Predictive rail maintenance

Predictive rail maintenance

Optimising condition-based maintenance for rail networks

Rail network system operators maintain electromechanical systems based on scheduled and reactive maintenance. The result of this means that a lot of valuable asset data is not being used to make decisions about their assets.

A metro operator approached BMT to explore opportunities to make use of this data to optimise their maintenance regime. BMT collaborated with the operator to provide a mechanism to achieve data interconnection to different data sources using BMT DEEP.

BMT DEEP as a digital, fully connected smart data gathering, analysis and diagnostics platform, has been proven to optimise operations, maintenance and asset management and to extend asset life. BMT subject matter experts and data analysts worked with the metro operator to develop rules and algorithms to predict failures and trigger maintenance actions.

Using BMT DEEP vast amounts of historical and real time data were collected and analysed to assist the operator visualise all the target electromechanical systems within its rail network. This helped to estimate the remaining useful life of the asset, indicate asset health status and produce a maintenance regime that is continually updated and optimised as the system is fed more data.

By using BMT DEEP, the metro operator was able to allow for a considerable reduction in maintenance man-hours and an increase in overall system availability.