基于6C系统大数据分析的接触网零部件缺陷特征研究

Research on Defect Characteristics of OCS Parts and Components Based on Big Data Analysis of 6C System

铁路供电专业构建6C系统对沿线接触网等供电设备进行全方位的检测监测,在提升养护维修效率的同时积累了海量数据,令基于大数据的分析成为可能。为促进数据在生产流通各环节的共享运用,综合考虑现场维修与数据分析的需求,采用线分类与面分类相结合的方法,形成6C缺陷描述分类规范;利用文本分析,对已归集全路6C系统缺陷数据中人工填写的缺陷描述字段特征进行挖掘,得到高频词组排序规律;利用可视化图表分析等方法,得到接触网零部件缺陷空间分布特征与规律。通过基于归集数据形成的缺陷图像库,验证了对智能识别算法训练效果的支撑作用。该研究可为接触网养护维修策略优化提供决策支持。

The railway power supply discipline realizes all-round inspection and monitoring of power supply equipment such as OCS along the line by building a 6C system,providing data support for condition-based maintenance.In order to promote the sharing and application of data in all links of production and circulation,considering the needs of on-site maintenance and data analysis,a 6C defect description classification specification is formed by combining line classification with plane classification.By using text analysis,the characteristics of manually filled defect description fields in the collected 6C system defect data of the of the whole railway are mined,and the sorting rules of high-frequency phrases are obtained.By using methods such as visual chart analysis,the spatial distribution characteristics and rules of defects in OCS parts and components are obtained.Through the defect image database formed based on the collected data,the supporting effect on the training effect of intelligent identification algorithm is verified.This study can provide decision support for the optimization of railway OCS maintenance strategy.