基于灰色成分数据模型的轨道不平顺指标结构预测
Prediction for track irregularity index structure based on grey compositional data model

针对轨道不平顺指标结构变化规律，提出一种基于灰色成分数据模型的轨道不平顺指标结构预测模型，结合轨道不平顺指标结构特征，对轨道不平顺指标结构进行预测。通过采用兰新线上行K741+200~K741+400和K741+400~K741+600 2单元区段共6年的历史轨检车超限数据对预测模型的有效性进行验证，结果表明所建模型可以较好地应用于预测轨道不平顺指标结构。
In view of the variation law of the track irregularity index structure, a prediction model of track irregularity index structure based on grey compositional data model was proposed. In the light of the grey compositional data model theory and characteristics of track irregularity index structure, the model was used to predict track irregularity index structure. A total of 6 years track inspection car historical overrun data of two unit sections (K741+200~K741+400 and K741+400~K741+600) of Lanzhou-Xinjiang railway up line was used to illustrate the effectiveness of the model. Results show that the model can be used to predict the track irregularity index structure well