摘要
随着地下轨道交通在各大城市规模的持续增长,地铁车辆逐步进入高密度、大负荷的运行状态,地铁车辆的安全性、稳定性也随之降低,地铁事故慢慢增多,地铁检修时间和检修成本与日俱增,传统的计划修与故障修已无法适应目前的检修现状。本文提出一种基于决策树和层次分析的地铁车辆健康评估方法,利用神经网络蒸馏的软决策树定性地判断地铁车辆是否为健康状态,然后利用层次分析法在专家打分和权重计算的基础上定量地评估地铁车辆健康状态的具体分数。实验表明,在定性算法最优的情况下,定量地对地铁车辆健康状态进行宏观评估与把控,可以有效地预测地铁车辆的健康状态,并且该方法具有较好的鲁棒性。
With the continuous growth of underground rail in large cities,subway vehicles have gradually entered a high-density and heavy-load operation state,which will reduce the safety and stability of subway vehicles and bring more subway accidents.The time and cost of subway maintenance have been increasing day by day.Traditional planned repair and fault repair cannot adapt to the current status of maintenance.In this paper,a method of health assessment of subway vehicles based on decision tree and hierarchical analysis is proposed.The soft decision tree distilled by neural network is used to qualitatively judge whether the subway vehicles are in a healthy state or not.Then,according to the expert scoring and weight calculation,the specific scores of the health status of subway vehicles are quantitatively evaluated by analytic hierarchy process.Experiments show that under the condition of optimal qualitative algorithm,quantitative evaluation of the health status of subway vehicles can effectively predict the health status of subway vehicles,and the method is robust.
作者
吕坦悦
陆小敏
王健
LV Tan-yue;LU Xiao-min;WANG Jian(Institute of Ocean and Offshore Engineering,Hohai University(Nantong),Nantong 226300,China;College of Computer and Information,Hohai University,Nanjing 210098,China)
出处
《计算机与现代化》
2020年第3期29-32,43,48,共6页
Computer and Modernization
基金
南通市科技计划项目(GY12017014)
中央高校基本科研业务费资助项目(2018B16214)。
关键词
地铁车辆健康评估
决策树
层次分析
subway vehicle health assessment
decision-making tree
analytic hierarchy process
