Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor

Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor

Curve fitting; Deterioration; Fiber optic sensors; Forecasting; Learning algorithms; Machine learning; Signal to noise ratio; Temperature measurement; Temperature sensors; Theorem proving; Brillouin frequency shifts; Brillouin gain spectrum (BGS); Brillouin optical time domain analysis; Distributed...

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Main Authors: Nordin N.D., Zan M.S.D., Abdullah F.
Other Authors: 57217851042
Format: Article
Published: Academic Press Inc. 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-252762023-05-29T16:07:47Z Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor Nordin N.D. Zan M.S.D. Abdullah F. 57217851042 24767242400 56613644500 Curve fitting; Deterioration; Fiber optic sensors; Forecasting; Learning algorithms; Machine learning; Signal to noise ratio; Temperature measurement; Temperature sensors; Theorem proving; Brillouin frequency shifts; Brillouin gain spectrum (BGS); Brillouin optical time domain analysis; Distributed temperature sensing; Generalized linear model; Low signal-to-noise ratio; Temperature prediction; Temperature resolution; Time domain analysis This study describes the deployment of machine learning algorithm called generalized linear model (GLM) to improve the temperature prediction performance in Brillouin optical time domain analysis (BOTDA) fiber sensor for distributed temperature sensing application. In GLM, the temperature prediction is made from the Brillouin gain spectrum (BGS) and the link function, without the need to determine the Brillouin frequency shift (BFS). In this proof-of-concept experiment, the performance of GLM was investigated by collecting the BGS and comparing it to the conventional Lorentzian curve fitting (LCF) method. From the experimental results, we have found that the GLM method produced a more consistent temperature prediction than the conventional LCF method. Furthermore, the proposed GLM method could still retain an accurate temperature measurement regardless of low signal-to-noise ratio (SNR) and large frequency scanning step while collecting BGS, which is difficult to be achieved by the conventional LCF method at certain level. In addition to that, the prediction obtained is 655 times faster than the conventional LCF method. The small and negligible deterioration to the temperature resolution confirmed the robustness of GLM in performing fast and accurate temperature measurement for BOTDA. � 2020 Elsevier Inc. Final 2023-05-29T08:07:47Z 2023-05-29T08:07:47Z 2020 Article 10.1016/j.yofte.2020.102298 2-s2.0-85087725318 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087725318&doi=10.1016%2fj.yofte.2020.102298&partnerID=40&md5=e2aead5ba01fa9ce745bf190c3ee973a https://irepository.uniten.edu.my/handle/123456789/25276 58 102298 Academic Press Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Curve fitting; Deterioration; Fiber optic sensors; Forecasting; Learning algorithms; Machine learning; Signal to noise ratio; Temperature measurement; Temperature sensors; Theorem proving; Brillouin frequency shifts; Brillouin gain spectrum (BGS); Brillouin optical time domain analysis; Distributed temperature sensing; Generalized linear model; Low signal-to-noise ratio; Temperature prediction; Temperature resolution; Time domain analysis
author2 57217851042
author_facet 57217851042
Nordin N.D.
Zan M.S.D.
Abdullah F.
format Article
author Nordin N.D.
Zan M.S.D.
Abdullah F.
spellingShingle Nordin N.D.
Zan M.S.D.
Abdullah F.
Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor
author_sort Nordin N.D.
title Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor
title_short Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor
title_full Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor
title_fullStr Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor
title_full_unstemmed Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor
title_sort generalized linear model for enhancing the temperature measurement performance in brillouin optical time domain analysis fiber sensor
publisher Academic Press Inc.
publishDate 2023
_version_ 1806424103100350464

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