Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors.
BOFDA
BOTDA
Brillouin scattering
artificial neural networks
distributed fiber optic sensors
machine learning
strain and temperature measurements
structural health monitoring
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
06 Jul 2023
06 Jul 2023
Historique:
received:
01
06
2023
revised:
29
06
2023
accepted:
04
07
2023
medline:
17
7
2023
pubmed:
14
7
2023
entrez:
14
7
2023
Statut:
epublish
Résumé
This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system's cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.
Identifiants
pubmed: 37448034
pii: s23136187
doi: 10.3390/s23136187
pmc: PMC10347264
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : PhD-program of Bundesanstalt für Materialforschung und-prüfung (BAM)
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