2D Temperature Field Reconstruction Using Optical Frequency Domain Reflectometry and Machine-Learning Algorithms

Autor:	Alexey Wolf, Nikita Shabalov, Vladimir Kamynin, Alexey Kokhanovskiy
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	fiber-optic sensor machine learning optical frequency domain reflectometry Chemical technology TP1-1185
Zdroj:	Sensors, Vol 22, Iss 20, p 7810 (2022)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s22207810
Popis:	We present experimental results on the reconstruction of the 2D temperature field on the surface of a 250 × 250 mm sensor panel based on the distributed frequency shift measured by an optical backscatter reflectometer. A linear regression and a feed-forward neural network algorithm, trained by varying the temperature field and capturing thermal images of the panel, are used for the reconstruction. In this approach, we do not use any information about the exact trajectory of the fiber, material properties of the sensor panel, and a temperature sensitivity coefficient of the fiber. Mean absolute errors of 0.118 °C and 0.086 °C are achieved in the case of linear regression and feed-forward neural network, respectively.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7838414aeec04be591602aea07e5308e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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