Railway safety monitoring algorithm based on distributed optical fiber vibration sensor

Fuyang CHEN; Bin JIANG; Yu SHA

doi:10.11959/j.issn.2096-3750.2020.00173

您当前的位置：

首页 >

文章列表页 >

Railway safety monitoring algorithm based on distributed optical fiber vibration sensor

Topic:IoT in Intelligent Transportation | 更新时间：2024-06-05

- Railway safety monitoring algorithm based on distributed optical fiber vibration sensor
- Chinese Journal on Internet of Things Vol. 4, Issue 3, Pages: 106-111(2020)
- 作者机构：
  
  1. 南京航空航天大学自动化学院，江苏南京 211106
  2. 物联网与控制技术江苏省高校重点实验室，江苏南京 211106
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2020.00173
  CLC： TP273
- Published：30 September 2020，
  
  Published Online：2020-09，
- 稿件说明：
移动端阅览
FUYANG CHEN, BIN JIANG, YU SHA. Railway safety monitoring algorithm based on distributed optical fiber vibration sensor. [J]. Chinese journal on internet of things, 2020, 4(3): 106-111.
DOI：

FUYANG CHEN, BIN JIANG, YU SHA. Railway safety monitoring algorithm based on distributed optical fiber vibration sensor. [J]. Chinese journal on internet of things, 2020, 4(3): 106-111. DOI： 10.11959/j.issn.2096-3750.2020.00173.

摘要

针对铁路沿线存在人员攀爬栅栏网这一问题，将分布式光纤传感技术和信号分析技术相结合，提出了一种基于分布式光纤振动传感器的铁路安全监测算法。通过沿铁路周界栅栏网敷设的光缆感知并传导信号，搭建铁路与监测算法的物联网连接，实现对攀爬行为的智能化监测。针对铁路周围环境较复杂、干扰较多的情况，采用海明窗分帧加小波阈值去噪的方法对每帧信号进行滤波，提高了振动信号的信噪比。在特征选择上，从时域和频域上分别提取信号的功率和短时过电平率作为联合特征，对是否存在攀爬行为进行判别。因为攀爬行为具有空间上的连续性，在监测过程中设置最小报警范围滤除范围过小的报警，提高了监测系统的准确性。

Abstract

Aiming at the monitoring problem of the human climbing behaviour existing along the railway

a railway safety detection algorithm based on distributed optical fiber vibration sensors was proposed by combining the distributed optical fiber sensing technology and signal analysis technology.The surrounding vibration was sensed and transmitted through the optical cables laid along the fence network of the railway

and then the Internet of things (IoT) connection between the railway and monitoring algorithm was built to realize the intelligent monitoring of the climbing behavior.In view of the complicated surrounding environment of the railway and more interference

the Hamming window and wavelet threshold denoising method were used to filter the signal of each frame to improve the signal-to-noise ratio of the vibration signal.In the selection of features

the power spectrum and short-time over-level rate of the signal were extracted from the time domain and frequency domain respectively as a joint feature to determine whether there was climbing or creeping behavior.Since that the climbing behavior was spatially continuous

the minimum alarm range was set to filter out alarms with a too small range

which improved the accuracy of the monitoring system.

关键词

分布式光纤振动传感器铁路沿线信号处理入侵物联网

Keywords

distributed optical fiber vibration sensoralong the railwaysignal processingintrusionInternet of things

references

ZHANG S H, LIU B, HE J P . Pipeline deformation monitoring using distributed fiber optical sensor[J]. Measurement, 2019: 208-213.

余辰, 张丽娟, 金海 . 大数据驱动的智能交通系统研究进展与趋势[J]. 物联网学报, 2018,2(1): 56-63.

YU C, ZHANG L J, JIN H . Research progress and trend of big data-driven intelligent transportation system[J]. Chinese Journal on Internet of Things, 2018,2(1): 56-63.

MENG L J, WANG L B, HOU Y ,et al. A research on low modulus distributed fiber optical sensor for pavement material strain monitoring[J]. Sensors, 2017,17(10):2386.

CHAI J, WEI S M, CHANG X T ,et al. Monitoring deformation and damage on rock structures with distributed fiber optical sensing[J]. International Journal of Rock Mechanics and Mining Sciences, 2004,41(3):413.

YAN X Y, CHEN F Y, WANG L ,et al. High speed train trajectory algorithm through fiber optic sensor[C]// 2019 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2019: 2342-2346.

朱燕, 代志勇, 张晓霞 ,等. 分布式光纤振动传感技术及发展动态[J]. 激光与红外, 2011,41(10): 1072-1075.

ZHU Y, DAI Z Y, ZHANG X X ,et al. Developments of distributed optical fiber vibration sensor[J]. Laser ＆ Infrared, 2011,41(10): 1072-1075.

徐铖晋 . 分布式光纤传感系统的信号处理技术研究[D]. 杭州:浙江大学, 2017.

XU C J . Research on signal processing technology of distributed optical fiber sensing system[D]. Hangzhou:Zhejiang University, 2017.

许海燕 . 分布式光纤振动传感器及其定位技术研究[D]. 上海:复旦大学, 2011.

XU H Y . Research on distributed optical fiber vibration sensor and positioning technology[D]. Shanghai:Fudan University, 2011.

周正仙, 段绍辉, 田杰 ,等. 分布式光纤振动传感器及振动信号模式识别技术研究[J]. 光学仪器, 2013,35(6): 11-15.

ZHOU Z X, DUAN S H, TIAN J ,et al. Research on optical fiber distributed vibration sensor and pattern recognition technique of vibration signal[J]. Optical Instruments, 2013,35(6): 11-15.

SAXENA M K, RAJU S D V S J, ARYA R ,et al. Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of raman backscattered signals[J]. Optics ＆ Laser Technology, 2015,65: 14-24.

ZHANG D, XU H Z, SHI B ,et al. Brillouin power spectrum analysis for partially uniformly strained optical fiber[J]. Optics ＆ Lasers in Engineering, 2009,47(9): 976-981.

MENG J L, SHANG H K, BIAN L . The application on intrusion detection based on K-means cluster algorithm[C]// International Forum on Information Technology ＆ Applications. IEEE Computer Society, 2009.

李洪成, 吴晓平, 姜洪海 . 基于改进聚类分析的网络流量异常检测方法[J]. 网络与信息安全学报, 2015,1(1): 66-71.

LI H C, WU X P, JIANG H H . Network traffic anomaly detection method based on improved clustering analysis[J]. Journal of Network and Information Security, 2015,1(1): 66-71.

郑南宁 . 人工智能新时代[J]. 智能科学与技术学报, 2019,1(1): 1-3.

ZHENG N N . A new era of artificial intelligence[J]. Journal of Intelligent Science and Technology, 2019,1(1): 1-3.

MA Y, GAO Y, FU C ,et al. TV white space spectrum analysis based on machine learning[J]. Journal of Communications and Information Networks, 2019,4(2): 68-80.

Views

686

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception

A survey of federated learning for 6G networks

New flexible sensor for the internet of things

Optical wireless communication and internet of things

Study of international standard ISO/IEC 30144: 2020 applied in intelligent substation auxiliary monitoring

Related Author

HUANG Yuankang

ZHAN Wen

SUN Xinghua

Guanglei GENG

Bo GAO

Ke XIONG

Pingyi FAN

Yang LU

Related Institution

Sun Yat-sen University

Sun Yat-sen University Shenzhen Campus

School of Computer and Information Technology, Beijing Jiaotong University

Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, Beijing Jiaotong University

National Research Center for Information Science and Technology, Tsinghua University

⁰