Track grid health index for grid-based, data-driven railway track health evaluation

Abstract

Railway managers must have accurate assessments of railway track health to optimize maintenance and replacement scheduling and allocate resources reasonably. A model for railway track health evaluation, in which a continuous track line is divided into adjacent segments of the same length, referred to as track grids, is proposed in this study. A condition-evaluation index system for track grids was established, and deep autoencoder networks were used to reduce the dimensions of data on multiple condition measures. The set of all possible health features of the track grids was obtained using the hybrid hierarchical k-means clustering method. The tree-augmented naïve Bayes algorithm was employed to obtain the track grid health index and evaluate the overall health of the track grids. The proposed model was verified using measurement data from the Lanxin Railway in China. The proposed model was found to be superior to conventional health evaluation methods used in railway management in China. These results will enhance railway management knowledge and enable accurate determination of track health on a smaller spatial scale.