Dependability and maintenance analysis of railway signalling systems
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Railway signalling systems are composed of several different systems; each has its own purpose, but the main functionality of the overall system is determined by the interoperability between them. Railway signalling systems ensure the safe operation of the railway network, and their reliability and maintainability directly affect the capacity and availability of the railway network, in terms of both infrastructure and trains. The functionality of the signalling system is based on the principle of “fail safe”; this means that the railway section where a failure is located will be not fully operative until the failure is repaired (since safety cannot be ensured). Hence, the dependability of these systems directly affects the capacity of the network.Signalling systems take up a large part of the railway’s overall corrective maintenance. Railway managers need to have a holistic view of all systems to optimise maintenance. Signalling systems are especially important, given the need for interoperability. Given their complexity, knowledge must be correctly managed to ensure proper performance in all phases of the life cycle. Enhancing information logistics would lead to considerable improvements in this area. This licentiate analyses the dependability and maintenance of railway signalling systems and proposes various approaches to improve maintenance performance. External factors affecting the reliability of signalling systems are identified, such as their location. The signalling system is treated as a system of systems because of its interoperability and because failures occurring on different systems can be associated with the same failure effect. A data driven model for maintenance decision support is proposed, based on corrective maintenance work orders. The data driven model allows a holistic perspective of failure occurrence, as it integrates the information recorded in the many different parameters of the corrective maintenance work orders. With this model, existing maintenance policies could be reviewed and improved upon. This thesis proposes a model for configuration management, which simplifies the access and visibility of information. The model manages the change control process and ensures that configurations are updated in real-time. An enhancement of the configuration management has the potential to increase the efficacy of the maintenance actions in signalling systems by improving the accessibility of the information required to understand possible future failures. With increased accessible knowledge, the time needed to identify failures can be reduced, resulting in greater maintenance efficiency. It also establishes a framework for improving inter-organisational knowledge management between stakeholders, resulting in the creation of a holistic perspective of the maintenance and operation of the railway network, avoiding the loss of knowledge linked to outsourcing, and improving the effectiveness of the organisations involved.
Place, publisher, year, edition, pages
Luleå tekniska universitet, 2014. , 150 p.
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Research subject Operation and Maintenance
IdentifiersURN: urn:nbn:se:ltu:diva-17928Local ID: 5e6314c3-0039-4457-bc1e-898769bd0b07ISBN: 978-91-7439-841-0ISBN: 978-91-7439-842-7 (PDF)OAI: oai:DiVA.org:ltu-17928DiVA: diva2:990934
Godkänd; 2014; 20140114 (ampmor); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Amparo Morant Ämne: Drift och underhållsteknik/Operation and Maintenance Uppsats: Dependability and Maintenance Analysis of Railway Signalling Systems Examinator: Professor Uday Kumar, Institutionen för samhällsbyggnad och naturresurser, Luleå tekniska universitet Diskutant: Adjunct Professor Peter Söderholm, Trafikverket – LTU Tid: Fredag den 14 februari 2014 kl 10.00 Plats: F1031, Luleå tekniska universitet2016-09-292016-09-29Bibliographically approved