城市轨道交通正线列车故障发生概率预测模型
作者:
作者单位:

1.同济大学 道路与交通工程教育部重点实验室,上海 201804;2.同济大学 上海市轨道交通结构耐久与系统安全重点实验室,上海 201804;3.上海申通地铁集团有限公司技术中心,上海 201103

作者简介:

王镇波(1994—),男,博士生,主要研究方向为城市轨道交通规划与设计。 E-mail: wingzerb@foxmail.com

通讯作者:

叶霞飞(1961—),男,教授,博士生导师,工学博士,主要研究方向为城市轨道交通规划与设计。E-mail: yxf@tongji.edu.cn

基金项目:


Prediction Model of Train Fault Probability on Urban Rail Transit Main Line
Author:
Affiliation:

1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;2.Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China;3.Technology Center of Shanghai Shentong Metro Group Co., Ltd., Shanghai 201103, China

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    摘要:

    为了合理预测城市轨道交通列车在正线上发生故障的概率,首先通过定性分析得到列车编组数、累计走行公里、架修或大修经历为列车故障发生概率的主要影响因素。之后基于实际数据以每12万km为观测范围生成单列车在一定走行公里内故障发生次数的离散数据集,并根据数据呈现的分布特点选择泊松分布、零膨胀泊松分布及可能的函数形式构造3个备选模型。经过模型比选,最终提出基于泊松分布的城市轨道交通正线列车故障发生概率预测模型。结果表明:列车编组数的增加会提高列车故障发生概率;累计走行公里的增加会使列车故障发生概率先降低后回升,在列车投入运营后的第4个12万km阶段达到最低值,在第7个12万km阶段超过初始值。

    Abstract:

    A qualitative analysis was made to investigate the major influencing factors in predicting the probability of the train fault happening on urban rail main line. Then, a discrete dataset was collected about a single train’s fault in running for 120 000 km . Three alternative models were established on the basis of the data characteristics, Poisson distribution and zero-inflated Poisson distribution as well as the potential fault forms. According to the comparative study results,a Poisson distribution-based prediction model of train fault probability is finally proposed. Study results show that the train fault probability tends to increase with the increasing of train formation. It decreases first and then increases with the cumulative running kilometers,and the minimum train fault probability occurs in the fourth 120 000 km period, but the initial value is exceeded in the seventh 120 000 km period.

    表 2 模型估计结果Table 2
    表 4 不同影响因素组合下的列车故障发生概率Table 4
    图1 列车故障成因统计Fig.1 Statistics of train fault cause
    图2 单列车在各累计走行公里阶段故障发生次数的数据生成Fig.2 Data generation of fault occurrence number for single train in each cumulative running kilometer period
    图3 列车故障发生频率随累计走行公里阶段变化的趋势Fig.3 Trend of train fault frequency with cumulative running kilometer period
    图4 列车故障发生次数的观测频数Fig.4 Observation frequencies for different train fault occurrence numbers
    图5 各备选模型的ROC曲线Fig.5 ROC curves for each alternative model
    表 1 各累计走行公里阶段统计情况Table 1
    表 3 各累计走行公里阶段总列车故障记录数的预测结果Table 3
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引用本文

王镇波,叶霞飞,沈坚,施董燕.城市轨道交通正线列车故障发生概率预测模型[J].同济大学学报(自然科学版),2020,48(12):1751~1757

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  • 收稿日期:2020-02-18
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  • 在线发布日期: 2020-12-31