无砟轨道区段维修时间识别与劣化速率分析

Maintenance time identification and deterioration rate analysis for ballastless track sections

针对运营前期信息化程度不足导致的高速铁路线路历史维修记录缺失进而影响后期高速铁路运维合理规划的问题,建立基于信息熵和数值滤波的高速铁路高低不平顺维修时间识别模型.首先,基于挖掘长时间跨度下的轨道动检数据,获取精确的维修信息并揭示线路维修与不平顺劣化速度的关系.然后,将模型提取时间节点与某CRTSΙ型板式无砟轨道的综合检测列车历史维修数据对比,分析模型提取效果.最后,采用线性回归进行不同劣化时间段劣化速率预测和分析.结果表明:维修作业方案的改善程度不同,18%的维修方案能够实现高低不平顺原始数据70%的改善量,75%的维修方案能够实现高低不平顺原始数据30%的改善量;对比不同轨下结构类型,劣化速率最大的10%个线路区段所需平均维修周期隧道段最长,为28个月,桥梁段最短,为9个月;未开展维修的线路区段占整体线路的18%,高低不平顺标准差劣化速率分布较为集中,不超过0.01 mm/月;自2016年1月起,依靠日常检养修工作,预计下次维修时间最早为82个月后.

In response to the issue of historical maintenance record deficits in high-speed railway lines due to insufficient informatization in the early stages of operation,thereby impacting the rational planning of high-speed railway operation and maintenance in later stages,a model for identifying maintenance times of high and low track irregularities using information entropy and numerical filtering is established.Firstly,based on mining long-term track dynamic inspection data,precise maintenance information is obtained,revealing the relationship between track maintenance and irregularity deterioration rate.Next,the model’s extracted time nodes are compared with historical maintenance data of CRTSΙslab track for assessment of extraction effectiveness.Finally,linear regression is used to predict and analyze deterioration rates during different deterioration periods.Results indicate varying degrees of improvement among maintenance schemes:18%of schemes achieve a 70%improvement in the original high and low irregularity data,while 75%achieve a 30%improvement.Comparison across different substructure types reveals that the longest average maintenance cycle for the top 10%deteriorating track sections is found in tunnel sections at 28 months,and the shortest in bridge sections at 9 months.Sections without maintenance account for 18%of the total track,with the standard deviation of high and low irregularity deterioration rates being relatively concentrated below 0.01 mm/month.Since January 2016,relying on routine inspection and maintenance activities,the earliest estimated next maintenance time is expected in 82 months.