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Baseline-free guided wave damage detection with surrogate data and dictionary learning.

K Supreet Alguri1, Joseph Melville2, Joel B Harley1

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Summary

This study introduces a dictionary learning framework to detect structural damage using surrogate data when baseline measurements are unavailable. The method creates a synthetic baseline, enabling damage detection even with sparse guided wave data.

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Area of Science:

  • Structural Health Monitoring
  • Materials Science
  • Signal Processing

Background:

  • Guided wave structural health monitoring typically requires baseline data for damage detection.
  • Practical applications often lack reliable baseline data, and surrogate structure data is prone to inaccuracies due to variations in properties.

Purpose of the Study:

  • To develop a dictionary learning framework for damage detection using surrogate information when baseline data is unavailable.
  • To create a synthetic damage-free baseline by integrating wave propagation and geometric data.

Main Methods:

  • A dictionary learning framework was employed to combine wave propagation characteristics of the test structure with geometric information from surrogate structures.
  • A synthetic baseline was generated and compared with test data for damage detection.
  • The framework was evaluated on aluminum and steel plates of varying thicknesses.

Main Results:

  • The framework successfully isolated reflections from a mass using full wavefield data without explicit baseline data.
  • A drop in a correlation coefficient effectively detected a mass using sparse guided wave data, also without explicit baseline data.

Conclusions:

  • The proposed dictionary learning framework effectively overcomes the challenge of unavailable baseline data in guided wave structural health monitoring.
  • The method demonstrates robust damage detection capabilities using surrogate structures and both full and sparse wavefield data.