Metals & Mining

AI shovel bucket camera to analyse fragmentation and boost production

Key facts

over3%increase in excavator productivity
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Top business benefits

Increased ore production for lower costs

Improved excavator utilisation and lower downtime

Improved blasting performance

Better fragmentation

Better rock type information to the block model

Increased efficiency

Increased excavator part life

Lower maintenance costs

In more detail

Full description

The amount of material a shovel bucket can pick up is dictated by the size and granularity of the blasted rock it is moving. By monitoring the fragmentation of your excavator load, you can determine the optimum rock size for optimal excavator efficiency and adjust subsequent drill depths, burden and spacing, and explosive volumes accordingly.

This AI camera technology is retrofitted to the excavator to monitor the bucket. It continually records rock sizing in the shovel bucket. and details the fragmentation in every truck load. By optimising the fragmentation for the shovel bucket from the information generated, it can minimise damage to the bucket and teeth, cutting down on excavator maintenance costs, and also reducing the power use in the crushing and grinding circuit.

Working in conjunction with existing fleet management systems (FMS), the data the cameras collect can then be used to build a blasting efficiency map that enables mines to achieve a better balance between mining productivity and blasting costs. This results in a more efficient and productive fleet of excavators that require less maintenance.

The challenge

The efficiency of an excavator is very much reliant on the size of the fragmentation that it is working with. Uneven rock sizes or poorly blasted ground are difficult for shovel buckets to rip and can lead to bucket teeth being damaged or lost. This leads to costly repairs, lost production time and inefficient digging.

Determining the ideal fragmentation size for the excavator is difficult for the human eye to assess. A great deal of experimentation with different drill depths and explosive burdens and spacing needs to be undertaken before the ideal fragmentation balance for optimum efficiency and productivity can be determined. However different rock/ore types within a mine mean this can quickly change with depth or area leading to either constant readjustment or standardised blast patterns which produce suboptimal results.

The approach

This AI camera technology with night vision is retrofitted to monitoring excavator shovel buckets and teeth. It continually records and monitors rock sizing in the shovel bucket and details the fragmentation in every truck. By optimising the fragmentation for the shovel bucket, this device also minimises damage to the bucket and teeth, cutting down on maintenance costs.

Installed in less than a day, this standalone onboard solution can work without a network, is dust and water-resistant and works in temperatures from –30°C to 60°C.

If run in conjunction with existing Fleet Management Systems, the data the cameras collect can then be used to build a blasting efficiency map that allows the mine to achieve a better balance between mining productivity and blasting costs and automatically analyse current and planned blast performance.

This results in improved blasting performance and a more efficient and productive fleet of excavators that require less maintenance.

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