A data-driven approach for railway in-train forces monitoring

Abstract

Railway in-train forces are an essential element in assessing multiple aspects of rolling stocks. Conventional methods for obtaining the forces can be time-consuming and require significant investment in manpower and domain expertise, while only gathering the force data for specific service conditions one at a time. However, automatic train operation (ATO) systems can measure real-time information for trains and tracks by on-board and trackside devices, which could provide an opportunity for in-train forces monitoring. This paper presents a data-driven approach that uses ATO-measured data and a neural network model to monitor in-train forces under service conditions. To develop this approach, longitudinal train dynamics simulations (LTSs) for a freight train were conducted to establish the relationship between ATO measurements and in-train forces on specific couplers, which was embedded in a large amount of training data. After that, a specially developed self-attention-based causal convolutional neural network (SA-CNN) was employed to learn the underlying relationship and estimate the in-train force histories considering temporal dependencies. The comparative evaluation between the SA-CNN against four alternative neural network models revealed that the SA-CNN exhibits a slightly higher level of accuracy. Furthermore, the generalisation capability of the well-trained SA-CNN model was confirmed by numerical LTSs under four different service conditions. The results indicated that the data-driven approach has superior compatibility for arbitrarily combined inputs with significantly reduced computational time compared to LTSs. This approach holds the potential for achieving reliable in-situ monitoring of railway in-train forces, which is beneficial to both in-train force-related research and industrial applications.