摘要
本文构建的列车故障诊断及应急控制系统是基于信号采集技术,采用工业以太网拓扑结构,通过车载设备和地面设备实时数据采集、分析、处理,并且通过建立不断迭代的大数据模型对车辆走行部元件进行故障诊断,根据诊断结果通过分级故障报警系统实现对车辆运行进行分级管理。该系统的故障预判能力,有效降低了车辆运行风险,提高了城市轨道车辆的维护保养的精准化和效率,并同时实现了对车辆走行部元件全生命周期的健康监测及管理。
The train fault diagnosis and emergency control system is based on signal acquisition technology,using industrial Ethernet topology structure,real-time data acquisition,analysis,and processing by on-board and ground equipment,and fault diagnosis of vehicle running gear components through the establishment of iterative big data models.According to the diagnosis results,hierarchical fault alarm systems are used to achieve hierarchical management of rolling stock operation.The system’s fault prediction capability effectively reduces rolling stock operational risks,improves the accuracy and efficiency of rolling stock maintenance,and simultaneously achieves health monitoring and management of the entire life cycle of rolling stock running gear components.
作者
孔涛
KONG Tao(China Civil Engineering Construction Corporation,Beijing 100038,China)
出处
《铁道建筑技术》
2023年第9期78-80,共3页
Railway Construction Technology
基金
辽宁省科学技术计划项目(2021020825-JH1/104)。
关键词
故障诊断
大数据模型
故障预测
故障分类控制
fault diagnosis
big data model
fault prediction
fault classification control
