基于支持向量机的相位敏感光时域反射仪研究

doi:10.3969/j.issn.1000-1158.2022.05.08

摘要
图/表
参考文献(15)
相关文章 (15)

全文: PDF (2686 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为了解决实际环境中振动事件易误报的问题,在基于相位敏感光时域反射仪的分布式光纤振动传感系统中,引入了一种融合小波能量谱和支持向量机(SVM)的模式识别方法。首先,利用小波能量谱分析方法,设定最优分解层为5层,并从原始信号中提取出特征向量;然后利用支持向量机的“一对一法”多分类策略对振动事件进行识别分类。考虑到实际环境因素的影响,对沿光纤行走、敲击光纤以及沿光纤慢跑3种模式的振动进行了检测试验;最后,采用准确率、精确率、召回率和F值来综合评价支持向量机分类器的性能。实验结果表明:该模式识别方法实现了84.9%的振动事件分类准确率。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	马诗洋
	王宇
	王鹏飞
	李君婵
	白清
	刘昕
	靳宝全

关键词 ：计量学, 相位敏感光时域反射仪, 支持向量机, 光纤传感, 模式识别, 小波能量谱

Abstract：In order to solve the problems of false alarm rates of vibration signals in the actual laying environment of optical fibers, a pattern recognition method based on wavelet energy spectrum and support vector machine (SVM) is introduced in the optical fiber vibration sensing system based on phase sensitive optical time domain reflectometer. Firstly, the optimal decomposition layer of wavelet energy spectrum analysis is set as 5 layers to extract feature vectors from the original signals. Then the classification of vibrations events are carried out under the “one to one method” multi-classification strategy of support vector machine. Considering the influence of experimental environment, three vibration patterns are carried out including walking through the fiber, striking on the fiber and jogging along the fiber. Finally, the accuracy rate, precision rate, recall rate and F value are used to evaluate the performance of support vector machine. According to the experimental results, this method has achieved 84.9% classification accuracy for vibration events.

Key words： metrology phase sensitive optical time domain reflectometer SVM optical fiber sensing pattern recognition wavelet energy spectrum

收稿日期: 2021-01-28 发布日期: 2022-05-18

PACS:

TB936

基金资助:国家自然科学基金(62175175);山西省基础研究计划(20210302122210);山西省科技成果转化引导专项(201904D131022);山西省煤层气联合研究基金(2016012011)

通讯作者: 王宇(1986-),男,山西忻州人,太原理工大学副教授,主要从事光纤传感技术研究。Email:wangyu@tyut.edu.cn E-mail: wangyu@tyut.edu.cn

作者简介: 马诗洋(1996-),男,山西运城人,太原理工大学硕士研究生,主要研究方向为光纤传感技术。Email: 13835977333@163.com

引用本文:

马诗洋,王宇,王鹏飞,李君婵,白清,刘昕,靳宝全. 基于支持向量机的相位敏感光时域反射仪研究[J]. 计量学报, 2022, 43(5): 609-616.
MA Shi-yang,WANG Yu,WANG Peng-fei,LI Jun-chan,BAI Qing,LIU Xin,JIN Bao-quan. Research of Phase Sensitive Optical Time Domain Reflectometer Based on Support Vector Machine. Acta Metrologica Sinica, 2022, 43(5): 609-616.

链接本文:

http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2022.05.08 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2022/V43/I5/609

