城市轨道交通多专业融合主动维修决策关键技术研究

Key Technologies for Multi-disciplinary Fused Proactive Maintenance Decision-Making in Urban Rail Transit

目的:城市轨道交通客流持续增长,线网运营对故障容忍度越来越小,特别是涉及多专业的综合故障,因此需要结合不同专业的智能运维和大数据技术研究多专业融合主动维修决策模式。方法:分析了多专业主动维修需求及其响应路径,探讨了其中的关键技术与难点;提出了多专业融合主动维修决策的功能架构与设备健康管理平台架构;讨论了面向多专业设备的健康度评价与预测方法。结果及结论:多专业融合主动维修决策的实现需要充分发挥数据资源优势,针对不同数据类型、数据丰富度以及数据分析目标设计相应的采集方法、存储方法、处理方法与分析方法。基于健康度的维修决策评估与预测能够为多专业设备的维护、大修与延长使用寿命提供决策支撑。

Objective:As the passenger flow of urban rail transit continues to grow,the tolerance for operation failures in line network is decreasing,especially for the comprehensive failures involving multiple disciplines.Therefore,it is necessary to study the multi-disciplinary fused proactive maintenance decision-making model that combines intelligent operation-maintenance from various disciplines and big data technology.Method:By analyzing the demand and response pathways for multi-disciplinary proactive maintenance,the key technologies and difficulties involved are explored,and the functionality structure for decision-making on multi-disciplinary fused proactive maintenance and the structure of equipment health management platform are proposed.Health degree assessment and prediction methods targeting multi-disciplinary equipment are discussed as well.Result and Conclusion:Implementation of multi-disciplinary fused proactive maintenance decision-making requires leveraging the advantages of data resources,which involves designing appropriate methods for data collection,storage,processing and analysis with specific consideration of different data types,richness,and analysis objectives.Maintenance decision-making evaluation and prediction based on health degree can facilitate the decision-making in maintenance,overhaul and service life extension of multi-disciplinary equipment.