Predictive maintenance of railway subsystems using an Ontology based modelling approach

  • Published 2011

Abstract

The trend towards a more efficient, cost-effective and performance oriented railway system is strongly pushed by political, economic and environmental motivations, as it represents an important and strategic asset for guaranteeing citizens and goods mobility at reasonable cost, involves a 35 billion €/year still growing market and is by far the lower impact transportation mode. Despite some sporadic and non-objective scepticism [1], railways are the best (maybe the only) candidate to achieve sustainable transport capacity and growth, provided that they can keep pace with increasing customers expectations, evolution of market demand and high safety/security levels. While the railways’ impressive technological progress is evident to all, a lot can still be done in system optimisation and efficiency improvement, in order to increase substantially their overall competiveness and market share, not only referring to state of the art high-speed trains, but also to high-capacity conventional trains. Information and Communication Technologies (ICT) have already brought important innovations to railways in terms of improved safety (signalling systems, like ERTMS), support to operations (monitoring and control systems) and customer services (passenger information, electronic ticketing and so on). They have been and will be essential in turning railways into an Intelligent Transportation System [3], following basic guidelines stated by the new Directive on the interoperability of the rail system within the Community [5]. An area where improvements are needed and can be achieved using advanced ICT solutions is that related to rolling-stock maintenance and condition monitoring. The principal goal is to minimise the probability of a mission-critical fault occurring during train service, with strong consequences in terms of quality of service and extra costs, while at the same time keeping maintenance costs as low as possible, in order to reduce life-cycle spending and improve return of investment.

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Cite this paper

@inproceedings{2011PredictiveMO, title={Predictive maintenance of railway subsystems using an Ontology based modelling approach}, author={}, year={2011} }