Scotland’s railway is under increasing pressure to deliver a more reliable, resilient and sustainable service. As traffic grows and assets continue to age, the challenge is no longer simply maintaining infrastructure, but understanding how it behaves in real time. That shift requires better visibility, giving operators and maintenance teams the ability to identify risks earlier, intervene before failures occur, and make more informed decisions. Transmission Dynamics’ collaboration with Network Rail Scotland is demonstrating what this looks like in practice.
At the centre of this approach is PANDAS-V®, an AI-powered pantograph and overhead line monitoring system that delivers continuous, in-service insight. Deployed on Network Rail Scotland's Class 334 fleet in November 2025, four systems began monitoring the network during normal operations. Within just three months, they identified 104 detached droppers, logged 195 impact locations, and flagged three critical faults. This is not simply data collection; it is actionable intelligence. By working together to interpret and act on these insights, teams were able to intervene earlier, reducing the risk of escalation and avoiding unnecessary disruption to services. Alan Anderson, Infrastructure Maintenance Engineer at Network Rail, highlighted the success of this instalment: “The results are impressive. After running a Scottish line of route through the new algorithm, Transmission Dynamics identified dozens of faults.”
The value of this capability becomes even clearer in response to complex or previously hard-to-detect issues. Following an incident at Glasgow Central Station that resulted in pantograph and infrastructure damage, Transmission Dynamics and Network Rail collaborated to address the challenge of detecting misaligned converging wires. Traditional inspection methods can struggle to identify these faults, particularly at low speeds or in subtle configurations.
In response, a new computer vision approach was developed using PANDAS-V® data. Combining high-quality video from the PANDAS-V® camera system with intelligent analytics, this method uses pantograph tilt as an indicator of wire misalignment. When a low-set converging wire contacts the pantograph horn or carbon strip, a lateral and vertical force is induced as the converging wire climbs on the aluminium horn, resulting in a measurable tilt of the pantograph head.
Beyond individual use cases, this collaboration is helping to establish a more informed and responsive approach to infrastructure management. Continuous monitoring provides a clearer picture of asset condition across the network, allowing maintenance teams to prioritise effectively and respond with greater confidence.
By working closely with Network Rail Scotland, Transmission Dynamics is helping to deliver the visibility needed to better understand, manage and maintain OLE assets. This access to real-time, high-quality insight supports faster interventions today, while enabling more efficient, data-driven maintenance strategies for the future.
Image credit: iStock
