城市轨道交通正线列车故障发生概率预测模型

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

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.