An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways ’ condition, planning and cost

Durazo-Cardenas, Isidro, Starr, Andrew, Turner, Christopher J, Tiwari, Ashutosh, Kirkwood, Leigh, Bevilacqua, Maurizio, Tsourdos, Antonios, Shehab, Essam, Baguley, Paul, Xu, Yuchun et al and Emmanouilidis, Christos (2018) An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways ’ condition, planning and cost Transportation Research Part C: Emerging Technologies, 89. pp. 234-253.

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Abstract

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel in- tegrated system for automatic job scheduling is presented; from concept formulation to the ex- amination of the data to information transitional level interface, and at the decision making level. The underlying architecture con fi gures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise de- gradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum sche- duling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account speci fi c cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The de- monstrator structure was found fi t for purpose with logical component relationships, o ff ering further scope for research and commercial exploitation

Item Type:	Article
Divisions :	Faculty of Arts and Social Sciences > Surrey Business School
Authors :	Name Email ORCID Durazo-Cardenas, Isidro Starr, Andrew Turner, Christopher J christopher.turner@surrey.ac.uk Tiwari, Ashutosh Kirkwood, Leigh Bevilacqua, Maurizio Tsourdos, Antonios Shehab, Essam Baguley, Paul Xu, Yuchun Emmanouilidis, Christos
Date :	22 February 2018
Funders :	EPSRC
DOI :	10.1016/j.trc.2018.02.010
Copyright Disclaimer :	Crown Copyright © 2018 Published by Elsevier Ltd. This is an Open Access article unde the CC-BY license
Depositing User :	Melanie Hughes
Date Deposited :	19 Jul 2018 12:01
Last Modified :	11 Dec 2018 11:24
URI:	http://epubs.surrey.ac.uk/id/eprint/848755

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