基于变权组合模型的地铁车辆车轮踏面磨损预测

为准确获取地铁车辆车轮踏面随列车运行里程的磨损变化情况,选取灰色新陈代谢模型、二次指数平滑模型以及一元线性回归模型为单项预测模型,以样本点处组合预测误差绝对值最小为最优准则,建立了地铁车辆车轮踏面磨损变化趋势的最优非负变权组合预测模型。通过实例检验变权组合模型和各单项模型的预测性能,结果表明,构建的变权组合预测模型能够有效克服各单项预测模型的缺陷,预测精度及稳定性明显优于各单项预测模型,可为地铁车辆在实际运行过程中车轮踏面磨损趋势的准确预测提供了一种可行的求解方法。

Prediction of Metro Wheel Tread Wear Based on Variable Weight Combination Model

In order to obtain the accurate wear conditions of metro wheel tread with the running mileage,the gray metabolism model,quadratic exponential smoothing model and unary linear regression model are selected as the single prediction model,the minimum absolute value of combined prediction error at the sample points is taken as the optimal criterion,an optimal non-negative variable weight combination forecasting model is established for the wheel tread wear changing trend.The prediction performance of the variable weight combination model and that of each individual model are tested by practical cases,the results show that this model can effectively overcome the defects of single prediction models,the accuracy and stability of the variable weight combination prediction model are significantly better.It provides a feasible solution for the accurate prediction of wheel tread wear trend for metro trains in actual operation.