IBM will supply Big Data to Thiess, global contract miners, to improve machine availability and operational productivity utilizing predictive analytics and modeling technologies.
This initial collaboration focuses on Thiess’ Mining haul trucks and excavators, and will help unify asset management and business operations, said IBM.
Unlike traditionally data-intensive industries such as banking or telecommunications, which rely on advanced information technology (IT) to drive operational performance, asset-intensive industries such as mining, have typically not invested as much operationally in IT systems.
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 optimization.
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.
“Association with IBM to build a platform that feeds the models with the data we collect and then presents decision support information to our team in the field will allow us increase machine reliability, lower energy costs and emissions, and improve the overall efficiency and effectiveness of our business,” said Michael Wright, executive general manager Australian Mining, Thiess.
Detection of anomaly and malfunction patterns can be used to predict the likelihood of component failures and other areas of risk. This will 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.
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.
IBM said predictive machine management differs from traditional machine management in several ways. First, it bases decisions about a machine’s maintenance and operation on the actual condition or health at that given time. Second, it has the ability to predict the health of a given machine far enough in the future to enable decision makers to execute correct actions such as adjusting production plans or ordering spare parts.