The initial collaboration focuses on Thiess’ Mining haul trucks and excavators, and will help unify asset management and business operations.
Matthew Denesuk, Manager of Smarter Planet Analytics, IBM Research, said the IBM Research and Thiess collaboration has been integrating current and historical machine sensor data, along with maintenance and repair, operational, and environmental data to use as a basis for data-driven operational optimisation. “Factors such as repair and inspection history, payload size, sensor based component alerts, operator variability, weather, and ground conditions are being used to construct models which assess and predict the life of discrete components and the overall health of a piece of equipment. This information will enable decision makers to co-optimise maintenance and production decisions, resulting in better operational performance.”
“Analytics and modeling can offer great opportunities to improve our business, but we need to integrate them with our current processes in order to have a real bottom-line impact,” said Michael Wright, Executive General Manager Australian Mining, Thiess.
According to Denesuk, early detection of even minor anomaly and malfunction patterns can be used to predict the likelihood of component failures and other areas of risk. “This will dramatically increase the uptime of the equipment and improve Thiess’ ability to manage the full life of discrete components, overall machine health and the deployment of limited maintenance resources.
“Natural resource industries are facing a perfect storm of demand growth, scarcity and rising costs that threaten their ability to deliver materials, fuel, and food to the world.
“By combining knowledge of the physical health of the equipment with information about how it needs to be used, we are able to know when something is going to go wrong and what can be done to fix the root problem before that occurs.
“Developing a unified predictive equipment and operational management system requires finding common connection between physical and computer scientists, who often operate with different skill sets and goals. The models used in this project bring together the physical and digital worlds by supplementing data-driven modeling that computer scientists tend to employ with information from engineers who have first-hand expertise about the mechanics of the equipment.”
Denesuk said that, today, many organisations in natural resource industries rely heavily on either a ‘fix-it-when it-breaks’ approach or time-based scheduled maintenance techniques. “These methods often result in unnecessary downtime, premature component replacements, extra expense and lost production. They also do not explicitly factor in an individual piece of equipment’s actual condition and performance capability.
“However, this trend is changing, as companies realise how IT can help them extract liquid, rocks and insights out of the ground. The increased deployment of machine and environmental sensors combined with new data collection methods is enabling the development of predictive machine maintenance analytics which can help increase equipment availability, lower production costs and provide greater operational flexibility.”