IBM Case Studies Predict to prevent: Transforming mining with machine learning

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	Predict to prevent: Transforming mining with machine learning IBM

Predict to prevent: Transforming mining with machine learning

IBM

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics Infrastructure as a Service (IaaS)
Applicable Industries	Mining
Applicable Functions	Maintenance
Use Cases	Predictive Maintenance
Challenge	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. Read More
About Customer	Sandvik Mining and Rock Technology is a world-leading mining equipment manufacturer. The offers equipment and tools, service and technical solutions for mining and rock excavation covering rock drilling, rock cutting, crushing and screening Read More
Customer Name	Sandvik Mining and Rock Technology Read More
Solution	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. Read More Log in to view content
Data Collected	Equipment Status, Overall Equipment Effectiveness, Disposal Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Infrastructure as a Service (IaaS)

Applicable Industries

Mining

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

Challenge

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.

About Customer

Sandvik Mining and Rock Technology is a world-leading mining equipment manufacturer. The offers equipment and tools, service and technical solutions for mining and rock excavation covering rock drilling, rock cutting, crushing and screening

Customer Name

Sandvik Mining and Rock Technology

Solution

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.

Data Collected

Equipment Status, Overall Equipment Effectiveness, Disposal

Impact #1	[Efficiency Improvement - Asset Utilization] an ability to take a more predictive—and thereby more proactive—approach to keep assets up and running.
Impact #2	[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.

Impact #1

[Efficiency Improvement - Asset Utilization]

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

Impact #2

[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.

Benefit #1	Reduce mine production downtime by 30%.
Benefit #2	Reduce cost-per-ton ore production by up to 50%.

Benefit #1

Reduce mine production downtime by 30%.

Benefit #2

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

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Overview

Predict to prevent: Transforming mining with machine learning

Operational Impact

Quantitative Benefit