Archard model guided feature engineering improved support vector regression for rail wear analysis

Abstract

This paper applied Archard wear law in feature engineering for the improvement of Support Vector Regression (SVR) performance and realized the rail steel wear behavior prediction. The actual complex rail wear multidimensional degradation information was obtained from field maintenance records over a decade and the hidden data outliers raised the modelling challenges. We applied pre-process technologies including feature importance analysis, physical model guided feature generation and outlier detection to build up the SVR based robust nonlinear regression analysis framework. Individual railway parameters’ effects on the wear process were investigated and revealed through model interpretation-based post analysis. This work provides a practical approach to deploying machine learning algorithms for rail service maintenance data analysis and treatment of data outliers.