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Machine Learning Applications in Optical Fiber Sensing: A Research Agenda
Erick Reyes-Vera1, Alejandro Valencia-Arias2, Vanessa García-Pineda1
1Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia.
Sensors (Basel, Switzerland)
|April 13, 2024
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.
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.
