Abstract Summary
Understanding the dynamics of the interaction between railway vehicles and tracks is essential for forecasting condition and performing maintenance action to preserve the safety of railway infrastructure. In this work, physics-based models are deployed to predict the dynamic response of railway vehicles to track alignment and irregularities. Such models comprise a large number of parameters that need to be validated and possibly tuned; a task often accomplished on the basis of expert knowledge, without necessarily reflecting the condition of the operating system as-is. Uncertainties persist due to idealizations and approximative assumptions on the actual system behavior, as well as due to varying operational parameters (wheel profile, rail profile, rail moisture). In improving condition estimation capabilities, this study adopts a multi-body vehicle model, realized in SIMPACK software, and performs parameter tuning based on on-board measurement data (accelerations and forces) from an instrumented tilting train, which regularly traverses the Swiss Federal Railways (SBB) network. The pertinent vehicle model is optimized in terms of the interaction forces at the wheel-rail contact using a Bayesian model updating approach, relying on Markov Chain Monte Carlo (MCMC). The MCMC method is applied to estimate both vehicle parameters and their uncertainty, ultimately resulting in improved contact force estimations. We show that by applying MCMC, the mean squared error between simulated and measured forces can be significantly reduced, leading in an improved vehicle twin.
