An autonomous system for maintenance scheduling data-rich complex infrastructure: fusing the railways’ condition, planning and cost
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Authors
Durazo-Cardenas, IsidroStarr, Andrew
Turner, Christopher J.
Tiwari, Ashutosh
Kirkwood, Leigh
Bevilacqua, Maurizio
Tsourdos, Antonios
Shehab, Essam
Baguley, Paul
Xu, Yuchun
Emmanouilidis, Christos
Affiliation
Cranfield UniversityIssue Date
2018-02-22
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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. Citation
Durazo-Cardenas I, Starr A, Turner CJ, Tiwari A, Kirkwood L, Bevilacqua M, Tsourdos A, Shehab E, Baguley P, Xu Y, Emmanouilidis C (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.Publisher
ElsevierAdditional Links
https://www.sciencedirect.com/science/article/pii/S0968090X18302055Type
ArticleLanguage
enISSN
0968-090XEISSN
0968-090XSponsors
EPSRC and Network Railae974a485f413a2113503eed53cd6c53
10.1016/j.trc.2018.02.010
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