Here’s a sample use case for how companies are harnessing our unique software and creating machine learning models that help them conduct faster, cheaper and safer asset inspections. Check out the cost comparisons table below to discover how you can save literally millions on asset inspections.
Use Case #1:
Sector: A power company
Asset: Power poles
With a vast network of power poles to inspect, how do they do it quickly, accurately, cost effectively without missing a defect?
- The power company need to take photographs of 400,000 power poles to complete one full inspection.
- They take approx. 5 photos per pole (that’s 2 million photos per inspection cycle)
- Those images get uploaded to a system where a team of expert assessors analyse each one.
- When they spot a defect, it gets flagged for repair.
It can take up to 9 months to capture and review all those images. That’s a long time.
Can machine learning be used in asset management to speed up the assessment of vast networks like this and do it for a fraction of the current cost?
Yes. How? Take a look.
Here’s a cost comparison of how the image capture and assessment process is currently being done (the current way) and how it could be done (the Machine Dreams way):