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Machine Learning Applications in Optical Fiber Sensing: A Research Agenda.

Erick Reyes-Vera1, Alejandro Valencia-Arias2, Vanessa García-Pineda1

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Summary

This bibliometric analysis reveals machine learning applications in fiber optic sensors are advancing, particularly deep learning for structural health monitoring. Future research should explore novel materials like graphite.

Keywords:
PRISMAdeep learningfiber Bragg gratingfiber sensorsmachine learning

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Area of Science:

  • Sensor Technology
  • Artificial Intelligence
  • Materials Science

Background:

  • Technological devices are crucial for monitoring health, infrastructure, and natural factors.
  • Sensors detect anomalies for early risk detection, enhanced by artificial intelligence.
  • Fiber optic sensors are key components in various monitoring systems.

Purpose of the Study:

  • To conduct a bibliometric analysis of machine learning applications in fiber optic sensors.
  • To identify research trends and key concepts using PRISMA 2020 methodology.
  • To evaluate publication quantity and quality from Scopus and Web of Science.

Main Methods:

  • Bibliometric analysis utilizing the PRISMA 2020 guidelines.
  • Data collection from Scopus and Web of Science databases.
  • Evaluation of research trends, key concepts, and temporal advancements.

Main Results:

  • Deep learning techniques and fiber Bragg gratings are prominent in infrastructure research.
  • Fiber optic sensors are extensively used for structural health monitoring.
  • A research gap exists in utilizing novel materials like graphite for fiber optic sensors.

Conclusions:

  • Machine learning significantly enhances fiber optic sensor capabilities.
  • Structural health monitoring is a key application area for these sensors.
  • Future research should investigate novel materials to expand sensor applications.