Predictive rail maintenance

Assessing the integrity and operation of rail networks using BMT DEEP.

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Key contact

Andy Brown - Offshore

Head of Business Development, North America

Andy Brown - Offshore

Head of Business Development, North America

Houston, United States

+1 281 200 3115 offshore@bmtglobal.com

South America

Joao Matheus de O. Arantes

Head of Business Development, South America

Joao Matheus de O. Arantes

Head of Business Development, South America

Brazil

+55 (12) 3937 7952 offshore@bmtglobal.com

Europe/Africa

Louise Ledgard

Director of Business Development

Louise Ledgard

Director of Business Development

Teddington, United Kingdom

+44 (0) 208 9435544 offshore@bmtglobal.com

Asia Pacific

Dale Hastings-Payne - Coastal Infrastructure

Head of Business Development, Asia Pacific

Dale Hastings-Payne - Coastal Infrastructure

Head of Business Development, Asia Pacific

Singapore

+65 6517 6800 coastal@bmtglobal.com

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.

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