Data driven savings

Do more, with less. It sounds simple enough, and is essential in today’s market, but is it really possible for a mining operation to cut costs without sacrificing productivity? Thanks to the rapidly growing base of connected products known as the Internet of Things (IoT), optimizing mine performance to drive down costs per ton is now more achievable than ever.

The amount of data flowing off mining machines has grown significantly in recent years. Komatsu’s connected mining products worldwide now stream more than 60,000+ data points per second, more than double what was measurable just a few years ago.  In the next five years, it’s anticipated that number will grow to half a million data points per second.

The faster data comes off the machines, is analyzed and turned into useful information, the more quickly mines can react; heading off potential delays and preventing costly downtime. But acting quickly requires trusting the information; essentially, trusting the machines. Engineers employ machine learning methods to continually improve the accuracy of the data that drives cost-saving decisions.

In mining, every machine faces unique challenges. This includes tough operating conditions and a variety of minerals. But to accurately predict machine behavior, there must be an understanding of what normal looks like. Customized models, otherwise known as digital twins, are built unique to each machine and specific conditions. Then as conditions change, so do the models and predictions. Effective data analytics focuses on looking at abnormalities in the data from typical operation and highlighting abnormal behaviors. You can’t prevent what you haven’t seen.

Through continued machine learning and evolving technology capabilities, such as faster and more reliable networks, the opportunities to use data to drive down operational costs -- by reducing or eliminating downtime and increasing productivity -- will only continue to grow. By leveraging IoT solutions the mining industry can increase operational efficiencies, allowing operations to mine the same amount of minerals while utilizing less energy in a more cost-effective manner.

Machine learning capabilities and algorithms grow more accurate with each challenge solved, so the more engagement with industry partners, the stronger everyone’s capabilities grow. As algorithms become more reliable, constant and customer accepted, that information can be incorporated into the basics of machine design, allowing connected manufacturers to continue improving product offerings.

Working together with the machines and our customers, we are steadily improving the flow of data and the speed at which we translate that into actionable information, allowing operators to do more, with less, without sacrificing quality.

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