Big Data boost to machine availability

This initial collaboration focuses on Thiess’ Mining haul trucks and excavators, and will help unify asset management and business operations. 

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. Working 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.

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 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.

For example, in one mining scenario, several haul trucks that move coal may be reported to need maintenance, while at the same time a substantial order of product is due for delivery in 11 days. The predictive machine management system will be able to look at a variety of options for addressing this problem, and provide a decision maker with a model-based prediction that if the trucks are loaded no more than 85% of normal capacity and driven at no more than 80% of normal speed, the failure probability over the next 11 days would be minimal. This allows companies to avoid badly-timed and costly downtime at the sacrifice of only a minor, temporary decrease in throughput.