Dovile Rama
A holistic approach to railway infrastructure asset management
Rama, Dovile; Andrews, John D.
Authors
John D. Andrews
Abstract
In the railway industry asset management decisions are focused on the maintenance, enhancement and renewal of assets in order to ensure a required level of dependability and improvement in services at the lowest whole life costs. To achieve these objectives system lifecycle models, rather than individual asset models,= offer a greater advantage. The paper presents a modelling approach developed for constructing multi asset system models to support well-informed railway infrastructure asset management decisions. The models are built using the Petri Net formalism and are analysed by a means of Monte Carlo simulations. A specific example of the railway superstructure model is presented. Its simulation results demonstrate the superiority of the system-wide model against individual asset models in terms of its accuracy in predicting the superstructure (system) performance and information available to support asset management decisions. Furthermore, by using the multi-asset system model interdependencies among maintenance regimes of different assets and different parts of the infrastructure can be modelled.
Citation
Rama, D., & Andrews, J. D. (2015). A holistic approach to railway infrastructure asset management. International Journal of Performability Engineering, 11(2),
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 26, 2014 |
Publication Date | Mar 30, 2015 |
Deposit Date | Dec 16, 2016 |
Publicly Available Date | Dec 16, 2016 |
Journal | International Journal of Performability Engineering |
Electronic ISSN | 0973-1318 |
Peer Reviewed | Peer Reviewed |
Volume | 11 |
Issue | 2 |
Keywords | Railway infrastructure, asset management, system-wide models, Petri Nets |
Public URL | https://nottingham-repository.worktribe.com/output/746459 |
Publisher URL | http://www.ijpe-online.com/march-2015-p1-a-holistic-approach-to-railway-infrastructure-asset-management.html#axzz4T0hL0Z2I |
Contract Date | Dec 16, 2016 |
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