|本期目录/Table of Contents|

[1]王志波,王继柱.基于光纤光栅传感技术和卷积神经网络的铁路信号调节方法研究[J].工业仪表与自动化装置,2023,(01):91-96.[doi:10.19950/j.cnki.cn61-1121/th.2023.01.018]
　WANG Zhibo,WANG Jizhu.Research on railway signal regulation based on fiber grating sensing technology and convolutional neural network[J].Industrial Instrumentation & Automation,2023,(01):91-96.[doi:10.19950/j.cnki.cn61-1121/th.2023.01.018]
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基于光纤光栅传感技术和卷积神经网络的铁路信号调节方法研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2023年01期
页码:
91-96
栏目:
出版日期:
2023-02-15

文章信息/Info

Title:
Research on railway signal regulation based on fiber grating sensing technology and convolutional neural network
文章编号:
1000-0682（2023）01-0091-06
作者:
王志波; 王继柱
中国神华神朔铁路分公司，陕西 榆林 719316
Author(s):
WANG Zhibo ; WANG Jizhu
China Shenhua Shenshuo Railway Branch,Shanxi Yulin 719316,China
关键词:
光纤光栅传感器; 卷积神经网络; 铁路信号; 小波变换原理
Keywords:
fiber grating sensor;  convolutional neural network;  railway signal;  the principle of wavelet transform
分类号:
U216.3
DOI:
10.19950/j.cnki.cn61-1121/th.2023.01.018
文献标志码:
A
摘要:
为提高铁路信号瞬时频率识别的准确率，实现对铁路信号的故障分类和调节，该文设计了一种基于光纤光栅传感技术和卷积神经网络的铁路信号调节方法。首先通过光纤光栅传感器构建铁路信号光纤光栅传感监测模块，获取铁路信号数据，并对铁路信号数据进行预处理，运用小波变换原理提取处理后铁路信号数据的瞬时频率特征；然后组建基于卷积神经网络的铁路信号异常诊断模型，采用小波脊计算轨道电路移频信号的瞬时频率，诊断铁路信号故障，根据不同的故障类型，维护人员及时采用有效调节方法进行故障信号处理。最后以某市的某一线路列车作为测试对象，采集该列车的相关数据信息，并通过MATLAB软件进行仿真测试。实验表明，该方法可以准确地对铁路信号故障进行识别，维修人员能够及时采取不同调节措施对铁路故障信号进行调节，保障铁路运行的安全，具有一定的应用价值。
Abstract:
In order to improve the accuracy of instantaneous frequency identification of railway signals and realize fault classification and regulation of railway signals, a railway signal regulation method based on fiber grating sensing technology and convolutional neural network is designed in this paper. Firstly, the fiber Bragg grating sensor monitoring module of railway signal is constructed by fiber Bragg grating sensor to obtain the railway signal data, and preprocess the railway signal data. The instantaneous frequency characteristics of the processed railway signal data are extracted by using the wavelet transform principle; Then, a railway signal abnormality diagnosis model based on convolutional neural network is established. The instantaneous frequency of the frequency shift signal of the track circuit is calculated by wavelet ridge, and the railway signal fault is diagnosed. According to different fault types, the maintenance personnel timely adopt effective adjustment methods to deal with the fault signal. Finally, a certain line train in a city is taken as the test object, and the relevant data information of the train is collected, and the simulation test is carried out through MATLAB software. The experiment shows that this method can accurately identify the railway signal fault, and the maintenance personnel can take different adjustment measures to adjust the railway fault signal in time, so as to ensure the safety of railway operation. It has certain application value.

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备注/Memo

备注/Memo:
收稿日期：2022-08-26
第一作者：
王志波（1983-），男，汉，陕西神木人，工程师，研究方向为铁路信号。
通信作者：
王继柱（1972-），男，汉，山西应县人，工程师，研究方向为铁路信号。
更新日期/Last Update: 1900-01-01