A methodology for railway track maintenance modelling using Plausible Petri nets

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

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• Presentation Conference Type: Conference Paper (unpublished)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