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Prediction of railway track geometry defects: a case study

机译：铁路轨道几何缺陷预测 - 以案例研究

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摘要

The aim of this study has been to develop a data-driven analytical methodology for prediction of isolated track geometry defects, based on the measurement data obtained from a field study. Within the study, a defect-based model has been proposed to identify the degradation pattern of isolated longitudinal level defects. The proposed model considered the occurrence of shock events in the degradation path. Furthermore, the effectiveness of tamping intervention in rectifying the longitudinal level defects was analysed. The results show that the linear model is an appropriate choice for modelling the degradation pattern of longitudinal level defects. In addition, a section-based model has been developed using binary logistic regression to predict the probability of occurrence of isolated defects associated with track sections. The model considered the standard deviation and kurtosis of longitudinal level as explanatory variables. It has been found that the kurtosis of the longitudinal level is a statistically significant predictor of the occurrence of isolated longitudinal level defects in a given track section. The validation results show that the proposed binary logistic regression model can be used to predict the occurrence of isolated defects in a track section.
机译：本研究的目的是基于从现场研究获得的测量数据，开发用于预测隔离轨道几何缺陷的数据驱动的分析方法。在该研究中，已经提出了一种基于缺陷的模型来识别分离的纵向水平缺陷的降解模式。所提出的模型被认为是降解路径中的冲击事件的发生。此外，分析了夯实干预在整流纵向水平缺陷方面的有效性。 The results show that the linear model is an appropriate choice for modelling the degradation pattern of longitudinal level defects.此外，已经使用二元逻辑回归开发了一种基于截面的模型，以预测与轨道部分相关联的分离缺陷的发生概率。该模型认为是纵向水平的标准偏差和峰值，作为解释性变量。已经发现，纵向水平的峰值是在给定轨道部分中分离的纵向水平缺陷的统计上显着的预测因子。验证结果表明，所提出的二进制逻辑回归模型可用于预测轨道部分中分离缺陷的发生。

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