The KAI platform is being used at National Grid to support analysts to rapidly identify the objects of interest in recorded footage. Using deep learning, KAI is able to identify the key components automatically and present these to the operator for review.
National Grid were able to achieve an over 60% reduction in the time taken to review over head line footage for condition assessment. As part of a follow on project the input from the users assessment is being used to train a machine learning algorithm to allow the model to automatically identify defects.
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The Problem Statement
- National Grid captures video footage of Overhead Lines to identify any potential faults with towers, fittings or conductors.
- Analysts spend thousands of hours reviewing footage looking for problems and reporting on condition.
- The process is laborious, has significant bottlenecks and quality is dependant on the engineer reviewing the footage.
Successful Outcomes
- OPEX Saving: Reduced the time taken to process footage by 66%.
- Self Sufficiency: Analysts are able to train AI models to identify and extract relevant components (Spacers, Joins, Dampers, Insulators).
- Risk Reduction: Reduced the risk of asset failure by removing the backlog of footage to be processed.
- Improved Asset Management: Allows the cataloguing of historic footage to support and maintain asset inventory.
- Rapid ROI: Entire capability deployed (with models and user training) in two months.
It took just one meeting! Keen AI went away and built a working prototype. We’ve spent months with some external suppliers and not got anywhere.
Mark Simmons OHL Condition Monitoring Team Leader National Grid ETO
