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Bayesian nonparametric reliability analysis for a railway system at component level

Conference Paper


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Abstract


  • Railway system is a typical large-scale complex system with interconnected sub-systems which contain numerous components. System reliability is retained through appropriate maintenance measures and cost-effective asset management requires accurate estimation of reliability at the lowest level. However, real-life reliability data at component level of a railway system is not always available in practice, let alone complete. The component lifetime distributions from the manufacturers are often obscured and complicated by the actual usage and working environments. Reliability analysis thus calls for a suitable methodology to estimate a component lifetime under the conditions of a lack of failure data and unknown and/or mixture lifetime distributions. This paper proposes a nonparametric Bayesian approach with a Dirichlet Process Mixture Model (DPMM) to facilitate reliability analysis in a railway system. Simulation results will be given to illustrate the effectiveness of the proposed approach in lifetime estimation.

Publication Date


  • 2013

Citation


  • Mokhtarian, P., Namazi-Rad, M., Ho, T. Kin. & Suesse, T. (2013). Bayesian nonparametric reliability analysis for a railway system at component level. IEEE International Conference on Intelligent Rail Transportation (ICIRT) (pp. 197-202). China: The Institute of Electrical and Electronics Engineers Inc.

Scopus Eid


  • 2-s2.0-84893218524

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2915&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1906

Start Page


  • 197

End Page


  • 202

Abstract


  • Railway system is a typical large-scale complex system with interconnected sub-systems which contain numerous components. System reliability is retained through appropriate maintenance measures and cost-effective asset management requires accurate estimation of reliability at the lowest level. However, real-life reliability data at component level of a railway system is not always available in practice, let alone complete. The component lifetime distributions from the manufacturers are often obscured and complicated by the actual usage and working environments. Reliability analysis thus calls for a suitable methodology to estimate a component lifetime under the conditions of a lack of failure data and unknown and/or mixture lifetime distributions. This paper proposes a nonparametric Bayesian approach with a Dirichlet Process Mixture Model (DPMM) to facilitate reliability analysis in a railway system. Simulation results will be given to illustrate the effectiveness of the proposed approach in lifetime estimation.

Publication Date


  • 2013

Citation


  • Mokhtarian, P., Namazi-Rad, M., Ho, T. Kin. & Suesse, T. (2013). Bayesian nonparametric reliability analysis for a railway system at component level. IEEE International Conference on Intelligent Rail Transportation (ICIRT) (pp. 197-202). China: The Institute of Electrical and Electronics Engineers Inc.

Scopus Eid


  • 2-s2.0-84893218524

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2915&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1906

Start Page


  • 197

End Page


  • 202