［1］孙晓东, 任晓霞, 王琳. 应用于定位的光纤振动传感技术的发展现状［J］. 工业仪表与自动化装置, 2016(2): 21-25.
Sun X D, Ren X X, Wang L. Present development situation of optical fiber vibration sensing technology ［J］. Industrial Instrumentation & Automation, 2016(2): 21-25.
［2］Liu X, Jin B Q, Bai Q, et al. Distributed Fiber-Optic Sensors for Vibration Detection ［J］. Sensors, 2016, 16(8): 1164.
［3］Bao X Y, Chen L. Recent Progress in Distributed Fiber Optic Sensors ［J］. Sensors, 2012, 12(7): 8601-8639.
［4］Li Q, Zhang C X, Li L J, et al. Localization mechanisms and location methods of the disturbance sensor based on phase-sensitive OTDR ［J］. Optik, 2014, 125(9): 2099-2103.
［5］王忠东, 王宝辉, 闫铁, 等. 一种分布光纤式石油管道防盗监测系统［J］. 东北石油大学学报, 2008, 32(4): 70-72.
Wang Z D, Wang B H, Yan T, et al. A distributed optical fiber monitoring system for anti-stealing of oil pipelines ［J］. Journal of Northeast Petroleum University, 2008, 32(4): 70-72.
［6］张朝晖, 耿艳峰, 廖明燕. 油气管道的破坏监测［J］. 仪器仪表学报, 2001, 22(3): 374-375.
Zhang C H, Geng Y F, Liao M Y. The Supervision of Breakage for the Oil and Gas Pipelines ［J］. Chinese Journal of Scientific Instrument, 2001, 22(3): 374-375.
［7］Juarez J C, Taylor H F. Field test of a distributed fiber-optic intrusion sensor system for long perimeters ［J］. Applied Optics, 2007, 46(11): 1968-1971.
［8］谢鑫, 吴慧娟, 饶云江. 一种基于光纤布喇格光栅振动传感器的光纤围栏入侵监测系统及其模式识别［J］. 光子学报, 2014, 43(5): 32-37.
Xie X, Wu H J, Rao Y J. A Fiber-optical Perimeter Intrusion Detection System Based on the Fiber Bragg Grating Vibration Sensors and its Identification Method ［J］. Acta Photonica Sinica, 2014, 43(5): 32-37.
［9］王思远, 娄淑琴, 梁生, 等. M-Z干涉仪型光纤分布式扰动传感系统模式识别方法［J］. 红外与激光工程, 2014, 43(8): 2613-2618.
Wang S Y, Lou S Q, Liang S, et al. Pattern recognition method of fiber distributed disturbance sensing system based on M-Z interferometer ［J］. Infrared and Laser Engineering, 2014, 43(8): 2613-2618.
［10］孙茜, 封皓, 曾周末. 基于图像处理的光纤预警系统模式识别［J］. 光学精密工程, 2015, 23(2): 334-341.
Sun Q, Feng H, Zeng Z M. Recognition of optical fiber pre-warning system based on image processing ［J］. Optics and Precision Engineering, 2015, 23(2): 334-341.
［11］蒋立辉, 盖井艳, 王维波, 等. 基于总体平均经验模态分解的光纤周界预警系统模式识别方法［J］. 光学学报, 2015, 35(10): 60-66.
Jiang L H, Gai J Y, Wang W B, et al. Ensemble Empirical Mode Decomposition Based Event Classification Method for the Fiber-Optic Intrusion Monitoring System ［J］. Acta Optica Sinica, 2015, 35(10): 60-66.
［12］Tejedor J, Martins H F, Piote D, et al. Toward Prevention of Pipeline Integrity Threats Using a Smart Fiber-Optic Surveillance System ［J］. Journal of Lightwave Technology, 2016, 34(19): 4445-4453.
［13］沈隆翔, 封皓, 沙洲, 等. 基于下变频和IQ解调的外差型相位敏感光时域反射技术的模式识别［J］. 光学学报, 2017, 37(8): 88-97.
Sheng X L, Feng H, Sha Z, et al. Pattern Recognition of Heterodyne Phase-Sensitive Optical Time-Domain Reflection Technique Based on Down Conversion and IQ Demodulation ［J］. Acta Optica Sinica, 2017, 37(8): 88-97.
［14］王艳歌, 程丹, 刘继红. 改进的HHT变换在光纤振动模式识别中的应用［J］. 现代电子技术, 2019, 42(9): 22-25.
Wang Y G, Cheng D, Liu J H. Application of modified Hilbert-Huang transform in fiber-optic vibration pattern recognition ［J］. Modern Electronics Technique, 2019, 42(9): 22-25.
［15］陈沛超, 游赐天, 丁攀峰. 光纤周界防区入侵事件的模式识别研究［J］. 中国激光, 2019, 46(10): 275-284.
Cheng P C, You C T, Ding P F. Pattern Recognition of Intrusion Events in Perimeter Defense Areas of Optical Fiber ［J］. Chinese Journal of Lasers, 2019, 46(10): 275-284.
［16］张艾伦. 基于概率神经网络分类器的φ-OTDR分布式光纤扰动传感系统模式识别研究［D］. 北京: 北京交通大学, 2019.
［17］张沫, 郑慧峰, 倪豪, 等. 基于遗传算法优化支持向量机的超声图像缺陷分类［J］. 计量学报, 2019, 40(5): 887-892.
Zhang M, Zheng H F, Ni H, et al. Ultrasonic Image Defect Classification Based on Support Vector Machine Optimized by Genetic Algorithm ［J］. Acta Metrologica Sinica, 2019, 40(5): 887-892.
［18］张金凤, 李雪, 杨蕊, 等. 基于商空间和支持向量机的滚动轴承故障智能诊断［J］. 计量学报, 2020, 41(7): 835-841.
Zhang J F, Li X, Yang R, et al. Intelligent Diagnosis for Rolling Bearing Fault Based on Quotient Space and Support Vector Machine ［J］. Acta Metrologica Sinica, 2020, 41(7): 835-841.
［19］Daubechies I. The wavelet transform, time-frequency localization and signal analysis ［J］. Journal of Renewable & Sustainable Energy, 1990, 36(5): 961-1005.
［20］Cattermole K W. The Fourier Transform and its Applications ［J］. Electronics & Power, 1965, 11(10):267-272.
［21］Hlawatsch F, Boudreaux-Bartels G F. Linear and quadratic time-frequency signal representations ［J］. IEEE Signal Processing Magazine, 1992, 9(2): 21-67.
［22］马波. 支持向量机多类分类算法的分析与设计［D］. 扬州: 扬州大学, 2008.
［23］洪翠, 付宇泽, 郭谋发, 等. 改进多分类支持向量机的配电网故障识别方法［J］. 电子测量与仪器学报, 2019, 33(1): 7-15.
Hong C, Fu Y Z, Guo M F, et al. Identification method of distribution network faults based on improved multi-classification support vector machine ［J］. Journal of Electronic Measurement and Instrumentation, 2019, 33(1): 7-15.
［24］Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection ［J］. International Joint Conference on Artificial Intelligence, 1995, 14: 1137-1143.