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A methodology for railway track maintenance modelling using Plausible Petri nets

Chiachio, Manuel; Chiachio, Juan; Prescott, Darren; Andrews, John

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Authors

Manuel Chiachio

Juan Chiachio

JOHN ANDREWS john.andrews@nottingham.ac.uk
Professor of Infrastructure Asset Management



Abstract

This paper proposes a new mathematical methodology to model expert systems with the ability to sequentially learn from data. To this end, the Plausible Petri nets (PPNs) methodology, first developed in M. Chiachío et al. [Proceedings of the Future Technologies Conference, San Francisco, (2016), pp. 165-172] is used due to their ability to integrate continuous and discrete dynamics in a single net model, which allows us to analyse hybrid systems with interaction of diverse sources of information, like in expert systems. The efficiency of the proposed approach is demonstrated in an expert system model for railway track inspection management taken as case study using published data from a laboratory simulation of train loading on ballast, carried out at the Nottingham Railway Test Facility, University of Nottingham.

Citation

Chiachio, M., Chiachio, J., Prescott, D., & Andrews, J. (2018, September). A methodology for railway track maintenance modelling using Plausible Petri nets. Paper presented at Probabilistic Safety Assessment and Management PSAM 14

Presentation Conference Type Conference Paper (unpublished)
Conference Name Probabilistic Safety Assessment and Management PSAM 14
Start Date Sep 16, 2018
End Date Sep 21, 2018
Acceptance Date Jul 30, 2018
Publication Date Sep 16, 2018
Deposit Date Sep 18, 2018
Publicly Available Date Sep 18, 2018
Public URL https://nottingham-repository.worktribe.com/output/1081179
Contract Date Sep 18, 2018

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