The project should develop methods that can provide high-quality deterioration estimates as input to RAMS (Reliability, Availability, Maintainability and Safety) and LCC (Life Cycle Cost) analyses of railway components. RAMS and LCC are general frameworks that are highly sensitive to the quality of input parameters. The ultimate goal is thus to enhance predictive capabilities of these methods when applied to railway operations. Such enhanced predictive capabilities in a railway setting could potentially lead to significant cost reductions for railway operations due to improvements in investment and maintenance strategies. To this end, the project aims to develop methods for robust estimates of degradation rates for chosen components in the railway system as functions of given traffic scenarios. The methods will be based on numerical simulations of train–track interaction that can account for the scatter in traffic parameters typically observed in railway operations. Examples of traffic parameters that can vary are vehicle type, train speed, wheel and rail profiles etc. Identified sub-tasks include the characterization of traffic scenarios using statistical methods and the creation of load collectives which are representative for the traffic scenario at hand. In order to make the methods computationally efficient, identification of the most influential parameters with regard to degradation is also of interest. The uncertainty in deterioration rates due to uncertainties in input parameters will be considered.
Senior Lecturer at Chalmers, Mechanics and Maritime Sciences, Dynamics
Funding Chalmers participation during 2014–2021
Areas of Advance