PREDICTIVE ANALYSIS OF DEFECTIVE WHEEL–RAIL INTERACTION FOR OPTIMIZATION OF RAILWAY TRACK MAINTENANCE AND SAFETY

Increase in railway demand has significantly been met by rail operators through technological advances that have seen increase in train speed and axle loads, both to which have ensured an increase in operating capacity. However, from the infrastructure side, this has led to an increase in rail traffic demand that has been met with little extensions of rail infrastructure. Infrastructure managers (IMs) are faced with the challenge of having to increase the number of runs passenger and freight trains make across a relatively fixed network. This growing pressure on increasing operation time also reduces the infrastructure accessible time for maintenance activities. Furthermore, the traffic on railway lines varies from vehicle type, load, and speed which subjects the track to a wide range of stresses. These stresses and generally effects of rail vehicle performance on the infrastructure need to be monitored and managed effectively to avoid operational downtime and delays, increased maintenance and in the worst-case scenario derailment. Therefore, there is need for infrastructure managers (IMs) to adopt innovative and sustainable operation and maintenance strategies to be able to meet these challenges. With improved understanding of rail vehicle running behaviour on the track from the wheel–rail interface perspective, infrastructure managers (IMs) can be better placed to formulate suitable cost-effective asset management and maintenance strategies. Optimization of rail infrastructure maintenance highly helps to decrease interruptions of train operation, reduce costs, and ensure safety. In this thesis focus is placed on the wheel–rail interface. Defective wheelset lateral movement on the rails (truck hunting) is addressed. Potential safety and maintenance issues i.e., derailment and rolling contact (RCF) defects respectively that may arise due to excessive truck hunting is also addressed. The study aims to facilitate improvements and optimization of maintenance decision-making for railway infrastructure.