基于卷积神经网络的ZPW-2000R轨道电路智能故障诊断方法

doi:10.3969/j.issn.1007-7375.2021.04.015

工业工程 ›› 2021, Vol. 24 ›› Issue (4): 127-133.doi: 10.3969/j.issn.1007-7375.2021.04.015

基于卷积神经网络的ZPW-2000R轨道电路智能故障诊断方法

卢皎¹, 禹建丽¹, 黄春雷², 陈洪根¹

1. 郑州航空工业管理学院管理工程学院，河南郑州 450046;
2. 黑龙江瑞兴科技股份有限公司，黑龙江哈尔滨 150030

收稿日期:2020-03-23 发布日期:2021-09-02
作者简介:卢皎(1996-)，女，河南省人，硕士研究生，主要研究方向为质量管理与质量工程、人工智能
基金资助:
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)

A Fault Diagnosis Method of ZPW-2000R Track Circuit Based on Convolutional Neural Network

LU Jiao¹, YU Jianli¹, HUANG Chunlei², CHEN Honggen¹

1. School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China;
2. Heilongjiang Ruixing Technology Co., Ltd., Harbin 150030, China

Received:2020-03-23 Published:2021-09-02

摘要/Abstract

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。

关键词: 卷积神经网络, 轨道电路, 故障诊断

Abstract: 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.

Key words: convolutional neural network, track circuit, fault diagnosis

中图分类号:

U284.22

卢皎, 禹建丽, 黄春雷, 陈洪根. 基于卷积神经网络的ZPW-2000R轨道电路智能故障诊断方法[J]. 工业工程, 2021, 24(4): 127-133.

LU Jiao, YU Jianli, HUANG Chunlei, CHEN Honggen. A Fault Diagnosis Method of ZPW-2000R Track Circuit Based on Convolutional Neural Network[J]. Industrial Engineering Journal, 2021, 24(4): 127-133.

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基于卷积神经网络的ZPW-2000R轨道电路智能故障诊断方法

A Fault Diagnosis Method of ZPW-2000R Track Circuit Based on Convolutional Neural Network

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