Abstract
Railway wheelsets are an essential part in rail transport systems, influencing safety, operational efficiency, and overall life cycle cost Life Cycle Cost (LCC). South Africa together the passenger rail operator has made a new long-term technical support and spares supply agreement with the introduction of modern electric multiple units (EMUs), and this has introduced the need for optimized wheel maintenance strategies. Although traditional preventive maintenance approaches are effective in reducing failures, these approaches may often result in higher costs and unnecessary maintenance interventions. This study aims to address the gap by applying data-driven predictive maintenance strategies within the South African context by understanding the effect of wheel measurement data on improving the maintenance planning, strategies and reducing the LCC. The research aims to quantify the impact of wheel measurement data on wheel life extension and cost savings, thereby informing both the rail operator and the original equipment manufacturer (OEM) on more efficient resource allocation.
A quantitative research design was adopted, utilizing historical wheel measurement data from EMU operations across different regions. Time-series modelling and LCC simulations were employed to evaluate the effect of predictive maintenance strategies compared to traditional overhaul-based approaches. Findings indicate that integrating wheel measurement data can extend wheel life by approximately 29–35%, reduce annual LCC significantly, and support a shift toward condition-based maintenance strategies. These results underscore the potential for predictive maintenance to transform rail asset management and inform contractual frameworks between OEMs and operators. Recommendations include enhancing data collection accuracy, adopting automated measurement technologies, and exploring machine learning applications for real-time prognostics. Future research should investigate seasonal and operational factors influencing wheel wear and conduct cost-benefit analyses of advanced measurement tools.