Railway track design & degradation

The long-term behaviour of railway track has attracted increasing attention in recent years. Improvements in long-term structural performance reduce demands for maintenance and increase the continuous availability of railway lines. The focus of this paper is on the prediction of the sensitivity of a track design to long-term deterioration in terms of track geometry. According to the state of the art literature, degradation is often investigated using empirical models based on field measurement data. Although a rough maintenance forecast may be made employing empirical models, the predictions are not generic, and the physical processes which govern track degradation under train operation remain unclear. The first aim of this study is to present a mathematical model to elucidate the underlying physics of long-term degradation of railway tracks. The model consists of an infinitely long beam which is periodically supported by equidistantly discrete sleepers and a moving unsprung mass which represents a travelling train. The mechanical energy dissipated in the substructure is proposed to serve as a measure of the track degradation rate. Secondly, parametric studies on energy dissipation are conducted to identify effects of various track design parameters on the susceptibility of the track to degradation, as well as the effect of the train speed. It has been shown that the track/subgrade stiffness is the most influential parameter on degradation whereas other system parameters do influence the degradation rate but at lower magnitudes. The conclusions can be used to optimise the track design in the early stage for better long-term structural performance of railway tracks

Towards network assessment of permanent railway track deformation

The permanent railway track deformation caused by regular train traffic is important for infrastructure managers and railway contractors, as it determines the railway track quality. Although several successful approaches have been made to address the topic of the permanent railway track deformation, these have only been applied at specific locations, and have not yet been successfully applied at a network level. This paper presents a methodology that can be applied at the network level, by making use of a stochastic subsoil model to characterise the subsoil uncertainty and variability along the railway line, and by combining it with a dynamic train-track model and a cumulative cyclic deformation model. This methodology is illustrated by analysing a railway track section of 9 km in the Netherlands. The effects of the train service, such as train speed and axle loads, on the permanent deformation of the track are quantified. The proposed methodology has been partially validated against results of the dynamic stiffness obtained during the passage of a measurement train. The results illustrate the added value of this methodology for infrastructure managers and railway contractors as it allows for the quantification, at network level, of the consequences of train service changes for the future state of the railway network

Intelligent Data Fusion for Anomaly Detection in Dutch Railway Catenary Condition Monitoring

Aiming to handle the increasing variety and volume of railway infrastructure monitoring data, this paper explores the use of intelligent data fusion methods for automatic anomaly detection of railway catenaries. Three classical data dimensionality reduction methods, namely the principal component analysis (PCA), the autoencoder neural network, and the t-distributed stochastic neighbor embedding (t-SNE) are adopted for the data fusion of catenary monitoring data. Then, anomaly detection can be achieved using new features that are automatically extracted from the original data, which requires no prior knowledge of the data or catenary conditions. A case study using data measured from the Dutch railway is presented to compare the performance of the three methods. Six types of catenary monitoring data, including pantograph-catenary contact force, pantograph-catenary friction force, contact wire thickness, contact wire height and stagger, are used in the presented case study. It is demonstrated that both PCA and autoencoder can detect anomalies from catenary monitoring data, while t-SNE shows little indication of such ability. Further, the autoencoder outperforms PCA in distinguishing anomalies in the case study, likely owing to its superiority in analysing data with nonlinearity. Overall, autoencoder is a promising technique for automating the anomaly detection of railway catenaries. The detection results can provide indicators for failure prediction and maintenance decision making.Railway Engineerin