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Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors.

Christos Karapanagiotis1, Katerina Krebber1

  • 1Bundesanstalt für Materialforschung und-Prüfung, Unter den Eichen 87, 12205 Berlin, Germany.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning enhances Brillouin distributed fiber optic sensors (DFOSs) for accurate temperature and strain monitoring. These advancements improve industrial applications like structural health monitoring and pipeline integrity.

Keywords:
BOFDABOTDABrillouin scatteringartificial neural networksdistributed fiber optic sensorsmachine learningstrain and temperature measurementsstructural health monitoring

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Area of Science:

  • Optoelectronics and Sensor Technology
  • Machine Learning Applications
  • Fiber Optic Sensing

Background:

  • Brillouin distributed fiber optic sensors (DFOSs) offer continuous, long-range temperature and strain monitoring.
  • Industrial applications include structural health monitoring of infrastructure and pipelines.
  • Traditional signal processing limits measurement speed and resolution.

Purpose of the Study:

  • To review machine learning (ML) methodologies applied to Brillouin DFOSs.
  • To highlight ML's impact on measurement accuracy, speed, and resolution.
  • To discuss future research directions in this interdisciplinary field.

Main Methods:

  • Literature review of ML algorithms used in Brillouin DFOS signal processing.
  • Analysis of ML's role in enhancing Brillouin optical time domain analysis (BOTDA) and Brillouin optical frequency domain analysis (BOFDA).
  • Categorization of ML techniques based on their application in DFOS data analysis.

Main Results:

  • ML integration leads to faster and more accurate temperature, strain, and humidity measurements.
  • ML improves spatial resolution in BOTDA systems.
  • ML significantly reduces measurement times in BOFDA systems without added cost.
  • ML enables enhanced data analysis for complex sensing scenarios.

Conclusions:

  • Machine learning is a transformative technology for Brillouin distributed fiber optic sensing.
  • ML offers cost-effective improvements in performance for critical industrial monitoring.
  • Future research should focus on advanced ML models for even greater sensing capabilities.