ABSTRACT
ABSTRACT In this paper, risk-centred approaches are presented on how to optimize decision-making for the maintenance of railway transport assets, including rail infrastructure and rolling stock systems. In the presented approaches, the repair and maintenance requirements for various components are analyzed, prioritized and optimized based on the risk assessment and criticality analysis information. For the identification and evaluation of systems failure modes and failure effects, some expert-driven tools such as safety integrity level (SIL) analysis, root cause analysis (RCA), fault tree analysis (FTA), reliability block diagram (RBD), and failure mode and effects analysis (FMEA) are used, whereas for the planning of maintenance tasks data-driven methods such as artificial neural network (ANN), genetic programming (GP), decision tree, etc. can be applied. For the purpose of clearly illustrating the proposed approaches, a number of case studies from rail operating companies will be provided.
