• 查询稿件
  • 获取最新论文
  • 知晓行业信息
蔡宇晶, 高凡, 孟宇坤, 宣秀彬, 钟建峰. 城市轨道交通设备智能运维系统设计及关键技术研究[J]. 铁路计算机应用, 2023, 32(7): 79-83. DOI: 10.3969/j.issn.1005-8451.2023.07.15
引用本文: 蔡宇晶, 高凡, 孟宇坤, 宣秀彬, 钟建峰. 城市轨道交通设备智能运维系统设计及关键技术研究[J]. 铁路计算机应用, 2023, 32(7): 79-83. DOI: 10.3969/j.issn.1005-8451.2023.07.15
CAI Yujing, GAO Fan, MENG Yukun, XUAN Xiubin, ZHONG Jianfeng. Design of intelligent operation and maintenance system for urban rail transit equipment and study on its key technologies[J]. Railway Computer Application, 2023, 32(7): 79-83. DOI: 10.3969/j.issn.1005-8451.2023.07.15
Citation: CAI Yujing, GAO Fan, MENG Yukun, XUAN Xiubin, ZHONG Jianfeng. Design of intelligent operation and maintenance system for urban rail transit equipment and study on its key technologies[J]. Railway Computer Application, 2023, 32(7): 79-83. DOI: 10.3969/j.issn.1005-8451.2023.07.15

城市轨道交通设备智能运维系统设计及关键技术研究

Design of intelligent operation and maintenance system for urban rail transit equipment and study on its key technologies

  • 摘要: 针对现有城市轨道交通(简称:城轨)设备状态信息采集不完整、设备运维管理缺少决策支持数据、各专业运维业务缺乏统筹协调、运维效率低等现实问题,着眼于城轨设备综合运维管理,基于故障预测与健康管理 (PHM,Prognostics Health Management)理念,提出城轨设备智能运维系统的总体架构和功能框架,探讨亟需深入研究的关键技术。该系统通过采集和利用大量设备监测数据,利用故障诊断模型、人工智能算法和工作流引擎,在实现关键设备健康管理的基础上,自动生成设备运维计划,辅助设备运维管理决策,支持多业务作业协同,有助于提高城轨设备整体运维效能,提高城轨设备健康水平和性能,最小化停运时间,降低维修保障费用,保障城轨安全、可靠、高效运营。

     

    Abstract: To deal with the problems of incomplete data acquisition of eqipment operating status, lack of decision support data in equipment operation and maintenance management, lack of overall planning and coordination of specialty-otriented equipment operation and maintenance services, and low eqipment operation and maintenance efficiency of urban rail transit, this paper puts forward the general architecture and function framework of the intelligent equipment operation and maintenance system for urban rail aimed at comprehensive operation and maintenance management of urban rail equipment and based on the concept of Prognostics Health Management (PHM) and explores the key technologies that need in-depth research. By collecting and utilizing a large amount of equipment monitoring data, using fault diagnosis model, artificial intelligence algorithm and workflow engine, the system automatically generates equipment operation and maintenance plans, assists equipment operation and maintenance management decisions, and supports multi-service operation collaboration on the basis of realizing health management of key equipment, which helps to improve the overall operation and maintenance efficiency of urban rail equipment and improve the health level and performance of urban rail equipment, thus minimizing shut-off period, reducing maintenance costs, and ensuring safe, reliable and efficient operation of urban rail transit.

     

/

返回文章
返回