Innovation is fundamental to our continual development of new and improved systems. Keeping up to date with global scientific and technological developments is vital, so we relish the opportunity to take part in research projects with a variety of academic and industrial partners, in order to inform future developments.
The Station of the Future
telent are a partner in a major new research project into “The Station of the Future.” We are working with Workware Systems Ltd and Abellio Transport Holdings Ltd on the project, which is to be led by CISCO Systems Ltd. The two-year project is co-funded by the Technology Strategy Board (TSB).
Stations as a Service (StaaS) will create a new management and commercial model for future stations, integrating communications and mobile subsystems onto a single, IP-based network. The project will involve a demonstration at a station to show technical feasibility, as well as a wide range of associated benefits.
StaaS will bring together the connectivity demands of passengers, retailers, train operators and security providers. By converging various systems and technologies such as Building Management, Internet of Things and Big Data, StaaS will deliver benefits to all of these groups. This will enable operators to move towards a holistic architecture with centralised management, helping to unlock future investment and innovation in the UK Rail Sector.
Health and Prognostic Assessment of Railway Assets for Predictive Maintenance
We are one of the members of a consortium which has won funding from the “Enabling the Digital Rail” competition – a research programme funded jointly by the UK Technology Strategy Board and the RSSB.
We will be working with London Underground, Humaware, the University of Nottingham and the University of Loughborough on the project, focussing on the “Health and Prognostic Assessment of Railway Assets for Predictive Maintenance”.
The objective of this project is to use Remote Condition Monitoring (RCM) data to provide a reliable and dependable health assessment of the asset, to manage asset degradation and undertake maintenance intervention at the optimum time, in advance of failure. The project will provide an open architecture system that integrates data from a number of RCM sources. Condition Indicators will be derived from the RCM data based on detection of incipient defects and trends to develop an automated approach to introducing prognostics assessment via a risk-based Remaining Useful Life (RUL).
This approach will significantly improve on current state detection methods which are based on simple thresholds. The technology developed will assess the RUL via a dynamic scheduler to determine the optimum maintenance period in order to minimise the risk of failure to the asset and maximise its availability.
The project deliverable is to provide the end user with advisories (actionable information) relevant to their needs. This will ensure that ‘information overload’ is minimised and addresses security of information issues by only displaying information relevant to the rank and role of the user.
The project will also address the process re-engineering and human factor issues resulting from the paradigm shift of moving from a schedule and demand based maintenance management regime to a condition based forecasting approach; static schedules and depth of maintenance regimes will be replaced with dynamic processes.
It is anticipated that the end product will help London Underground in delivering continued reduction in Lost Customer Hours and will provide export opportunities for similar savings with Global mass transit and rail operators.