基于动静态分析的地铁隧道设备安全预警系统集成
范乐乐1於泽1何丽坤2袁世杰2篮杰1余伟之1刘健1
Integration of Metro Tunnel Equipment Safety Early Warning System Based on Static-Dynamic Analysis
FAN Lele1YU Ze1HE Likun2YUAN Shijie2LAN Jie1YU Weizhi1LIU Jian1
-
作者信息:1.中铁第四勘察设计院集团有限公司， 430063， 武汉
2.中铁十六局集团北京轨道交通工程建设有限公司， 101100， 北京
-
Affiliation:1.China Railway Siyuan Survey and Design Group Co., Ltd., 430063, Wuhan, China
2.China Railway 16th Bureau Group Beijing Rail Transit Engineering Construction Co., Ltd., 101100, Beijing, China
-
关键词:
-
Key words:
-
DOI:10.16037/j.1007-869x.20230545
-
中图分类号/CLCN:U231.96
-
栏目/Col:机电设备
摘要:
［目的］针对地铁隧道内列车高速运行引发的周期性风压及设备自振导致的紧固件松动，传统人工检修存在响应滞后、预判缺失等问题，亟待开发智能预警系统，实现设备状态的实时监测与风险预警，提升运维管理效能。［方法］基于隧道设备松动机理，构建基于动静态分析的地铁隧道设备安全预警系统。该系统通过分布式传感器采集设备振动数据，提取振动特征，并采用集中管理模式对设备振动状态进行实时监测与智能预警。以某地铁车站隧道为例，对该系统进行了实验室模拟和隧道实测对比分析。［结果及结论］基于动静态分析的地铁隧道设备安全预警系统预警准确率符合设计要求。该系统完成了设备维护模式从被动处置向主动预防的转变，构建了覆盖数据采集、智能分析与决策支持的完整技术体系，实现了设备振动异常的及时识别与风险预判，形成统一高效的运维管理机制，有效降低了设备故障风险。
Abstracts:
［Objective］ In response to issues such as periodic wind pressure caused by high-speed train operations in metro tunnels and fastener loosening due to equipment self-excitation, traditional manual maintenance suffers from delayed responses and insufficient predictive capabilities. It is urgently necessary to develop an intelligent early-warning system to achieve equipment status real-time monitoring and risk alerting, thereby improving the efficiency of operation and maintenance management. ［Method］ Based on the tunnel equipment loosening mechanism, a metro tunnel equipment safety early-warning system is constructed using static-dynamic analysis. The system collects vibration data through distributed sensors, extracts vibration features, and adopts a centralized management model for real-time monitoring and intelligent early-warning of equipment vibration status. Taking a specific metro station tunnel as an example, a comparative analysis for laboratory simulation and in-situ tunnel test is conducted. ［Result & Conclusion］ The safety early-warning system for metro tunnel equipment based on static-dynamic analysis could meet the design requirements in terms of early warning accuracy. The system enables a transition from passive response to proactive prevention in equipment maintenance, and establishes a comprehensive technical framework encompassing data acquisition, intelligent analysis, and decision support. It achieves timely identification of abnormal equipment vibration and risk forecasting, forming a unified and efficient operation and maintenance management mechanism, and effectively reduces the risk of equipment failure.
过刊浏览
资料下载
友情链接