摘要
为解决ZPW-2000R型轨道电路故障智能自诊断问题,提出一种基于深度卷积神经网络的ZPW-2000R轨道电路故障诊断模型,输入微机存储的38个实时监测变量数据,可自动诊断包括轨道电路室内及室外设备的共29种故障类型,且故障诊断准确率可达96%。为轨道电路故障诊断提供了有效的智能化解决方案。
In order to solve the problem of intelligent fault diagnosis of ZPW-2000R track circuit,a fault diagnosis model of ZPW-2000R track circuit based on deep convolution neural network is proposed.By inputting 38 real-time monitoring variable data stored by microcomputer,29 kinds of fault types including indoor and outdoor equipment of track circuit can be automatically diagnosed,and the accuracy rate of fault diagnosis can reach 96%.It provides an effective intelligent solution for track circuit fault diagnosis.
作者
卢皎
禹建丽
黄春雷
陈洪根
LU Jiao;YU Jianli;HUANG Chunlei;CHEN Honggen(School of Management Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046,China;Heilongjiang Ruixing Technology Co.,Ltd.,Harbin 150030,China)
出处
《工业工程》
北大核心
2021年第4期127-133,共7页
Industrial Engineering Journal
基金
国家自然科学基金资助项目(U1404702)
河南省科技攻关资助项目(182102210107)。
关键词
卷积神经网络
轨道电路
故障诊断
convolutional neural network
track circuit
fault diagnosis
