基于机器学习的布里渊光时域分析传感系统温度提取研究进展  

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

作　　者：李治龙 张卫华[2] 王一民 张煜峰 罗斌[3] 朱宏娜[1] Li Zhilong;Zhang Weihua;Wang Yimin;Zhang Yufeng;Luo Bin;Zhu Hongna(School of Physical Science and Technology,Southwest Jiaotong University,Chengdu,Sichuan 610031,China;College of Meteorology and Ocean,National University of Defense Technology,Changsha,Hunan 410073,China;School of Information Science and Technology,Southwest Jiaotong University,Chengdu,Sichuan 610031,China)

机构地区：[1]西南交通大学物理科学与技术学院,四川成都610031 [2]国防科技大学气象海洋学院,湖南长沙410073 [3]西南交通大学信息科学与技术学院,四川成都610031

出　　处：《激光与光电子学进展》2021年第13期314-322,共9页Laser & Optoelectronics Progress

基　　金：四川省科技计划项目(2020YJ0016)。

摘　　要：布里渊光时域分析(BOTDA)在分布式光纤传感系统中展现出独特的优势并得到了广泛的关注,对BOTDA传感系统的温度分布信息进行快速且精确的提取至关重要。随着机器学习算法的快速发展,其在BOTDA传感系统的温度分布信息的提取中展现出巨大潜力。首先,阐述了BOTDA传感系统温度测量的原理。接着,介绍了几种基于机器学习的算法并分析了其在BOTDA传感系统温度提取中的应用和优势。最后,对未来的研究进行了展望。Brillouin optical time domain analysis(BOTDA)has shown its unique advantages in distributed optical fiber sensing systems and has received widespread attention. The rapid and accurate extraction of temperature distribution information in BOTDA sensing system is extremely desirable. With the rapid development of machine learning,it shows great potential in temperature extraction of BOTDA sensing system. First,the principle of BOTDA sensing system is introduced. Then,some machine learning algorithms are illustrated and their applications and advantages for temperature extraction of BOTDA sensing system are analyzed. Finally,outlook for future research is given.

关 键 词：光纤测量 机器学习 分布式光纤传感 布里渊光时域分析 布里渊增益谱 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]