为全面分析常导高速磁浮线路轨道系统的研究进展,通过Web of Science、EI、中国知网与万方数据核心数据库检索获取1987~2023年321篇中英文相关文献,涵盖16个国家与地区和59个国内机构;首次利用文献计量方法并借助CiteSpace工具构建科学...为全面分析常导高速磁浮线路轨道系统的研究进展,通过Web of Science、EI、中国知网与万方数据核心数据库检索获取1987~2023年321篇中英文相关文献,涵盖16个国家与地区和59个国内机构;首次利用文献计量方法并借助CiteSpace工具构建科学知识图谱梳理常导高速磁浮线路轨道系统研究进展;基于文献时空分布特点,关键词共现、突现与聚类分析,对领域发展脉络、研究力量、研究主题与热点进行分析总结并可视化展示。研究结果表明:常导高速磁浮线路轨道系统研究经历了发展起步阶段、初步发展阶段,目前正处于高速发展阶段;领域研究热度与重要工程建设和政策支持呈正相关;中国是领域研究的中坚力量,所发表英文研究成果占总体的83.82%;研究核心机构以高校为主,与企业有紧密联系;研究主题主要围绕线路设计优化,轨道梁结构设计,轨道制造和安装技术研究,轨道静力学与动力学性能分析,环境荷载响应分析,系统耦合动力学响应分析,轨道结构检测、监测与维护等7个方向展开;从研究热点上看,以车轨耦合效应、轨道结构检测与监测为主,设计、制造和维护等方面的研究还略显不足。上述计量分析成果表明常导高速磁浮线路轨道系统研究还存在较大提升空间,中国新一代时速600 km高速磁浮交通发展将是提升该领域研究水平的良好契机。展开更多
The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structur...The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structures,manual visual inspection,short inspection window times,and limited GPS positioning accuracy.To address these issues,this paper proposes a deep learning-based method for detecting and locating stator surface damage.This study establishes a maglev track stator surface image dataset,trains different object detection models,and compares their performance.Ultimately,YOLO and ByteTrack object tracking algorithms were chosen as the basic framework and enhanced to achieve automatic identification of high-speed maglev track stator surface damage images and track and count stator surface localization feature images.By matching the identified damaged images with their corresponding stator segment and beam segment sequence numbers,the location of the damage is pinpointed to the corresponding stator segment,enabling rapid and accurate identification and localization of complex damage to the maglev track stator surface.展开更多
文摘为全面分析常导高速磁浮线路轨道系统的研究进展,通过Web of Science、EI、中国知网与万方数据核心数据库检索获取1987~2023年321篇中英文相关文献,涵盖16个国家与地区和59个国内机构;首次利用文献计量方法并借助CiteSpace工具构建科学知识图谱梳理常导高速磁浮线路轨道系统研究进展;基于文献时空分布特点,关键词共现、突现与聚类分析,对领域发展脉络、研究力量、研究主题与热点进行分析总结并可视化展示。研究结果表明:常导高速磁浮线路轨道系统研究经历了发展起步阶段、初步发展阶段,目前正处于高速发展阶段;领域研究热度与重要工程建设和政策支持呈正相关;中国是领域研究的中坚力量,所发表英文研究成果占总体的83.82%;研究核心机构以高校为主,与企业有紧密联系;研究主题主要围绕线路设计优化,轨道梁结构设计,轨道制造和安装技术研究,轨道静力学与动力学性能分析,环境荷载响应分析,系统耦合动力学响应分析,轨道结构检测、监测与维护等7个方向展开;从研究热点上看,以车轨耦合效应、轨道结构检测与监测为主,设计、制造和维护等方面的研究还略显不足。上述计量分析成果表明常导高速磁浮线路轨道系统研究还存在较大提升空间,中国新一代时速600 km高速磁浮交通发展将是提升该领域研究水平的良好契机。
基金supported in part by the National Natural Science Foundation of China under Grant 52432012in part by the Shanghai Science and Technology Project with 25ZR1402508。
文摘The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structures,manual visual inspection,short inspection window times,and limited GPS positioning accuracy.To address these issues,this paper proposes a deep learning-based method for detecting and locating stator surface damage.This study establishes a maglev track stator surface image dataset,trains different object detection models,and compares their performance.Ultimately,YOLO and ByteTrack object tracking algorithms were chosen as the basic framework and enhanced to achieve automatic identification of high-speed maglev track stator surface damage images and track and count stator surface localization feature images.By matching the identified damaged images with their corresponding stator segment and beam segment sequence numbers,the location of the damage is pinpointed to the corresponding stator segment,enabling rapid and accurate identification and localization of complex damage to the maglev track stator surface.
