Physical Model-Based Predictive Maintenance for Rail Infrastructure

The very first rails in the Netherlands were introduced in around 1839 and the maintenance activities over the past decades have been based mainly on experience from the past and from periodic inspections. With increasing traffic load, introduction of new vehicles and changing environmental conditions the prediction of rail damage becomes inaccurate and railway maintenance planning needs to be optimized. This thesis describes the utilization of physics-based models for rail damage prediction and railway maintenance planning optimization. A physical model is able to calculate the yet unknown future degradation if the current state of the system is known. To achieve this, numerical or mathematical descriptions of dominant failure mechanisms are usually used. However, physical failure models directly coupled with monitoring techniques for varying operating conditions in the rail infrastructure field are not
available. Most of the research in this field is conducted at material level and the application in maintenance modelling is limited. Hence, the main objective of this research is the further development and application of the physics-based models within the rail-infrastructure for varying conditions and to use the outcome (e.g. critical parameters and Remaining Useful Life) for monitoring and maintenance purposes. From the rail failure and maintenance cost database of Strukton Rail and also verified by literature study, it became apparent that wear and Rolling Contact Fatigue (RCF) are the most dominant degradation mechanisms for rail damage. Wear and RCF are caused by high stresses within the wheel-rail contact resulting from heavy loads and vehicle dynamics. Therefore, multi-body dynamics simulations are used to predict the dynamic behaviour and contact forces. However, the prediction for varying operation conditions means performing multiple simulations and can be time consuming. Therefore, this thesis proposes the use of meta-models for both wear and RCF in order to increase the computational efficiency.