Project Summary
Wheelset inspections on freight wagons currently depend heavily on manual checks, often performed in challenging conditions. This approach can introduce inefficiencies, human error and increase operational costs. Manual inspections are also time-consuming, which can delay fault detection and negatively impact safety and maintenance planning. Maintaining wagon wheelsets, including wheels, axles and brakes costs the UK rail freight sector around £60 million annually. Wheel wear and damage can cause significant service disruptions and pose risks to both safety and the environment. A low-cost, automated inspection system could improve asset management, reduce maintenance costs, and support a more proactive and data-driven maintenance strategy. Advancements in monitoring technology are facilitating the development of automated inspection systems that integrate innovative image-based dimensional measurement with machine learning for effective fault detection. These non-contact, automated solutions enhance accuracy and repeatability, reduce the need for personnel to enter hazardous environments, and enable more comprehensive and reliable data capture. A previous proof-of-concept study (TRL 2) demonstrated the feasibility of extracting key wheel dimensions from images taken in a maintenance facility. Building on this foundation, the current project aims to develop and validate a fully automated wheelset inspection system in a realistic operational railway environment (TRL 4/5).
