AI Case Study
Roy Hill iron ore project is developing a transportation vehicle maintenance prediction and prevention recommendation platform
Roy Hill iron ore mine project is working with researchers to develop a predictive tool using Microsoft Azure for maintenance requirements and recommendations for avoidance in real time for its transportation vehicles (trains and trucks).
Mining And Metals
"Roy Hill recently commissioned Ajilon for a business analytics platform, which runs on Microsoft Azure. Using Microsoft tools, the platform ingests large volumes of data in real-time, enabling the mine’s data scientists and engineers to soon self-serve visualisation tools, develop predictive algorithms, and combine disparate information sources. Roy Hill wanted to go beyond using Azure as a static analysis environment... The mine has become something of a test bed for how far technology can be exploited to optimise operations. Analytics proof of concepts, in and out of Azure, are taking shape. IoT sensors run from pit to port. Take the mine’s giant pink trucks. Although not self-driving (the technology simply wasn’t ready at the time of putting together the mine’s business case Kerr says), they are being closely monitored and analysed... sensor and GPS data, combined with external data like weather reports are being analysed. Soon, reports – timely enough to be up to date for when each shift begins – will help Roy Hill better educate drivers on how their behaviour can change to reduce say tyre wear or fuel consumption. The rail network too, will use sensor data and analysis to optimise the running of the network and predictive maintenance."
Azure is “not quite there yet" in terms of its planned predictive functionality.
"Roy Hill’s main goal is to be cost competitive, achieved by 'driving efficiencies and leading industry best practice'. As the mine dials up its output, its team is increasingly seeking to identify and solve and pinch points through data analytics. Data is drawn from every point of the operation, from processing plant to port. Bringing it together to make sense of it all is difficult."
A variety of data sources including GPS, transport vehicle sensors, weather information.