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Article
How the Internet-of-Things is Transforming Mining

Heavy equipment manufacturers are deeply involved in the Internet-of-Things (IoT) phenomena. Here we examine the impact of IoT on mining equipment and operations.


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One of the most mature and rapidly growing application domains for the Internet-of-Things (IoT) is in heavy equipment used in areas like construction, mining, agriculture, and logging. Mining equipment provides a great example of some of the things that are possible with the IoT.

Figure 1 - Bucket Wheel Excavators (See front-end loader in left foreground for sense of scale)1

Making Mining Safer

One of the key goals of IoT in these environments is improving safety. These machines are usually enormous and powerful (some of the largest machines in the world). If a person is in the wrong place at the wrong time, the results can be fatal.2 To prevent accidents, heavy equipment often incorporates location/proximity sensors and warning technology, such as GPS, radar, video, and RF locating devices (on both personnel and equipment) to ensure the safety of construction and mining.

Autonomous Operation

Perhaps the best way to reduce the chances that someone is hurt is to remove humans from the dangerous environment altogether. In the Pilbara region of Western Australia, Rio Tinto has been using autonomous, self-driving mining trucks since 2008. Fifty three massive Komatsu driverless mining trucks navigate routes and respond to local conditions using over 200 sensors on the truck, GPS, and a radar guidance system. This Autonomous Hauling System (AHS) is a highly proven system. Rio Tinto’s driverless trucks have driven almost 3 million miles and hauled nearly 250 million tons of ore. The trucks are connected wirelessly and overseen by controllers in Rio Tinto’s operations center in Perth over a thousand miles away.

Benefits of Driverless Mining Trucks

Safety is improved by eliminating driver fatigue and error and by reducing the number of people in the mining site. Autonomous trucks also enable more predictable, continuous, optimized operation, all day and night, without need for lunch breaks or shift changes. They have shifted the types of skill sets needed by Rio Tinto, who now needs more technical people sitting in their operations control center. Those workers have the advantages of living comfortably in a major city rather than out in the middle of nowhere.


Other Mining IoT Examples

BHP Billiton has also been testing Caterpillar’s MineStar system and 240 ton driverless mining trucks at its Jimblebar iron ore mine in Western Australia. The New Afton copper-gold mine near Kamloops Canada uses a number of drilling machines and shotcrete sprayers that are partially or fully automated.


Beyond Trucks

Rio Tinto has also been trialing its Autonomous Drilling Systems (ADS) in its West Angelas mine and they have invested over half a billion dollars to automate driverless trains that haul the ore from the mines to the ports. Rio Tinto’s ‘Mine of the Future’ vision includes not just automated haulage and drilling but also automated mining operations, remote monitoring of mining operations and processing plants, condition monitoring of stockpiles, computer-optimized flotation tanks (for extracting minerals), and autonomous trains, all controlled by centralized operations centers. James Petty, general manager of Mine of the Future for Rio Tinto Iron Ore, put it this way: “It’s a total system. The whole mining operation is planned in advance and is dynamic. It has a memory. It learns. It anticipates trouble and responds. Its neural paths interlink at light speed. It functions like a brain the size of an iron ore province, coordinated by the ‘control tower,’ the Operations Centre in Perth.

Rio Tinto's Mine of the Future Video

Mine Automation System and Big Data Visualization

On top of all this sits a Mine Automation System, which integrates all the automated physical elements. It creates real-time multi-dimensional models from a variety of data sources including the sensors on the equipment as well as geological and other data. The system can then be used to optimize the mine’s layout, operation, vehicle paths, and so forth, coordinating all the moving pieces for the most efficient operation. Rio Tinto also has visualization software (called RTVis™) providing 3D displays of the mine and other related data for use by pit controllers, geologists, drill-and-blast teams, mine planners, and supervisors.

Figure 2 - RTVis™ 3D display of an iron ore pit

Predictive Maintenance

For mining vehicles, predictive/preventative maintenance is becoming increasingly important. They are bristling with sensors to measure things like fluid temperatures, levels, pressures, contamination; bearing rotations, temperature, and vibrations; frame rack, bias, and pitch (affected by load and road conditions); engine speed and gear position; brake pressure and temperature; drive train performance; and vibrations at various locations in the truck (especially bearings). These data are all transmitted remotely to monitoring centers that can be alerted to potential trouble before it happens. Instead of going by regularly scheduled maintenance (e.g. every 2,000 hours of operations), a predictive model based on sensor data can recommend when maintenance should be performed. This simultaneously reduces the average frequency of maintenance (reducing maintenance costs) while doing earlier preventative maintenance in those cases where it is needed, thereby decreasing costly and potentially dangerous failures in the field.

Layered Architecture

In this mining equipment example, the IoT is embodied in the sensors, computers, control mechanisms/actuators, and communications systems on the mining equipment itself. As illustrated in Distributed Intelligence in the IoT, the embedded computer systems on the mining equipment are tightly integrated with both the sensors and control systems to drive the truck, control the drill, and so forth. But in addition, these devices are connected to the higher level centralized systems, which can monitor the situation as well as send new instructions and routes for equipment. All the data gets aggregated and can be used by the Mine Automation System and visualization software.

What Does This Mean for the Heavy Equipment Industry?

IoT has implications for the heavy equipment industry, such as:

  • Optimization Opportunities—The combination of fine grained sensor data and computer-controlled machines enables analytics and optimization which can reduce wear and tear, reduce fuel usage, increase machine lifetime, increase productivity, and more.
  • Improved Machine Longevity and ReliabilityPredictive maintenance enables machines to last much longer, with fewer productivity-interrupting breakdowns or maintenance stoppages.
  • Improves Safety—This is key in industries like mining, construction, and oil and gas, where safety is paramount.
  • Value-add Services—The possibilities for value-add services by heavy equipment manufacturers and others are practically limitless. We are entering a whole new era of creativity where heavy equipment manufacturers can find new ways to differentiate themselves, add value, and become more embedded in their customers’ businesses.
  • New Hardware and Software Solution Markets—Whole new domains of solutions are emerging, from sensors on up to controlling software, analytics, connectivity, development platforms, optimization solutions, and complete solution suites. Many of these are horizontal (not specific to an industry), but especially at the systems level (for hardware) and application level (for software), we are seeing and expect to see many more industry-specific solutions for mining, trucking, agriculture, etc. Some of these, such as the Cat® MineStar™ system, will come from the heavy equipment manufacturers themselves, but many will come from independent technology firms.
  • New Job Skills Required—This is part of a larger trend in general in the economy away from manual and skilled industrial labor jobs to skilled technology-based jobs. The example here is mining truck drivers being replaced by operations control center operators. In some cases the drivers themselves have been able to make that transition. In other cases the skills difference is too big. We see the same thing in farming—farmers are becoming big data maestros and technology coordinators. And similar changes in other sectors as well, such as construction, lumber, and other heavy equipment-using industries.

IoT is changing the mining industry. It is making it safer, more efficient, and more automated. It is making the jobs more high tech. It is allowing more people to work remotely at a place they prefer, and requiring fewer and fewer workers at the mine site. We are only just at the beginning of this transformation.

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1 One description of the bucket wheel excavator pictured above said it takes only two people to operate this machine, but it moves as much dirt as 40,000 men using shovels. -- Return to article text above

2 There were over 200 fatalities resulting from collisions between construction personnel and construction equipment or objects reported for 2008. This is over 20% of all construction industry fatalities in 2008. -- Return to article text above


To view other articles from this issue of the brief, click here.




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