One of the foundational goals driving the mining industry in Western Australia today is to be more efficient by reducing costs without compromising the health and safety of workers. One of the most common ways this is being achieved is through the introduction of innovative technological solutions which aim to provide a more efficient and safer operation environment. A leading example of this can be seen in the introduction of autonomous (driverless) trucks into operating mine sites across the Pilbara.
As with any new technology, considerable investment is made towards ensuring that it is being implemented in the most effective way possible, to yield the best possible outcome for the organisation. This usually involves a staged approach to implementation, coupled with detailed performance analysis and a model of continuous improvement.
Talis was engaged to provide these data analysis services, with a specifically spatial focus, for a project which was investigating the performance of autonomous (driverless) dump trucks within an active iron ore mine. The trucks themselves were fitted with dozens of on-board telemetry sensors to measure every aspect of its operational performance. The analysis of the data, coupled with high-precision GPS data, resulted in a spatial dataset which described numerous aspects of the truck’s behaviour during its operating cycle.
The study sought to assess the specific driving patterns that the autonomous trucks adopted, including driving paths along haul roads, adherence to safe driving conditions, reversing patterns and behaviours during loading and dumping, and in particular any instances of “queueing” or other non-productive occurrences along the route.
The data was then analysed across the various phases of haulage to determine key performance metrics throughout the cycle, and identify any significant differences in behaviour between autonomous trucks and those with drivers. This information then formed the basis of a series of recommendations to adjust certain aspects of the control algorithms used by the trucks, in order to improve the overall efficiency, and therefore profitability, of each truck in the fleet.