摘要
决策树作为机器学习和数据挖掘领域中广泛应用的预测模型,其输出结果易于理解和解释。针对高速铁路车载智能设备数量庞大的流数据且设备故障复杂和诊断效率低等问题,采用CVFDT决策树算法,通过对规范化的列控设备流数据进行机器学习,构建车载设备智能故障预测模型(低概率发生、高概率发生和已发生故障),实现对设备潜在故障“事前排除”,提高故障分类精度、定位和诊断准确性,保障高速铁路运营安全和运输效率。
Decision trees are widely used as predictive models in the field of machine learning and data mining,and their output is easy to understand and explain.The onboard equipment of high-speed railway has problems such as large streaming data,complicated equipment failure and low diagnostic efficiency.According to the characteristics,the CVFDT decision tree algorithm is proposed to build an intelligent fault prediction model for vehicle equipment(low probability,high probability and failure)by machine learning of the normalized column control device stream data.It becomes“pre-exclusion”of potential equipment failures,improving fault classification accuracy,positioning and diagnostic accuracy,and ensuring high-speed railway operation safety and transportation efficiency.
作者
袁焦
王珣
潘兆马
杨学锋
邹文露
YUAN Jiao;WANG Xun;PAN Zhao-ma;YANG Xue-feng;ZOU Wen-lu(China Railway Eryuan Engineering Group Co.Ltd.,Chengdu 610031,China)
出处
《计算机与现代化》
2020年第12期49-54,共6页
Computer and Modernization
基金
成都市科技局重点研发支撑计划(2019-YF08-00160-GX)
中国中铁二院科研项目(KYY2019101(19-20))。
关键词
高速铁路
流数据
车载设备
CVFDT算法
high-speed railway
streaming data
onboard equipment
CVFDT algorithm
