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Multilayer Structure Damage Detection Using Optical Fiber Acoustic Sensing and Machine Learning.

Beatriz Brusamarello1, Uilian José Dreyer1, Gilson Antonio Brunetto2

  • 1Graduate Program in Electrical and Computer Engineering (CPGEI-UTFPR), Curitiba 80230-901, Brazil.

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|September 14, 2024
PubMed
Summary
This summary is machine-generated.

Distributed acoustic sensing (DAS) effectively monitors multilayer structures by analyzing mechanical waves. Combining DAS with support vector machine classification achieved 93% accuracy in detecting structural damage.

Keywords:
damage detectiondistributed acoustic sensingmachine learningoptical fiber sensorsstructural health monitoring

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Area of Science:

  • Materials Science
  • Engineering
  • Physics

Background:

  • Distributed acoustic sensing (DAS) offers continuous spatio-temporal monitoring for structural health.
  • Its application in complex multilayer structures requires performance evaluation.
  • Optical fiber sensors provide extensive data for assessing large infrastructure integrity.

Purpose of the Study:

  • To assess the efficacy of DAS for monitoring a laboratory-scale multilayer structure.
  • To analyze the behavior of mechanical waves under different damage scenarios.
  • To develop an automated damage classification system.

Main Methods:

  • A four-layer structure (fiberboard and polyurethane foam) was subjected to emulated damages.
  • DAS was employed to detect and analyze mechanical wave propagation.
  • Data was compared with reference point sensors and analyzed in time and frequency domains.
  • A support vector machine classifier was trained for automated damage identification.

Main Results:

  • DAS successfully detected mechanical wave propagation through the multilayer structure.
  • Distinct signal characteristics were observed in time and frequency domains for each damage type.
  • The support vector machine classifier achieved 93% accuracy in damage classification.
  • DAS performance was validated against traditional point sensors.

Conclusions:

  • DAS is a viable technology for monitoring multilayer structures, even with damage.
  • Analyzing signals in both time and frequency domains provides comprehensive damage insights.
  • Integrating DAS with machine learning, like SVM, enables smart structural health monitoring tools.