IBM, Theiss use big data to predict mining gear failure

By Dylan Bushell-Embling
Monday, 24 February, 2014

IBM, Theiss use big data to predict mining gear failure

IBM and Australian contract mining group Theiss have teamed up to use big data to improve mining operational productivity and machine availability.

The collaboration will aim to use predictive analytics and modelling technologies to improve efficiency and asset management, with an initial focus on Theiss's mining trucks and excavators.

Announcing the deal, the companies said many natural resource industries have been slow to invest in operational IT systems and are still relying too heavily on a fix-it-when-it-breaks or a time-based approach to machine maintenance. This can result in premature part replacements, unnecessary downtime and extra expense.

But the sector is starting to recognise how IT can help them with their core exploration and extraction operations.

IBM Research and Theiss have been focused on integrating machine sensor data, maintenance history and operational and environmental factors to optimise the maintenance process.

Using a technique the companies call predictive machine management, Theiss aims to be able to use even minor anomaly and malfunction patterns to predict the likelihood of a component failure.

The companies' comments support the recent assertion by Deloitte that the mining sector is taking the lead in terms of big data adoption in Australia.

Theiss is a wholly owned subsidiary of Leighton Holdings.

Image courtesy of Burt Kaufmann under CC

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