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Laser & Optoelectronics Progress, Volume. 58, Issue 13, 1306022(2021)

Advances of Machine Learning in Brillouin Optical Time Domain Analysis Sensing Systems for Temperature Extraction

Zhilong Li1, Weihua Zhang2、**, Yimin Wang1, Yufeng Zhang1, Bin Luo3, and Hongna Zhu1、*
Author Affiliations
  • 1School of Physical Science and Technology, Southwest Jiaotong University, Chengdu , Sichuan 610031, China
  • 2College of Meteorology and Ocean, National University of Defense Technology, Changsha , Hunan 410073, China
  • 3School of Information Science and Technology, Southwest Jiaotong University, Chengdu , Sichuan 610031, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
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    References (42)
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    Figures & Tables(12)
    Diagram of optical fiber temperature sensing system based on BOTDA［9］

    Fig. 1. Diagram of optical fiber temperature sensing system based on BOTDA［9］

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    Principle of using SVM to extract temperature information of BGS［29］

    Fig. 2. Principle of using SVM to extract temperature information of BGS［29］

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    Schematic diagram of training and testing steps required to extract temperature information with optimized SVM algorithm［31］

    Fig. 3. Schematic diagram of training and testing steps required to extract temperature information with optimized SVM algorithm［31］

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    Typical ANN structure diagram［28］

    Fig. 4. Typical ANN structure diagram［28］

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    Schematic diagram of training and testing steps required to extract temperature information with ANN［28］

    Fig. 5. Schematic diagram of training and testing steps required to extract temperature information with ANN［28］

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    Structure of BP neural network［35］

    Fig. 6. Structure of BP neural network［35］

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    Schematic diagram of training and testing steps required to extract temperature information with BP neural network［35］

    Fig. 7. Schematic diagram of training and testing steps required to extract temperature information with BP neural network［35］

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    Structure of DNN with 2 autoencoder hidden layers［37］

    Fig. 8. Structure of DNN with 2 autoencoder hidden layers［37］

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    Principle of using DNN for simultaneous temperature and strain measurement［38］

    Fig. 9. Principle of using DNN for simultaneous temperature and strain measurement［38］

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    Structure of DnCNN［39］

    Fig. 10. Structure of DnCNN［39］

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    Structure of ELM with three layers［41］

    Fig. 11. Structure of ELM with three layers［41］

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    Structure of ESN［41］

    Fig. 12. Structure of ESN［41］

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    Zhilong Li, Weihua Zhang, Yimin Wang, Yufeng Zhang, Bin Luo, Hongna Zhu. Advances of Machine Learning in Brillouin Optical Time Domain Analysis Sensing Systems for Temperature Extraction[J]. Laser & Optoelectronics Progress, 2021, 58(13): 1306022

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Jan. 22, 2021

    Accepted: Mar. 22, 2021

    Published Online: Jul. 5, 2021

    The Author Email: Weihua Zhang (whzhang@nudt.edu.cn), Hongna Zhu (hnzhu@swjtu.edu.cn)

    DOI:10.3788/LOP202158.1306022

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology