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Statistical partial wavefield imaging using Lamb wave signals

Author(s):

Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , 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.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/1475921717727160.
  • About this
    data sheet
  • Reference-ID
    10562104
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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