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 idle time results in delays in mining operations.
  • Lack of meaningful data to identify excavators trends and inefficiencies caused by partial (non-optimized) filling and dumping processes.
  • No system in place for excavator monitoring system to track teeth and shroud conditions leading to performance degradation.
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Solution

  • We developed a computer vision-based desktop application integrated with a real-time equipment monitoring feature to analyze excavator mining footage.
  • A camera was mounted on the excavators arm to provide a clear view of the buckets operations.
  • The desktop application runs recorded videos from the camera and extracts critical operational data.
  • Key insights include pass and cycle counts, bucket and truck fill levels, idle times and other performance metrics by making it a comprehensive machine downtime tracking and machine downtime monitoring tool.
  • In addition, the system generates analytical graphs that enhance visibility into performance trends by making it an effective mining productivity software solution.

15%

Productivity

Implementation-Challenges

Implementation Challenges

  • Ensuring stable and clear video feed from the excavators arm in harsh mining conditions.
  • Handling varying light conditions, dust and obstructions in real-time video analytics.
  • Accurately syncing frame-by-frame video data with real-time operational timestamps.
  • Processing large video files efficiently to extract actionable insights.
  • Minimizing false positives in machine downtime monitoring and activity detection.
  • Integrating the system with existing mining operation workflows and reporting tools is important.
Key-Features

Key Features

  • Real-time equipment monitoring using mounted camera vision.
  • Excavator monitoring system with automated pass & cycle count detection.
  • Tracking machine downtime and idle periods with time-stamped insights.
  • Analysis of bucket & truck fill levels for productivity optimization.
  • Monitoring wear & tear on excavator teeth and shrouds.
  • Interactive dashboard with trend graphs and detailed analytics.
  • Offline desktop application with video input processing.
  • Compatible with harsh industrial environments in mining sites.
  • Data export feature for further integration with mining productivity software.

Occupancy management and beyond with a virtual replica

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