Excavator Machine Efficiency at Mining Site

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Customer

Global manufacturer of mining equipment and wear-resistant products in Australia.

Project Details

  • Duration: 4 Months

Technologies:

  • Deep learning
  • PyQt
  • MongoDb
  • OpenCV
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Problem

  • The inability to monitor machine idle time results in delays in mining operations
  • Lack of meaningful data to identify excavator’s trends and inefficiencies caused by to Partial (non-optimised) filling and dumping process.
  • Get Insights on the conditions of the Teeth and Shrouds.
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Solution

  • We developed a computer vision-based desktop application to analyze excavator’s mining footage.
  • The camera was mounted on the excavator’s arm to clear the vision of the bucket.
  • The Desktop application run the recorded videos from the camera.
  • From the video, we calculate the critical insights like pass & cycle counts, bucket & truck fill levels, idle times, and other key metrics.
  • Along with Insights, we generate the graphs for better understanding of the trends and analytics.

15%

Productivity

Occupancy management and beyond with a virtual replica

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