摘要
动车组故障数据呈现体量越来越大,来源日趋广泛,相关信息几何形态增长的特点。为研究动车组安全规律,挖掘海量历史故障的数据价值,基于动车组安全大数据平台,利用多源数据采集、融合处理及大数据机器学习技术,设计了动车组安全规律分析系统架构与功能。目前,该系统已研发完成,并针对CRH380系列动车组进行了安全规律验证。系统的建设有利于提高动车组安全管理水平,为动车组技术管理人员提供有效的决策支持,降低维修成本。
The volume of Electric Multiple Units(EMU) fault data is becoming larger and larger,showing the characteristics of geometric growth,and the sources are becoming more and more extensive.However,the accumulation of fault data has not significantly improved the level of fault analysis.In order to study the safety regular pattern of EMU and dig out the data value of massive historical faults,based on the EMU safety big data platform,using multi-source data collection,fusion processing,and big data machine learning technology,this paper designed the EMU safety law analysis system architecture and functions.At present,the system has been researched and developed,and the safety regular pattern of CRH380 series EMU have been verified.The construction of the system is conducive to improving the safety management level of the EMU,providing effective decision-making suggestions for the technical management personnel of the EMU,and reducing maintenance costs.
作者
杨晨
YANG Chen(Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)
出处
《铁路计算机应用》
2022年第2期50-53,共4页
Railway Computer Application
基金
北京经纬信息技术有限公司科研项目(DZYF20–28)。
关键词
动车组
安全规律
故障管理
大数据技术
机器学习
Electric Multiple Units(EMU)
safety regular pattern
fault management
big data technology
machine learning
