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WWT's Tim Brooks comments on the mining industry's use of data to anticipate when a massive piece of equipment might fail while in rough terrain.

Aug 10, 2017 2 minute read

Posted by TechTarget on August 10, 2017:

It's hard to imagine a mining company being digitally innovative, but that was the case with one such firm that discovered it could use data to anticipate when a massive piece of equipment might fail while in rough terrain at a remote location.

The company liked the idea of proactively performing maintenance on equipment before something went wrong, and wanted to take advantage of data being captured on thousands of sensors on hundreds of pieces of equipment at multiple mining sites, said Tim Brooks, big data principal consultant at World Wide Technology (WWT), which worked with mining company officials to set up a predictive analytics system to utilize the data the sensors were capturing. The sensors simultaneously gather data every few seconds and OSIsoft's PI System, a recording software product, logs the data, Brooks said.

The mining industry "is not exactly Amazon.com in terms of the cultural setting of most of the folks that have worked their way up from operating heavy equipment or running a mine to becoming senior executives of a company," Brooks observed. "So gathering that group to discuss how they use data and how they may derive value from it was a formidable challenge."

WWT examined and analyzed data that was gathered for 16 weeks in a Hadoop environment in its internal big data lab. Officials identified three instances that correlated with future engine failure with 90% probability on the heavy equipment, Brooks said.

This was a significant finding, he noted, because mines tend to be located in harsh, remote environments, and when a truck's transmission fails or an engine goes down, "it effectively shuts the mine down, thereby ruining productivity for a day, at a tune of one million dollars," he said. "If WWT can help its customer identify in advance which trucks are most likely to fail in 30 to 60 days, they can be taken out and proactively fixed."

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