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

Show simple item record Durazo-Cardenas, Isidro Starr, Andrew Turner, Christopher J. Tiwari, Ashutosh Kirkwood, Leigh Bevilacqua, Maurizio Tsourdos, Antonios Shehab, Essam Baguley, Paul Xu, Yuchun 2018-02-26T11:22:47Z 2018-02-26T11:22:47Z 2018-02-22
dc.identifier.citation Durazo-Cardenas I, Starr A, Turner CJ, et al., An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost. Transportation Research Part C: Emerging Technologies, Volume 89, April 2018, pp. 234-253 en_UK
dc.identifier.issn 0968-090X
dc.description.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 integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures 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 degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation. en_UK
dc.language.iso en en_UK
dc.publisher Elsevier en_UK
dc.rights Attribution 4.0 International *
dc.rights.uri *
dc.subject Data-driven asset management of rail infrastructure en_UK
dc.subject Systems integration en_UK
dc.subject Condition-based maintenance en_UK
dc.subject Intelligent maintenance en_UK
dc.subject Data fusion en_UK
dc.subject Cost engineering en_UK
dc.subject Planning and scheduling en_UK
dc.subject Systems design and implementation en_UK
dc.title An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways' condition, planning and cost en_UK
dc.type Article en_UK
dc.identifier.cris 19629775

Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International Except where otherwise noted, this item's license is described as Attribution 4.0 International

Search CERES


My Account