smart detection of wheel defects using artificial intelligence and wayside monitoring system

As the rail sector plays a significant role in society, traffic and maintenance costs are criticalaspects for railway managers and operators. although current track wayside monitoring systemsdetect geometric defects in wheels, such as flats, they do not categorize them in terms of severity.to overcome this limitation, the way4saferail project aims to develop techniques to improverail safety by determining the damage conditions of train wheels. as a preliminary step in applyingartificial intelligence techniques, the present paper presents part of the research developed in theway4saferail project, particularly the numerical simulations of wheel defects. the proposedmethodology has shown to be a reliable and cost-effective method for identifying wheel defects.