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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 104
Edited by: J. Pombo
Paper 299

Smart Railroad Maintenance Engineering with Stochastic Model Checking

D. Guck1 and J.-P. Katoen1,2 and M.I.A. Stoëlinga1, T. Luiten3 and J. Romijn4

1University of Twente, the Netherlands
2RWTH Aachen University, Germany
3ProRail, the Netherlands
4Movares Nederland, the Netherlands

Full Bibliographic Reference for this paper
D. Guck, J.-P. Katoen, M.I.A. Stoëlinga, T. Luiten, J. Romijn, "Smart Railroad Maintenance Engineering with Stochastic Model Checking", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 299, 2014. doi:10.4203/ccp.104.299
Keywords: dynamic fault trees, maintenance, availability, reliability, cost, recovery.

RAMS (reliability, availability, maintenance and safety) requirements are of utmost important for safety-critical systems like railroad infrastructure and signaling systems. Fault tree analysis (FTA) is a widely applied industry standard for RAMS analysis and is often one of the techniques preferred by railways organizations. FTA yields system availability and reliability, and can be used for critical path analysis. It can however not yet deal with a pressing aspect of railroad engineering: maintenance. While railroad infrastructure providers are focusing more and more on managing cost/performance ratios, RAMS can be considered as the performance specification, and maintenance the main cost driver. Methods facilitating the management of this ratio are still very uncommon. This paper presents a powerful, flexible and transparent technique to incorporate maintenance aspects in fault tree analysis, based on stochastic model checking. The analysis and comparison of different maintenance strategies (such as age-based, clockbased and condition-dependent maintenance) and their impact on reliability and availability metrics are thus enabled. Thus, the trade off between cost and RAMS performance is facilitated. To keep the underlying state space small, two aggressive state space reduction techniques are employed namely: compositional aggregation and smart semantics. The approach presented is illustrated using several existing, large fault tree models in a case study from Movares, a major RAMS consultancy firm in the Netherlands.

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