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

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dc.contributor.authorDurazo-Cardenas, Isidro
dc.contributor.authorStarr, Andrew
dc.contributor.authorTurner, Christopher J.
dc.contributor.authorTiwari, Ashutosh
dc.contributor.authorKirkwood, Leigh
dc.contributor.authorBevilacqua, Maurizio
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorShehab, Essam
dc.contributor.authorBaguley, Paul
dc.contributor.authorXu, Yuchun
dc.date.accessioned2018-02-26T11:22:47Z
dc.date.available2018-02-26T11:22:47Z
dc.date.issued2018-02-22
dc.description.abstractNational 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.identifier.citationDurazo-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. 234-253en_UK
dc.identifier.cris19629775
dc.identifier.issn0968-090X
dc.identifier.urihttp://dx.doi.org/10.1016/j.trc.2018.02.010
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13033
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectData-driven asset management of rail infrastructureen_UK
dc.subjectSystems integrationen_UK
dc.subjectCondition-based maintenanceen_UK
dc.subjectIntelligent maintenanceen_UK
dc.subjectData fusionen_UK
dc.subjectCost engineeringen_UK
dc.subjectPlanning and schedulingen_UK
dc.subjectSystems design and implementationen_UK
dc.titleAn autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways' condition, planning and costen_UK
dc.typeArticleen_UK

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