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王平, 吴文波, 杨友兰, 马毅华, 许江, 宗智诚. 基于人工智能的高铁动车组智能运维数据分析系统的构建[J]. 铁路计算机应用, 2022, 31(7): 14-18. DOI: 10.3969/j.issn.1005-8451.2022.07.03
引用本文: 王平, 吴文波, 杨友兰, 马毅华, 许江, 宗智诚. 基于人工智能的高铁动车组智能运维数据分析系统的构建[J]. 铁路计算机应用, 2022, 31(7): 14-18. DOI: 10.3969/j.issn.1005-8451.2022.07.03
WANG Ping, WU Wenbo, YANG Youlan, MA Yihua, XU Jiang, ZONG Zhicheng. Intelligent operation and maintenance data analysis system of high-speed railway EMU based on artificial intelligence[J]. Railway Computer Application, 2022, 31(7): 14-18. DOI: 10.3969/j.issn.1005-8451.2022.07.03
Citation: WANG Ping, WU Wenbo, YANG Youlan, MA Yihua, XU Jiang, ZONG Zhicheng. Intelligent operation and maintenance data analysis system of high-speed railway EMU based on artificial intelligence[J]. Railway Computer Application, 2022, 31(7): 14-18. DOI: 10.3969/j.issn.1005-8451.2022.07.03

基于人工智能的高铁动车组智能运维数据分析系统的构建

Intelligent operation and maintenance data analysis system of high-speed railway EMU based on artificial intelligence

  • 摘要: 针对高速铁路(简称:高铁)动车组部件故障诊断和预测的业务需求,依托动车组故障预测与健康管理(PHM,Prognostic and Health Management)系统,在基于人工智能的高铁动车组智能运营维护(简称:运维)算法研究平台中构建高铁动车组智能运维数据分析系统。介绍了高铁动车组智能运维算法研究平台的架构,以及高铁动车组智能运维数据分析系统的数据处理流程和关键算法。并以高铁动车组客室空调为例,选取客室空调相关传感器数据进行数据分析,得到影响客室空调健康状况的特征,并对聚类结果进行健康度数据标注,作为客室空调健康评估模型开发的基础。

     

    Abstract: For the business needs of fault diagnosis and prediction of high-speed railway EMU components, this paper relied on the EMU prognostic and health management system, built an intelligent operation and maintenance data analysis system of high-speed railway EMU in the intelligent operation and maintenance algorithm research platform of high-speed railway EMU based on artificial intelligence. The paper introduced the architecture of the intelligent operation and maintenance algorithm research platform for high-speed railway EMU, the data processing flow and key algorithms of the intelligent operation and maintenance data analysis system for high-speed railway EMU, took the passenger compartment air conditioning of high-speed railway EMU as an example, selected the relevant sensor data of passenger compartment air conditioning for data analysis, obtained the characteristics that affect the health status of passenger compartment air conditioning, and marked the health data of the clustering results as the basis for the development of health assessment model of passenger compartment air conditioning.

     

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