Melbourne rail

The rising prominence of digital twins in infrastructure asset management and maintenance is promising to revolutionise the way railway organisations approach the monitoring and upkeep of critical infrastructure.

While the fever around digital twins continues unabated, the challenges in adapting digital twin technology in day-to-day routine maintenance and inspections cannot be overestimated. With careful planning and focus, these challenges can be overcome.

The problems begin with the basic fact that a digital twin is just one representation of a railway network. 

As anyone who has ever worked in a large railway organisation would know, no corporate system can ever service the information requirements of every silo that exists. It is for good reason that silos develop, because managing complex assets within a particular engineering discipline needs specialist teams and budgets. 

These teams tend to develop their own information datasets in order to successfully operate and maintain their assets. It is for this reason that we, for example, see different linear referencing systems coexist within one railway network, and why detailed information on assets is often held and maintained by local teams outside of the enterprise asset management system. This poses immense challenges in relating the digital twin to the actual information that the business uses day-to-day.

The key to successful adoption of digital twin technology in day-to-day routine maintenance and inspections is the early and continued engagement of subject matter experts from different engineering disciplines. This will ensure that the information datasets that they work with are properly disclosed to the project and shared with the vendor as early as possible. This will help establish how unstructured the information datasets are and what treatments will be needed to bring these back to a common linear reference. 

Using AI algorithms and specialist workflows that integrate rail domain knowledge with intelligent data processing, unstructured datasets can be quickly translated into powerful reference datasets that can drive the automation of routine maintenance measurements and inspections. 

Taking such an approach can bring forward the benefits in adapting digital twin technology to routine maintenance and inspections which include having teams spend less time doing manual inspections in the danger zone and more time repairing defects. This improves the safety profile of routine maintenance while effecting an immediate reduction in safeworking costs. 

This sponsored editorial is brought to you by Agonics. Agonics will be exhibiting at CORE 2023 in Melbourne from June 19–21. For more information, visit

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