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New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform.

Asaad Migot1,2, Ahmed Saaudi2,3, Victor Giurgiutiu2

  • 1Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary

This study introduces a new method using a two-dimensional convolutional neural network (CNN) and piezoelectric wafer active sensors (PWAS) to precisely locate impacts on structures. The model achieved high accuracy in impact localization, demonstrating its effectiveness for structural health monitoring.

Keywords:
PWASconvolutional neural network (CNN)deep learning (DL)impact localizationwavelet transform (WT)

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Area of Science:

  • Structural Health Monitoring
  • Signal Processing
  • Machine Learning Applications

Background:

  • Impact events on plate-like structures generate reflection waves detectable by sensors.
  • Accurate localization of impacts is crucial for assessing structural integrity and damage.
  • Existing methods may face challenges in differentiating similar signal features.

Purpose of the Study:

  • To develop and evaluate a novel methodology for localizing impact events on plate-like structures.
  • To utilize a two-dimensional convolutional neural network (CNN) for processing impact signals.
  • To assess the performance of the proposed CNN model in impact localization accuracy.

Main Methods:

  • A network of piezoelectric wafer active sensors (PWAS) was used to acquire impact signals.
  • Received signals underwent wavelet transform (WT)-based time-frequency analysis.
  • Processed WT diagrams were used as image datasets to train and test a 2D CNN model.
  • Two sensor placement scenarios were investigated with varying numbers of impacts.

Main Results:

  • The proposed CNN model demonstrated exceptional performance in impact localization.
  • All impact points were accurately localized in the first scenario (two sensors).
  • The model achieved 99% accuracy in localizing impacts in the second scenario (four sensors).

Conclusions:

  • The developed CNN-based methodology is highly effective for localizing impact events on plate-like structures.
  • The study highlights the potential of WT-based signal processing and CNNs for structural health monitoring.
  • Future work may focus on addressing challenges related to differentiating similar signal features arising from segmentation and impact proximity.