Maintenance decision support models for railway infrastructure using RAMS & LCC analyses
Today's railway sector is imposing high demands for service quality on railway infrastructure managers. Since railway infrastructure has a long asset life, it requires efficient maintenance planning to perform effectively throughout its life cycle to meet these high demands. Traditionally maintenance decisions for the railway infrastructure have been based on past experience and expert estimations. The application of RAMS (Reliability, Availability, Maintainability and Safety) analysis for railway infrastructure is limited. The focus of this thesis is to demonstrate the applicability of RAMS analysis in effective maintenance planning. Within the scope of this research, various case studies associated with Banverket (the Swedish National Rail Administration and ALSTOM Transport have been carried out. The research presents approaches and models for estimating RAMS targets based on the service quality requirements of the railway infrastructure. The availability target of the infrastructure has been estimated by considering the capacity and punctuality requirements of the infrastructure, whereas the safety goal of the track has been estimated by calculating the probability of derailment by means of undetected rail breaks and poor track quality. Effective estimation of the RAMS targets will help infrastructure managers to predict the maintenance investment in the railway infrastructure needed over a period of time in order to achieve the targets. Nevertheless, the availability target of the infrastructure can lead to train delay. A model has been developed to achieve the availability target in both the scheduled and the condition based maintenance regimes by choosing an effective maintenance interval and detection probability respectively. This has been illustrated by a case study on track circuits. Different maintenance strategies can help in achieving the RAMS targets. In order to determine the cost-effective solution, LCC (life cycle cost) should be used. The maintenance strategy with lowest LCC will be the cost effective maintenance strategy. This has been demonstrated by a case study on a signalling system. Sensitivity analyses have been performed to calculate the maximum cost effectiveness of the system for different maintenance parameters. LCC estimation for a maintenance strategy should always consider the risks associated with the strategy. A fair degree of uncertainty is also associated with LCC estimation due to the statistical characteristics of RAMS parameters. An approach has been developed in this thesis to calculate the uncertainties associated with LCC estimation. Petri-Net analyses, Monte Carlo simulations, Design of Experiment have been used to develop models to achieve the objectives of this thesis. This thesis discusses the applicability of RAMS and LCC analyses for railway infrastructure and demonstrates models for effective infrastructure maintenance planning.
School:Luleå tekniska universitet
Source Type:Doctoral Dissertation
Date of Publication:01/01/2009