Predict to prevent: Transforming mining with machine learning

Mining companies have a lot of data at their disposal. Sensors are seemingly everywhere in their underground operations. But thus far it has been very hard for mining companies to capitalize on all their data because of the difficulty in making sense of it all.

So what’s the most important data for mining companies? The short answer: assets. Mining is one of the most asset-intensive businesses there is. At every point in the extraction chain— drilling, cutting, crushing, screening and removing ore-bearing rock—heavy equipment is critical. And it takes a beating. When equipment breaks down, requiring unscheduled maintenance, production takes a hit, costs rise and a critical measure of capital efficiency in mining—overall equipment effectiveness (OEE)—goes down.

IBM uses machine learning algorithms to analyze the equipment sensor data at a component level. The idea is as basic as it is powerful: if you analyze a large enough dataset on the maintenance and failure pattern for a particular component, you’ll be able to make an accurate prediction of when that component—say, part of an engine, a transmission or brakes—is likely to fail. The central insight these models produce—a prediction of each component’s lifetime—is tremendously powerful because it gives operators the critical element they need to optimize scheduled maintenance practices across their entire operations for all their equipment.

• [Efficiency Improvement - Asset Utilization]
  

  an ability to take a more predictive—and thereby more proactive—approach to keep assets up and running.

• [Efficiency Improvement - Operation]
  

  By combining the speed and power of cloud-based analytics with transparency across their operation, mine operators have been able to act on their insights in a way that directly impacts their production efficiency. 

• Reduce mine production downtime by 30%.

• Reduce cost-per-ton ore production by up to 50%.