Increasing uptime in mining loaders

Stream Analyze's AI solution significantly increased uptime for mining loaders. By accurately predicting equipment failures, the technology minimized unscheduled maintenance and ensured continuous operation, leading to a noticeable increase in productivity.


Mining loaders work a mile below ground, in a cramped, heavy-duty environment, with poor connectivity. Even though the mining loaders are robust and made for this kind day-to-day operations, they tend to break down with the extreme work setting. A broken down mining loader not only hampers overall operational efficiency, but it can also hinder other vehicles from passing, costing millions. Getting a technician a mile below ground to repair such a vehicle is both time-consuming and difficult.


Due to the poor connectivity in the mine, offloading vehicle data to a cloud or a server to run a predictive maintenance model is not feasible, so providing an onboard predictive model is essential for operations. Stream Analyze installed their platform directly on the wheel loader, providing the engineer with the ability to quickly see the data on the mining loader, develop a model based on several different sensors (sensor fusion), and deploy this to the wheel loader instantaneously. The model would predict 10-12 hours in advance when the gear box was about the break down.


The predictive maintenance model would now help the driver whenever the gear box was faulty, notifying him/her, allowing the loader to get above ground as soon as possible. Also, once connectivity was established, the central monitoring group would be notified as well, allowing them to deploy a technician from the service team, who would then meet up with the wheelloader, fix the problem and get the mining loader back into production as soon as possible, improving the mine’s overall operational capabilities.
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