机车车轮磨耗统计数据处理方法与镟修周期预测模型

Processing Method of Locomotive Wheel Wear Statistical Data and Prediction Model of Turning Period

本文基于数理统计理论,研究一种机车车轮磨耗统计数据处理方法并建立机车车轮镟修周期预测模型。以D20E型机车车轮磨耗统计数据为算例,按照χ2标准分别对其与7种概率分布的合适性进行了检验。通过分析可知,正态分布模型能准确反映该型机车车轮的实际磨耗情况。采用该统计模型绘制D20E型机车车轮磨耗速率的密度分布与概率分布曲线,并计算了其统计数据特征。根据磨耗到限车轮所占比例,预测了不同的机车车轮镟修周期。在保障机车行车安全的前提下,建议D20E型机车的镟修周期为22万km。由该统计学方法计算得到的车轮镟修周期更加符合机车的实际运营情况,能够在一定程度上降低铁路的运营成本。

Based on the theory of mathematical statistics, a method to process the locomotive wheel wear statisti- cal data was studied and a model to predict the turning period for locomotive wheels was established. Taking the wheel wear statistical data of D20E locomotive as a calculation example, the suitability between the realistic data and 7 types of theoretical probability distribution was examined respectively in compliance with χ2 stand- ard. The analysis results showed that, the normal distribution model can well reflect the actual wheel wear condition of D20E locomotive. With the normal distribution statistic model, the density distribution curve and the probability distribution curve of the wheel wear rate were plotted and the statistical features of the data were calculated. According to the proportion of the wheels that have reached wear limit, different turning peri- ods for the wheels of the locomotive were predicted. Under the precondition of ensuring the running safety of the locomotive, the turning period for the D20E locomotive was recommended to be 220,000 km. The turning period concluded from the calculation with this statistical method is more consistent with the actual operation condition of the locomotive. As a result, the railway operating costs can be reduced to a certain extent.