A hybrid maintenance approach for key components of the train bogie to optimize fleet availability

In the context of intelligent transport systems and the rapid development of the global economy, railways face increasing demand to become safer, more efficient, and more sustainable. As a critical structure of the railway vehicle, the bogie, designed to support the vehicle body and transmit dynamic loads through the wheels, houses several components that are vital for the reliability and safety of the train. The maintenance of such a structure is complex and can greatly benefit from hybrid approaches that leverage the potential of condition-monitoring data in proactively identifying faults or irregularities whilst ensuring appropriate intervals that maximize the availability of resources. This paper proposes a hybrid maintenance approach that combines preventive maintenance, condition-based monitoring, and predictive maintenance for the key components of the bogie, aiming to optimize costs while exploiting potential economic savings of maintenance decisions for different grouping strategies, therefore, maximizing fleet availability. The model aims to study the feasibility and measure the expected economic returns of shifting from a complete preventive maintenance program to a hybrid program incorporating predictive maintenance tasks in selected components of the bogie. A deterioration model, along with failure rate estimation, is proposed and integrated into the optimization formulation. A case study of a railway company operating in Catalonia, Spain, is presented. The framework demonstrates flexibility in tackling current maintenance practices, including hierarchy and periodicity of maintenance tasks according to the intervention type and flexible hazard rates with three stages to mimic the bathtub curve behaviour. The results show that shifting to a hybrid maintenance plan has great potential to reduce costs. • Grouped maintenance strategy considers various railway bogie subsystems. • Degradation model for bogie with failure rates from expert judgment and literature review. • MILP model for group maintenance schedule aligns with current railway practices. • Framework’s flexibility supports predictive maintenance tasks effectively.