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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 104
Edited by: J. Pombo
Paper 177

Reliability Analysis of Railway Rolling Stock Failure Patterns

B.M. Alkali1, G. Brown2, C. Tait2 and V. Orsi1

1Department of Mechanical Electrical and Environmental Engineering, Glasgow Caledonian University, United Kingdom
2First ScotRail Limited, Glasgow, United Kingdom

Full Bibliographic Reference for this paper
B.M. Alkali, G. Brown, C. Tait, V. Orsi, "Reliability Analysis of Railway Rolling Stock Failure Patterns", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 177, 2014. doi:10.4203/ccp.104.177
Keywords: reliability, rolling stock, preventive maintenance, delay time.

This paper presents an investigation conducted on the reliability of the classes of railway rolling stock safety critical systems. It is evident that the safety critical systems of trains affect the performance and the availability to operate a schedule service. These occurrences can cause large delays which subsequently result in additional maintenance expenditure and financial penalties to the service providers. This paper identifies the probable causes of in-service train failures in Scotland. Train wheel wear patterns damage suspension components, causing poor train operation resulting to costly unplanned maintenance and corrective repairs. Door failures contribute to delays, cancellations and part cancellations. In this paper, an in-depth analysis of historical failure data is conducted to have better idea of the technical requirements such as adequate inspection frequencies, preventive maintenance, appropriate profiling and measurement requirements. The relevance of the use of opportunistic maintenance when they arise, the risk of using an opportunity to do preventive maintenance and inspections on trains is investigated. A delay time model is considered for modelling the reliability of safety critical failure patterns and periodicity using existing history of failure and maintenance data records. The outcome of the investigation identifies operational and opportunities to model hard and soft defects and failures. The model algorithm developed is used for modelling analysis to support long term decisions on optimal cost effective replacement and maintenance strategy.

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