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Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring.

Gilbert A Angulo-Saucedo1, Jersson X Leon-Medina2,3, Wilman Alonso Pineda-Muñoz4

  • 1Department of Electrical and Electronic Engineering, Universidad Nacional de Colombia-Sede Bogotá, Cra 45 No. 26-85, Bogotá 111321, Colombia.

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Summary
This summary is machine-generated.

This study developed a machine learning approach for structural health monitoring (SHM) using piezoelectric sensors. The method effectively detects and classifies damage in plates, outperforming other algorithms.

Keywords:
damage classificationdata acquisition systemmachine learningpiezoelectricprincipal component analysisself-organizing mapsstructural health monitoring

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Area of Science:

  • Engineering
  • Materials Science
  • Computer Science

Background:

  • Advancements in computing power enable complex machine learning (ML) algorithms for diverse applications.
  • Structural Health Monitoring (SHM) increasingly utilizes ML for damage detection and classification in structures like aircraft and buildings.
  • Current SHM systems require further development in robustness, reliability, and cost-effectiveness.

Purpose of the Study:

  • To configure a data acquisition system for an active piezoelectric (PZT) sensor network.
  • To develop a damage classification methodology using signal processing and ML algorithms.
  • To experimentally validate the developed SHM system on aluminum and composite plates.

Main Methods:

  • Utilized an active piezoelectric (PZT) sensor network for signal acquisition.
  • Developed a damage classification methodology incorporating signal processing techniques (normalization, PCA).
  • Applied machine learning algorithms, specifically counterpropagation artificial neural network (CPANN), supervised Kohonen (SKN), and X-Y fused Kohonen (XYF).

Main Results:

  • Experimental validation was performed on aluminum plates with added masses and a CFRP plate with delamination and cracks.
  • The SKN and XYF networks demonstrated significant utility in damage classification tasks.
  • Achieved overall accuracies of 73.75% for SKN and 72.5% for XYF via cross-validation, outperforming other tested neural networks.

Conclusions:

  • The developed damage classification methodology shows promise for robust and reliable SHM.
  • The SKN and XYF variants of self-organizing maps are effective for damage classification in structural components.
  • This research contributes to the advancement of low-cost and efficient automated structural health monitoring systems.