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

机译：广义线性模型，用于提高布里渊光学时域分析光纤传感器温度测量性能

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摘要

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.
机译：本研究描述了一种称为广义线性模型（GLM）的机器学习算法的部署，提高布里渊光学时域分析（BOTDA）光纤传感器的温度预测性能，用于分布式温度传感应用。在GLM中，温度预测由布里渊增益频谱（BGS）和链接功能进行，而无需确定布里渊频移（BFS）。在该概念证明实验中，通过收集BGS并将其与传统的Lorentzian曲线配件（LCF）方法进行比较来研究GLM的性能。从实验结果来看，我们发现GLM方法产生比传统的LCF方法更一致的温度预测。此外，所提出的GLM方法仍然可以保持精确的温度测量，无论在收集BGS的情况下，不管低信噪比（SNR）和大的频率扫描步骤，这难以通过在一定水平处的传统LCF方法实现。除此之外，所获得的预测比传统的LCF方法快655倍。对温度分辨率的小而忽略不计的劣化证实了GLM在对BOTDA进行快速准确的温度测量时的稳健性。

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  • 来源
    《Optical fiber technology》 |2020年第9期|102298.1-102298.7|共7页
  • 作者

    Nordin Nur Dalilla; Zan Mohd Saiful Dzulkefly; Abdullah Fairuz;

  • 作者单位

    Univ Tenaga Nas Inst Power Engn Kajang Selangor Malaysia;

    Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi Selangor Malaysia;

    Univ Tenaga Nas Inst Power Engn Kajang Selangor Malaysia|Univ Tenaga Nas Coll Engn Dept Elect & Elect Engn Kajang Selangor Malaysia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    BOTDA; GLM; LCF; Machine learning;

    机译：BOTDA;GLM;LCF;机器学习;

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