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Review
. 2024 Mar 29;24(7):2200.
doi: 10.3390/s24072200.

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Erick Reyes-Vera  1 , Alejandro Valencia-Arias  2 , Vanessa García-Pineda  1 , Edward Florencio Aurora-Vigo  3 , Halyn Alvarez Vásquez  4 , Gustavo Sánchez  5
Affiliations
Review

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Erick Reyes-Vera et al. Sensors (Basel). .

Abstract

The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

Keywords: PRISMA; deep learning; fiber Bragg grating; fiber sensors; machine learning.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA flow chart. Own elaboration based on Scopus and Web of Science.
Figure 2
Figure 2
Quantity and percentage of publications per year. Own elaboration based on Scopus and Web of Science.
Figure 3
Figure 3
Main authors according to the number of citations vs. the number of publications. Own elaboration based on Scopus and Web of Science.
Figure 4
Figure 4
Major journals according to the number of citations vs. the number of publications. Author’s elaboration based on Scopus and Web of Science.
Figure 5
Figure 5
Main countries according to the number of citations vs. the number of publications. Author’s calculations based on Scopus and Web of Science.
Figure 6
Figure 6
Keywords by year for optical fiber sensors and machine learning techniques. Source: the authors.
Figure 7
Figure 7
Keyword co-occurrence network according to their main relationship node. Own elaboration based on Scopus and Web of Science.
Figure 8
Figure 8
Validity and frequency of keywords per year versus the frequency of appearance of each word. Own elaboration based on Scopus and Web of Science.
Figure 9
Figure 9
Research agenda for fiber optic sensing and machine learning based on the utilization of the concept during the mentioned period, highlighting the year of highest frequency. Source: the authors.
See this image and copyright information in PMC

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