Damage features for structural health monitoring based on ultrasonic Lamb waves: Evaluation criteria, survey of recent work and outlook

Houyu Lu, Balaji Chandran, Wen Wu, Jelena Ninic, Konstantinos Gryllias, Dimitrios Chronopoulos*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Guided Lamb waves are highly valued in structural health monitoring for identifying critical damage. The essence of Lamb wave-based damage detection is finding and processing damage-sensitive features (DSFs) that can reliably reflect and interpret structural damage. The primary emphasis of this work is the first time a comprehensive evaluation and comparison of the fifteen most commonly used DSFs in the time, frequency and time–frequency domain for Lamb waves, along with their statistical and wave interactive features based on the proposed five feature evaluation criteria. Five feature evaluation criteria are proposed concerning diagnostic capability, stability against environmental variability, computational complexity, sensor framework and structural complexity. These criteria aim to facilitate the evaluation of the robustness and reliability of each feature category. These evaluations highlight the strengths and limitations of each feature and qualitatively quantify its performance at four levels. Additionally, the paper provides explicit guidance on selecting appropriate Lamb waves’ features, including when to use specific features for damage description and determination. The Lamb wave-based data pre-processing and damage characterization approaches, as well as the influence of environmental and operational factors on damage features, are also discussed in this work. Conclusively, this work outlines four prospective research avenues concerning DSFs.

Original languageEnglish
Article number114666
Number of pages30
JournalMeasurement
Volume232
Early online date16 Apr 2024
DOIs
Publication statusE-pub ahead of print - 16 Apr 2024

Keywords

  • Lamb waves
  • Damage-sensitive features
  • Features evaluation criteria
  • Data processing
  • Environmental effect

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