城市轨道交通信号系统智能运维体系构建与应用研究
于萌1鲁怀科2梁喆2范仔超2高宁2
Construction and Application of Intelligent O&M System for Urban Rail Transit Signaling Systems
YU Meng1LU Huaike2LIANG Zhe2FAN Zichao2GAO Ning2
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作者信息:1.北京市地铁运营有限公司通信信号分公司， 100082， 北京
2.广西交控智维科技发展有限公司， 530025， 南宁
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Affiliation:1.Communication and Signal Branch of Beijing Subway Operation Co., Ltd., 100082, Beijing, China
2.Guangxi Zhiwei Technology Development Co., Ltd., 530025, Nanning, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.20253060
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中图分类号/CLCN:U231.7
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栏目/Col:通信信号
摘要:
［目的］针对城市轨道交通运维发展中面临的四大问题，以南宁轨道交通4号线为实践载体，旨在构建一套基于数据驱动与智能决策的信号系统智能运维体系，突破传统运维模式的技术与管理瓶颈，实现高效、精准、可持续的运维管理。［方法］通过整合多源异构数据并构建多层架构，实现了设备状态实时监测与劣化趋势分析，显著提升数据利用率与故障识别能力；融合复杂网络理论与设备失效机理分析，开发覆盖关键设备的故障预测模型，支持故障自动定位与提前预警。在管理体系上，通过数字化流程重构、轻资产物资配置及跨专业综合班组建设，打破传统数据孤岛与专业壁垒，形成“人机料法协同”的运维生态。［结果及结论］在南宁轨道交通4号线工程化应用中，智能运维体系推动运维模式实现三大转型：从被动响应转向主动预防，从经验驱动转向数据驱动，从分散管理转向协同优化。实践表明，智能运维体系通过智能化技术赋能与流程再造，显著提升了运维效率与设备可靠性，为行业提供了可复制的解决方案。未来研究将进一步探索人工智能和机器自学习等前沿技术在智能运维系统上的应用，推动轨道交通运维体系向更智能、更可持续的方向发展。
Abstracts:
［Objective］ In response to four major challenges faced in the development of urban rail transit O&M (operation and maintenance), Nanning Rail Transit Line 4 is taken as the practice platform to construct an intelligent O&M system for signaling system driven by data and intelligent decision-making. The goal is to break through the technical and managerial bottlenecks of traditional O&M models and achieve efficient, precise, and sustainable O&M system. ［Method］ By integrating multi-source heterogeneous data and building a multi-layer architecture, the system enables real-time equipment status monitoring and degradation trend analysis, significantly improving data utilization and fault identification capabilities. By leveraging complex network theory and equipment failure mechanism analysis, a fault prediction model covering key equipment is developed to support automatic fault localization and early warning. In terms of management, through refactoring digital workflows, allocating light-asset material, and establishing cross-disciplinary integrated teams, the O&M system eliminates traditional data silos and professional barriers, forming an ecosystem encompassing ′personnel-equipment-material method synergy′. ［Result & Conclusion］ In the engineering application of Nanning Rail Transit Line 4, this intelligent system promotes three major transformations in the O&M model: from passive response to proactive prevention, from experience-driven to data-driven, and from fragmented management to coordinated optimization. Practice has demonstrated that the intelligent O&M system, empowered by intelligent technologies and process re-engineering, significantly enhances O&M efficiency and equipment reliability, offering a replicable solution for the industry. Research in the future will further explore the application of cutting-edge technologies such as artificial intelligence and machine self-learning in intelligent O&M systems, promoting the evolution of rail transit O&M system towards greater intelligence and sustainability.
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