Area of Science:
- Structural Health Monitoring
- Wave Propagation Analysis
- Non-Destructive Testing
Background:
- Accurate damage localization in plate-like structures is challenging with sparse sensor networks.
- Dispersion removal and signal decomposition are critical for precise damage imaging.
- Existing methods struggle with overlapping wave packets and complex dispersion characteristics.
Purpose of the Study:
- To develop a high-resolution damage imaging method for sparsely distributed sensor networks.
- To precisely localize point-like damage by combining dispersion-removed wave packets with a damage-imaging algorithm.
- To validate the proposed approach through simulations and experimental testing.
Main Methods:
- Utilized a matching pursuit algorithm to decompose overlapping wave packets and recompress dispersion.
- Constructed a matching pursuit dictionary based on asymptotic solutions of Lamb wave dispersion relations.
- Employed a dispersion-based Hanning-window dictionary to extract wave packet parameters (time-delay, dispersion extent, phase).
- Applied extracted parameters to a dispersion-removal algorithm and a minimum-variance imaging algorithm.
Main Results:
- The proposed algorithm successfully recompressed multiple dispersive wave packets across different modes.
- Experimental validation on an aluminum plate demonstrated high-quality damage imaging with fine spatial resolution.
- Achieved a 62.1% improvement in accuracy compared to conventional minimum-variance imaging.
- Significantly suppressed image artifacts, enhancing localization precision.
Conclusions:
- The developed method enables high-resolution damage imaging in plate structures with sparse sensor networks.
- Matching pursuit effectively extracts wave packet parameters and removes dispersion for improved imaging.
- The validated approach offers a significant advancement in structural health monitoring accuracy and reliability.
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