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State-of-the-art in managing reliability in mega railway projects. A systematic literature review
Abstract
Abstract Mega Railway Projects (MRPs) are expensive and account for an increasing percentage of many a nation’s annual infrastructure expenditure. These MRPs frequently exceed their budget and schedule. The challenge of achieving reliability or availability targets stands out as a contributing factor to these overruns. A robust and targeted Reliability, Availability, and Maintainability (RAM) process, which covers systems and subsystems that comprise the railway, that is imbedded in the project from the outset and that is managed throughout the life cycle of the project, is crucial for success. However, a RAM process for MRPs is not readily available. While BS EN 50126-11 sets out the required RAM related tasks there is no guidance on how these tasks are to be undertaken or managed. This omission is likely to increase the challenge faced by RAM or Systems engineers as they put forth their case for ring-fenced funds and labour at the outset of an MRP. It is therefore important that RAM on an MRP is reviewed so that next steps in developing robust RAM process plan guidelines can be determined. The authors of this paper discuss why RAM is undertaken and the conceptualisation of RAM along with its fundamental features. Its application on railways focusing on RAM techniques and BS EN 50126-1 is outlined. A Systematic Literature Review (SLR) is undertaken to show the state-of-the-art by using a meta and content analysis within the context of railway systems, RAM techniques, RAM standards and Reliability levels. Furthermore, a set of Derived RAM requirements (DRR) based on BS EN 50126-1 are derived to determine the critical areas of RAM and are thus recommended for further development by researchers or RAM practitioners.
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Metadata
- Authors
- King, J., Nicholson, G., & Schmid, F.
- Venue
- Safety and Reliability
- Publisher
- Informa UK Limited
- Published
- 2023-07-03
- Volume
- 42
- Issue
- 2-3
- Pages
- 60-100
- ISSN
- 0961-7353
- 2469-4126
- Cited by
- 0
- References
- 1
- Language
- en
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.