IBM and Thiess collaborate on predictive analytics and modeling technologies

23 February 2014 (Last Updated February 23rd, 2014 18:30)

IBM has collaborated with Thiess to use Big Data to improve machine availability and operational productivity, by using predictive analytics and modeling technologies.

IBM has collaborated with Thiess to use Big Data to improve machine availability and operational productivity, by using predictive analytics and modeling technologies.

The initial collaboration, which will unite asset management and business operations, will focus on Thiess' Mining haul trucks and excavators.

In addition to maintenance and repair, operational, and environmental data, the IBM research and Thiess collaboration has been integrating current and historical machine sensor 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 will be used to construct models to assess and predict the life of discrete components and overall health of a piece of equipment.

This information will help decision-makers to co-optimise maintenance and production decisions, to improve operational performance.

Early detection of minor anomaly and malfunction patterns can be used to predict the likelihood of component failures and other areas of risk.

This will help in increasing the uptime of and improve firm's ability to manage the full life of discrete components, overall machine health and deployment of limited maintenance resources.

Thiess Australian mining executive general manager Michael Wright said the analytics and modeling can offer great opportunities to improve business of the company.

"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 to increase machine reliability, lower energy costs and emissions, and improve the overall efficiency and effectiveness of our business," Wright said.

Predictive machine management bases decisions about a machine's maintenance and operation on the actual condition or health at that given time.

It also has the ability to predict the health of a given machine in the future, which will enable decision-makers to execute correct actions such as adjusting production plans or ordering spare parts.

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