Smart railroad maintenance engineering with stochastic model checking

Dennis Guck, Joost P. Katoen, Mariëlle Ida Antoinette Stoelinga, Ted Luiten, Judi Romijn

  • 6 Citations

Abstract

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.
Original languageUndefined
Title of host publicationProceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, Railways 2014
EditorsJ. Pombo
Place of PublicationStirlingshire, UK
PublisherCivil Comp Press
Pages299
Number of pages15
ISBN (Print)1759-3433
DOIs
StatePublished - Apr 2014

Publication series

NameCivil-Comp Proceedings
PublisherCivil-Comp Press
Volume104
ISSN (Print)1759-3433

Fingerprint

Availability
Fault tree analysis
Railroads
Railroad engineering
Critical path analysis
Costs
Model checking
Stochastic models
Agglomeration
Semantics
Specifications
Industry

Keywords

  • EWI-24811
  • Dynamic Fault Trees
  • Cost
  • Recovery
  • METIS-305907
  • IR-91470
  • Availability
  • Maintenance
  • Reliability

Cite this

Guck, D., Katoen, J. P., Stoelinga, M. I. A., Luiten, T., & Romijn, J. (2014). Smart railroad maintenance engineering with stochastic model checking. In J. Pombo (Ed.), Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, Railways 2014 (pp. 299). (Civil-Comp Proceedings; Vol. 104). Stirlingshire, UK: Civil Comp Press. DOI: 10.4203/ccp.104.299

Guck, Dennis; Katoen, Joost P.; Stoelinga, Mariëlle Ida Antoinette; Luiten, Ted; Romijn, Judi / Smart railroad maintenance engineering with stochastic model checking.

Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, Railways 2014. ed. / J. Pombo. Stirlingshire, UK : Civil Comp Press, 2014. p. 299 (Civil-Comp Proceedings; Vol. 104).

Research output: Scientific - peer-reviewConference contribution

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Guck, D, Katoen, JP, Stoelinga, MIA, Luiten, T & Romijn, J 2014, Smart railroad maintenance engineering with stochastic model checking. in J Pombo (ed.), Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, Railways 2014. Civil-Comp Proceedings, vol. 104, Civil Comp Press, Stirlingshire, UK, pp. 299. DOI: 10.4203/ccp.104.299

Smart railroad maintenance engineering with stochastic model checking. / Guck, Dennis; Katoen, Joost P.; Stoelinga, Mariëlle Ida Antoinette; Luiten, Ted; Romijn, Judi.

Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, Railways 2014. ed. / J. Pombo. Stirlingshire, UK : Civil Comp Press, 2014. p. 299 (Civil-Comp Proceedings; Vol. 104).

Research output: Scientific - peer-reviewConference contribution

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AB - 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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Guck D, Katoen JP, Stoelinga MIA, Luiten T, Romijn J. Smart railroad maintenance engineering with stochastic model checking. In Pombo J, editor, Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, Railways 2014. Stirlingshire, UK: Civil Comp Press. 2014. p. 299. (Civil-Comp Proceedings). Available from, DOI: 10.4203/ccp.104.299