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Statistical partial wavefield imaging using Lamb wave signals

Auteur(s):

Médium: article de revue
Langue(s): anglais
Publié dans: Structural Health Monitoring, , n. 4, v. 17
Page(s): 919-935
DOI: 10.1177/1475921717727160
Abstrait:

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 ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1177/1475921717727160.
  • Informations
    sur cette fiche
  • Reference-ID
    10562104
  • Publié(e) le:
    11.02.2021
  • Modifié(e) le:
    19.02.2021
 
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