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Predictive Rail Asset Management Pilot

The pilot will be delivered to overcome the following major barriers to moving to a predict-and-prevent rail maintenance approach. Rail infrastructures involve a complex supply chain between equipment manufacturers, maintainers and operators. Acquiring long term, performance (maintenance, fault) data, understanding the quality, accuracy, and provenance of the largely unstructured data, processing it to identify emerging faults (diagnostics) and disseminating useful, timely prognosis information with known confidence levels for preventive and predictive maintenance has proved historically problematic.



This pilot will apply Big Data predictive and prescriptive analytics to a UK national rail route, to reduce the long-term cost of maintenance and increase network availability through the facilitation of focused short and medium term proactive interventions. The Pilot will be run on historical and real-time data sources.

The application of new Big Data technology and processes to facilitate the introduction of a step change in maintenance across European rail infrastructures to:

  • Verify the quality, accuracy and provenance of asset data, leading to confidence to
  • Provide timely focused prioritised maintenance activities (predict and prevent), leading to
  • Improved reliability and availability of track-side assets, with
  • Higher availability of rail infrastructure for passenger and freight services; and
  • Enhancing worker safety through minimising track-side activities