Haul trucks form a critical component in a mine-site’s operations, moving large volumes of resources from mine to crusher. As such it is critical to plan for necessary maintenance as unscheduled failures have significant cost implications from lost production, under-utilized staff, as well as heightened safety risks for personnel.
By using VROC AI to monitor the expected load distribution among tyres and the individual responses, it is possible to identify small degradations in tyre performance and load balance when tyre degradation events such as leaks / cuts impact performance.
Prior to the incident the VROC AI dashboard is showing green for the front and rear tkph indicating “Normal Operation”. Shortly after that there is a period of “Moderate Degradation” which progresses to “Severe Degradation”. It is possible to see that the tpkh distribution between the front and rear tyres is incorrect which indicates an issue with the tyres.
After noticing the degradation of the tyres, the truck was sent for repairs where maintenance personnel found a cut to the tyre sidewall. The workshop was able to repair the tyre before the damage became catastrophic.
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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