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动车组运用检修数字化转型的现状与探讨

Current Situation and Discussion on Digital Transformation of EMU Operation and Maintenance
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摘要 数字化转型是动车组运用检修发展到现阶段的必然趋势。为了诠释动车组运用检修中数据驱动的深刻内涵,文章立足于动车组实际检修现状,通过数字化工装设备、动车组运用管理信息系统(EMIS)、动车组故障预测与健康管理系统(PHM)在检修现场的应用,总结了动车组运用检修方式、故障管控模式、现场生产组织等方面在数字化转型过程中取得的显著成果,深入剖析了铁路数字化转型背景下限制动车组运用检修数字化进程进一步推进的客观因素,包括检修人员数字化技能掌握不全面、检测设备本身环境适应性差、数字化理念在检修现场传播范围窄。结合检修现场数字化技能的推广与动车组故障诊断技术研发等方面,对动车组数字化运用检修的发展方向做出了深入探讨。 The digital transformation is an inevitable development trend of EMU operation and maintenance at the present stage.To interpret the deep connotation of data driving in EMU operation and maintenance,this article,based on the current situation of actual maintenance of EMU,introduces the application status of the digital tooling and equipment,the EMU management information system(EMIS),the EMU prognostic and health management(PHM)system on the site of maintenance,summarizes the remarkable achievements of EMU operation and maintenance mode,failure control pattern,on-site production organization and other aspects in course of digital transformation,analyzes in depth the objective factors that limit the further promotion of digital transformation of EMU operation and maintenance in the context of digital transformation of railways,including incomplete digital skills mastered by the maintenance staff,bad adaptation of the inspection equipment to environment,narrow propagation scope of the digital concept on the site of maintenance.It also combines the promotion of the digital skills on the site of maintenance,the R&D of EMU failure diagnosis technology and other aspects to give a deep discussion on the development direction of the digital operation and maintenance of EMU.
作者 董仕 王宇翔 张勇 DONG Shi;WANG Yuxiang;ZHANG Yong(Beijing EMU Depot,China Railway Beijing Group Co.,Ltd.,Beijing 102600,China;Ankang Locomotive Department,China Railway Xi’an Group Co.,Ltd.,Xi’an 725005,China)
出处 《智慧轨道交通》 2024年第6期65-68,共4页 INTELLIGENT RAIL TRANSIT
关键词 动车组 数字化转型 运用检修 数据驱动 PHM EMU digital transformation operation and maintenance data driving PHM
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