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Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks.

Guillermo Azuara1, Mariano Ruiz1, Eduardo Barrera1

  • 1Instrumentation and Applied Acoustics Research Group, Universidad Politécnica de Madrid, C/Nikola Tesla, s/n, 28031 Madrid, Spain.

Sensors (Basel, Switzerland)
|September 10, 2021
PubMed
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This study introduces a Convolutional Neural Network (CNN) for analyzing ultrasonic guided waves (UGW) to detect damage in carbon fiber structures. The method accurately predicts damage locations, improving structural health monitoring.

Area of Science:

  • Materials Science
  • Structural Health Monitoring
  • Non-Destructive Testing

Background:

  • Nondestructive evaluation of carbon fiber reinforced materials is crucial.
  • Ultrasonic Guided Waves (UGW), especially Lamb waves, are effective for damage detection due to sensitivity and range.
  • Challenges in UGW analysis include signal complexity, material properties, and environmental factors.

Purpose of the Study:

  • To propose a data-driven approach using Convolutional Neural Networks (CNNs) for damage localization in carbon fiber structures.
  • To predict distance-to-damage values from UGW signals.
  • To develop an effective damage location algorithm for structural health monitoring.

Main Methods:

  • Utilizing Convolutional Neural Networks (CNNs) for signal analysis.
Keywords:
convolutional neural networksdamage imagingmachine learningstructural health monitoringultrasonic guided waveswavelet transform

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  • Employing Wavelet transform to convert experimental UGW signals into time-frequency images as NN input.
  • Integrating NN-predicted distances into a novel damage localization algorithm.

    • Main Results:

    • The proposed CNN accurately predicts distance-to-damage values from UGW signals.
    • The damage location algorithm, using NN outputs, achieves a deviation of less than 15 mm from actual damage positions.
    • The method demonstrates potential for precise damage mapping on structure surfaces.

    Conclusions:

    • CNNs offer a practical and accurate solution for analyzing complex UGW data.
    • The developed approach enhances the reliability and precision of damage localization in carbon fiber reinforced materials.
    • This technique advances structural health monitoring capabilities for composite structures.