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

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Nordin N.D.
Zan M.S.D.
Abdullah F.
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Academic Press Inc.
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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.
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