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Artificial Intelligence
Drill Bit Condition Monitoring
Problem:
Drill bits in mining operations often fail without warning, leading to unplanned downtime that disrupts productivity and increases maintenance costs. Additionally, worn drill bits put unnecessary strain on equipment, reducing overall efficiency and increasing the risk of further mechanical failures.
Solution:
Leverage vision AI to monitor the condition of drill bits in real-time by analyzing wear patterns and tracking the number of holes drilled. Provide early detection of potential failures, enabling proactive maintenance and reducing unexpected downtime. This ensures optimal drill performance, minimizes strain on equipment, and delivers a 10–15% increase in productivity through fewer interruptions and streamlined operations.
Excavation Cycle Monitoring
Problem:
Inefficiencies in excavation operations, such as excessive idle time, unbalanced cycle phases, and suboptimal operator performance, lead to delays and increased operational costs. Overuse of critical components like buckets, hydraulics, and swing motors accelerates wear, resulting in higher maintenance expenses and unplanned downtime. Poor alignment with haul truck schedules causes waiting times, further impacting productivity.
Solution:
Utilize vision AI to monitor and analyze each phase of the excavator cycle in real time. By tracking key metrics—including digging, swinging, dumping, returning, and idle times—vision AI identifies inefficiencies, bottlenecks, and delays. Computer vision provides actionable insights to optimize cycle performance, reduce idle time, and enhance operator efficiency. Proactive alerts prevent overuse of equipment, reducing maintenance costs and driving a 10–15% improvement in cycle efficiency while achieving a 20–30% reduction in idle time.
Detect Mis-Loading of Haul Trucks
Problem:
Over-filled haul trucks are causing issues for other vehicles by shedding material onto the haul road, while the excess weight also accelerates wear and tear on the truck's transmission and brake systems.
Solution:
Utilize Vision AI to monitor truckload capacity, alerting management of instances of overfilling or underfilling. Identify potential training issues with shovel or loader operators promptly, enabling immediate corrective action to ensure proper loading of haul trucks going forward.
Conveyor Belt Monitoring
Problem:
Conveyor belts play a crucial role in processing metals and materials, but they are susceptible to mechanical failures that can lead to inefficiency or complete breakdowns. Early detection of issues minimizes these consequences and reduces downtime for repairs. For instance, a tear in a belt can result in material spillage, causing blockages in the drive mechanism and accumulation beneath the conveyor.
Solution:
A strategically positioned single camera can efficiently monitor multiple common failure points on a conveyor system, ensuring continuous material flow and detecting spills, tears, obstruction from debris or large rocks, or other issues. Integration with an infrared camera and noise and vibration monitors provides early alerts for mechanical failures like worn-out bearings or belt slackness.