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

Research Output: Contribution to journal Article Peer-review

Open access

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 examinationofthedatatoinformationtransitional levelinterface,andatthedecisionmakinglevel. 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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 234-253

Journal (Volume, Issue Number)

Transportation Research Part C: Emerging Technologies (Volume 89)

Publication milestones

  • Accepted/In press - 15/02/2018
  • Published - 22/02/2018

Publication status

Published - 22/02/2018

ISSN

0968-090X

External Publication IDs

  • handle.net: 10547/623952
  • Scopus: 85044640421

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