Case Study: Real-time conveyor and material monitoring. DataMind AI uncovers crusher liner issues and prevents downstream equipment failures.
Crusher liners are essential to the effective operation of any crusher. Wear of the crusher liners and inaccuracies in the crusher gap calibration can result in oversized ore going unnoticed, potentially causing harm to downstream equipment, like the grinding circuit.
Early detection of these potential issues allows for the implementation of preventive strategies, safeguarding against production interruptions, equipment damage, costly repairs, and safety hazards.
DataMind AI was implemented on the crusher discharge conveyors to monitor ore size, consistency, and belt alignment and wear. Utilizing the established crusher gap and collected data, thresholds were set to trigger alerts.
In this scenario, sophisticated real-time computer vision analysis of ore size, as recorded by camera footage, detected sizes that consistently surpassed the expected 140mm crusher gap.
Analysis over a 9-hour duration revealed materials reaching up to 280mm. Such deviations could lead to significant damage and operational downtime for the downstream mill and potentially hazardous crusher blockages, among other issues.
DataMind AI provided compelling evidence of excessive ore size. Recalibration of the crusher gap, and the immediate return to ore-size specification, prevented equipment downtime and maintenance costs.
