Fast temperature extraction approach for BOTDA using Generalized Linear Model

Nordin, Nur Dalilla; Abdullah, Fairuz; Zan, Mohd Saiful Dzulkefly; Ismail, Amiruddin; Jamaludin, M. Z.; Bakar, Ahmad Ashrif A

doi:10.1109/icp46580.2020.9206466

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Machine Learning For Temperature and Strain Predictions Dire...1

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2023

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“…Nordin et al [151][152][153] proposed the use of GLM to extract temperature. GLM is a generalized form of linear regression that does not assume that the response variables (targets) are normally distributed.…”

Section: Machine Learning For Temperature and Strain Predictions Dire...mentioning

confidence: 99%

Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

Karapanagiotis

Krebber

2023

Sensors

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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.

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Section: Machine Learning For Temperature and Strain Predictions Dire...mentioning

confidence: 99%