New Civil Engineer

Recently, I attended a workshop considering the path to Industry 4.0, organised by The British Institute of Non-Destructive Testing (BINDT) in collaboration with the UK Research Centre for Non-Destructive Evaluation (RCNDE), the UK Forum for Engineering Structural Integrity (FESI), the Alan Turing Institute and The Welding Institute. It was a very informative event that helped with the understanding of NDT 4.0 and highlighted the continuing need to use familiar terms and understandings for this emerging technology.

Not long after returning from the workshop I came across two interesting articles published in the 
New Civil Engineer relating to drone deployment and the use of the collected data to populate digital twins of the assets.

The first article covered EDF Energy’s use of an underwater remotely operated vehicle (ROV) for the first-ever inspection of a wind farm’s foundations at its Blyth offshore wind farm. The ROV, equipped and operated by researchers from the Offshore Robotics for Certification of Assets (ORCA) Hub, was modified to include cutting-edge robotic technologies for autonomous inspection capabilities and carried out visual inspections of the gravity-based foundations of three offshore turbines.

The inspections were undertaken to investigate a wide range of potential applications for drone technology to assess the condition of offshore wind turbines. These inspections demonstrated the drone’s ability to work autonomously at the site, as it recorded videos to assess the exterior condition of turbine foundations and cables.

The data collected was used to create a 3D reconstruction model of parts of the underwater assets, which will be used in the future to monitor biofouling, which is the accumulation of a range of microorganisms, plants and algae on the turbine foundations. The term for this use of data at the workshop was ‘digital twin’.

The second article related to National Grid Electricity Transmission (NGET) launching trials of a system that seeks to fully automate the capture and processing of corrosion-related condition assessment data. The system uses highly automated drones flown ‘beyond visual line of sight’ to gather detailed close-quarter data, which is processed using artificial 
intelligence.

NGET owns nearly 22,000 steel lattice pylons that carry overhead transmission conductor wires in England and Wales. Transmission pylons can deteriorate through corrosion, so periodic assessments are made to understand the health of the network. Currently, there are around 3650 steel lattice pylons inspected each year, capturing high-definition still colour images of steelwork using helicopters and manually flown drones.

Currently, the images from drones are captured and processed manually by inspectors, with drone pilots who transport drones to the site of each asset to be inspected and always keep them in sight while flying. This trial is expected to enable a fleet of connected and autonomous drones to be flown nationally under licence from the Civil Aviation Authority (CAA) under the supervision of operators in a remote operation centre.

Automating the data capture and processing for the assessments offers significant benefits, including: enabling the optimal capture of data for automated processing; increasing the speed, efficiency and consistency of the data processing; predicting the future state of a pylon and the requirement for and impact of maintenance work; and reducing both the risk and environmental impact of data capture.
The 12-month trial is the first of its kind by NGET and signals a commitment to innovating with technologies to promote safety, drive efficiency and lower environmental impact.

The partner Sees.ai has developed a semi-autonomous drone inspection system that enables large-scale drone operations to be piloted and managed from a central location. The technology removes the requirement for a skilled pilot to travel to the inspection site and, as a result, dramatically increases the scalability of drone operations while maintaining the highest standards of quality and safety. This technology is particularly well suited to detailed drone inspections and surveys of industrial assets and infrastructure, where it is necessary to get close to the asset in hazardous environments. The system simultaneously captures both 2D and 3D information (digital twin), dramatically improving the ability to automate analysis and reducing the time and cost of inspection and maintenance.

Both applications are using drones and digital twins to assess the condition of assets. The impact on the NDT technician has yet to be assessed and could go either way, with more drone inspections being carried out that highlight additional areas where an actual NDT test is required to assess the asset or fewer inspections are required as the digital twins identify the potential problematic area. One thing that digital twins may struggle with, though, is accidental damage due to its potential random nature.

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