Railway Force Reconstruction Under Sparse Onboard Sensing: A Comprehensive Computational Approach

Abstract

The continuous and accurate monitoring of wheel-rail contact forces is fundamental to ensuring the operational safety, optimizing the maintenance scheduling, and enhancing the passenger comfort of modern railway vehicles. Traditional direct measurement techniques, such as instrumented wheelsets, suffer from high implementation costs, structural fragility, and limited long-term reliability in harsh operating environments. Consequently, indirect measurement approaches utilizing onboard inertial sensors have garnered significant attention. However, practical deployment constraints necessitate the minimization of sensor counts, leading to the challenge of force reconstruction under sparse sensing conditions. This paper presents a comprehensive computational framework for the dynamic reconstruction of vertical and lateral wheel-rail forces utilizing a highly restricted set of bogie and carbody acceleration measurements. By integrating advanced multibody dynamics modeling with a physics-informed data-driven inverse reconstruction algorithm, the proposed methodology overcomes the ill-posed nature of sparse state estimation. The study extensively investigates the optimal sensor placement configurations and develops a robust time-domain integration scheme that remains resilient to localized sensor noise and transient data loss. Extensive numerical simulations, incorporating realistic track irregularities and complex vehicle-track interactions, demonstrate that the proposed framework achieves high reconstruction fidelity across various operating speeds and track conditions. The findings offer a scalable and economically viable pathway for implementing real-time continuous force monitoring in commercial railway fleets, thereby advancing the paradigm of condition-based predictive maintenance.