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 where static schedules and depth of maintenance regimes are replaced with dynamic processes.