UK Launches AI-Powered Drone Asset Inspection Programme

The UK’s National Grid Electricity Transmission (NGET) is launching trials of a system that seeks to fully automate the capture and processing of corrosion-related condition assessment data.

Formed through a collaboration with deep tech start-ups Keen AI and sees.ai, the system uses highly automated drones flown ‘Beyond Visual Line of Sight’ to gather detailed close-quarter data, which is then processed using artificial intelligence.

NGET owns 21,900 steel lattice pylons that carry overhead transmission conductor wires in England and Wales. Transmission pylon steelwork condition can deteriorate through corrosion, so periodic assessments are made to understand the health of the network. NGET inspects around 3,650 steel lattice pylons each year, capturing high definition still colour images of steelwork using helicopters and manually flown drones.

Currently these images from drones are captured and processed manually by a pool of inspectors with drone pilots carrying drones to the site of each asset due to be inspected and then keeping them in sight at all times while flying. This trial will enable a fleet of connected and autonomous drones to be flown nationally under licence from the Civil Aviation Authority and under the supervision of remote operators in a secure Remote Operation Centre.

Automating data capture and processing for these assessments offers significant benefits, including:

  • Enabling the capture of data that’s optimal for automated processing
  • Increasing the speed, efficiency and consistency of data processing
  • Predicting the future state of a pylon and the impact of any maintenance work
  • Reducing 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.

Mark Simmons, Conditioning Monitoring Manager at National Grid Electricity Transmission, and pioneer of the trial within National Grid, said:

“Maintaining and investing in our transmission infrastructure is critical to a safe and reliable electricity network. Working with innovators like Keen AI and sees.ai we are able to take real time data and use it to predict when assets on our network need attention. This technology will be vital in the future as we connect more and more renewable and low carbon power, expanding our network and delivering world class reliability. We look forward to the technology complementing the methods we currently use to help our operational teams manage safety, inspections and maintenance.”

Amjad Karim, CEO, Keen AI said:

“We’ve been hearing for many years now how AI will automate and transform asset management; ultimately leading to reduced asset life cycle costs and a greener, more reliable network. I’m glad that together, we’re making it actually happen.

“National Grid has been conducting the end-to-end asset management process for steel lattice towers, from data collection to execution, for many years now and are seen by many as the leaders in this field.  I’m really excited to be using sees.ai’s autonomous drone tech to collect data which hands off to our systems to automatically assess corrosion and propose maintenance or replacement work.”

John McKenna, CEO, sees.ai said:

“National Grid’s requirement to carry out detailed close-quarter inspection of steelwork and components on the transmission network is a perfect fit for our technology. Equally exciting, their Condition Monitoring team has decades of experience of how this work has been carried out in the past – ranging from data capture using helicopters and manually flown drones, to data processing using humans and machines (working with Keen AI since 2018), to managing the resulting maintenance & repair work. It’s the ideal foundation on which to build. We can’t wait to get started on this project.”

Source: Press Release

 

This story republished from https://www.uasvision.com/2022/05/27/uk-launches-ai-powered-drone-asset-inspection-programme/