Safety regular pattern analysis system for EMU
-
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。Abstract: 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.
-
[1] 叶 丹. 关于动车组检修周期优化及运用效率提升的探讨 [J]. 铁道车辆,2018,56(8):30-32. [2] 史天运,孙 鹏. 动车组管理信息系统的建设与发展 [J]. 铁路计算机应用,2013,22(1):1-4,9. doi: 10.3969/j.issn.1005-8451.2013.01.003 [3] 李 强. 动车组车载信息无线传输系统应用 [J]. 科技创新与应用,2019(4):164-165. [4] 赵俊彦,任崇巍,王德明. 关于动车组故障轨边图像检测系统的设计 [J]. 铁道机车车辆,2011,31(6):19-22. [5] 厉 浩,张 渝,彭朝勇. 动车组滚动轴承故障轨边声学检测技术及应用 [J]. 中国铁路,2016(3):61-64. [6] 李国杰,程学旗. 大数据研究:未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考 [J]. 中国科学院院刊,2012,27(6):647-657. [7] 何 清,李 宁,罗文娟,等. 大数据下的机器学习算法综述 [J]. 模式识别与人工智能,2014(4):327-336. [8] 舒 康. 基于HDFS的分布式存储研究与实现[D]. 成都: 电子科技大学, 2014.
下载:
点击查看大图
图(2)
