Statistical partial wavefield imaging using Lamb wave signals
Autor(en): |
Joel B. Harley
Chen Ciang Chia |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Structural Health Monitoring, Juli 2017, n. 4, v. 17 |
Seite(n): | 919-935 |
DOI: | 10.1177/1475921717727160 |
Abstrakt: |
This article presents a baseline-free, model-driven, statistical damage detection and imaging framework for guided waves measured from partial (i.e. non-dense) wavefield scans. Wavefield analysis is an effective non-contact technique for non-destructive evaluation. Yet, there are several limitations to practically implement wavefield methods. These limitations include slow data acquisition and a lack of statistical reliability. Our approach addresses both of these challenges. We use sparse wavenumber analysis, sparse wavenumber synthesis, and data-fitting optimization to accurately model damage-free wavefield data. We then combine this model with matched field processing to image damage from a small number of partial wavefield measurements. We further derive a hypothesis test based on extreme value theory to statistically detect damage. We test our framework with Lamb wave measurements from a steel plate. With 70 experimental wavefield measurements, we achieve an empirical probability of damage detection of more than 98%, an empirical probability of false alarm of less than 0.17%, and an accurate image of the damage. |
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Datenseite - Reference-ID
10562104 - Veröffentlicht am:
11.02.2021 - Geändert am:
19.02.2021