Statistical partial wavefield imaging using Lamb wave signals
Author(s): |
Joel B. Harley
Chen Ciang Chia |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Structural Health Monitoring, July 2017, n. 4, v. 17 |
Page(s): | 919-935 |
DOI: | 10.1177/1475921717727160 |
Abstract: |
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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data sheet - Reference-ID
10562104 - Published on:
11/02/2021 - Last updated on:
19/02/2021