This new context driven Bayesian maintenance scenario will promote sustainable and cost-effective asset efficiency optimization in railway PHM and it will help us move closer to the ultimate goal of intelligent maintenance.
IN2RAIL is to set the foundations for a resilient, consistent, cost-efficient, high capacity European network by delivering important building blocks that unlock the innovation potential that exists in the SHIFT²RAIL Innovation Programmes (IP) 2 and 3.
Luleå University of Technology acts as a linked third party to Trafikverket with the responsibility of performing research activities with developing an overall concept for Intelligent Asset Management.
Due to limited resources and limited land area, the only way to adapt the infrastructure capacity to the expected increased transportation demand is to optimise the performance of the existing infrastructure.
The goal of railway infrastructure managers is to keep the RAMS parameters of railway system within acceptable thresholds at lowest possible cost. An efficient an effective way of achieving this goal is to employ applicable and effective maintenance and renewal strategy.
The goal of this project is to explore the human abilities to develop Situation Awareness of the changing situations of engineering systems in order to facilitate maintenance and make recommendations to improve Situation Awareness about intelligent maintenance systems.