Reliability and availability assessment of railway locomotive bogies under correlated failures

Abstract

Railways play a pivotal role in transportation systems worldwide. Effective maintenance policies are crucial to guarantee the reliability and availability of such systems inserted in a challenging and competitive environment. In this scenario, condition-based and predictive maintenance strategies emerge as key components of the European rail traffic system, where common advanced monitoring solutions of railway assets serve as performance metrics for the development of maintenance rules. However, the effectiveness of a good maintenance policy is intrinsically dependent on a clear assessment of the system on target, including not only aspects related to its complexity and financial importance, but fundamentally the physical characteristics and the environmental and operational conditions to which the system is subject to. In this sense, simulation models are useful as they can mimic the behaviour of systems, especially those too complex to solve analytically, incorporating the characteristics and the inherent stochastic behaviour associated with them, including their correlation structure. This study focuses on the locomotive bogie, which is a complex subsystem of the train onto which the wheels of the vehicle are fixed. The main components of the bogie are described as well as the stochastic behaviour associated with the occurrence of failure. A Discrete Event Simulation (DES) model is proposed to study the impact on the system, in terms of reliability and availability, under different scenarios with varying assumptions on the underlying failure modes, repairs, and on the failures’ correlation structure. The results point out that the model is suitable for representing the main characteristics of this train subsystem, and it identifies the parameters and components that are more critical in terms of reliability and availability fluctuations when contrasted with the variations imposed on the model by considering different scenarios. Overall, the DES model allows the robust assessment of where the focus should be put according to the uncertainty embedded in the correlations of failures and/or in the maintenance durations.