Writer David Shenk coined the term ‘data smog’ as far back as 1997. Even at that early point in the information revolution, there was a growing recognition that data alone means little unless it is refined and harnessed to deliver insight and analysis.
It could be argued that this data smog has yet to fully lift, particularly in the broad field known as the Industrial Internet of Things (IIoT).
However, powered by the growing sophistication of artificial intelligence models, the past couple of years have seen the deluge of data begin to take on a semblance of order.
Industrial intelligence company AVEVA specialises in developing software for a range of heavy industries, including mining, where its solutions are widely used by Tier 1 operators – including a major operator in Papua New Guinea.
Recently, in collaboration with industrial digital solutions company CereBulb, AVEVA applied its expertise to build a powerful, real-time analytics solution focused on heavy mobile vehicles and fleets in the mining industry.
This was achieved by leveraging AVEVA’s data infrastructure platform PI and its cloud-based system integration and orchestration layer, CONNECT, with CereBulb developing a customised application to monitor and optimise mobile equipment performance.
While mobile mining equipment is already equipped with OEM-based onboard telemetry and data-collection capabilities, the system created by AVEVA and CereBulb takes that data and transforms it into coherent, easy-to-understand, actionable insights.
“What we do is bring another layer of data analysis,” AVEVA global industry principal for mining and metals Glenn Kerkhoff told PNG Mining.
“Fleet Management Systems operate at what’s known as the cycle level. A haul truck gets a load of material from the shovel, takes it to the crusher, and then returns to the shovel. That’s one cycle – essentially one record of production activity,” Kerkhoff said.
“We look at the full time-series data – every data point, every second – as the truck travels that route and back, to understand what the haul truck is doing throughout the entire journey.
“We assess how the machine is performing at every point along the haul road and can see the cumulative effect across the whole cycle.”
Rather than a swarm of disconnected data points, AVEVA and CereBulb have created a platform that puts data to work, mining insights that can quickly drive operational improvements.
Kerkhoff gave an example of the kind of insights the platform can provide.“If you have an operator driving a haul truck below optimum, perhaps 10 percent below optimum speed in certain areas of the haul road, then that becomes a 10 per cent opportunity cost in lost production. And when you’re moving 240 tonnes of ore from point A to point B continuously 24×7, then 10 per cent becomes a big deal over time,” he said.
“We look at all of the time series data, so every data point per second as the truck travels along the haul road and back, to look at what it is doing on its journey across that cycle. The sorts of things we look at is the equipment performing at its peak performance at every point along that road, not just an average across the whole cycle.
“If we can monitor each point along the road and identify where operators need additional training to improve their behavioural performance, the cumulative impact across all machines and all shifts becomes significant.”
For a large mining operation in Australia-Pacific – operating on Aniolam Island in Papua New Guinea – AVEVA recently partnered with CereBulb, to integrate the AVEVA PI system for equipment telematics, fleet management, and ERP systems.
The results of this initiative improved equipment availability by three per cent, and uncovered a five per cent opportunity to cut diesel consumption, while also lowering CO2 emissions.
CereBulb chief executive officer Ankit Dhorajiya said the company was able to leverage all available OEM data collected from mobile equipment to build analytics that provided granular insights into heavy haul truck fuel burn.
“We built all the analytics using our domain expertise to execute various use cases around the equipment. One of those use cases was optimising fuel usage across the truck cycle time, covering both loading and dumping activities,” Dhorajiya said.
Utilising more than 150 sensor-based data collection points on each truck, the platform immediately identified areas for improvement. In addition to OEM data, the platform also integrates locational and environmental data, providing a comprehensive operational picture.
“We set a benchmark for fuel management and, from there, were able to identify a range of machine-related issues affecting the trucks. From that analysis, we were able to save the customer around five per cent in fuel burn, which represents a significant cost saving and a positive impact on carbon emissions,” Dhorajiya said.
“What we’re doing here is providing data-driven insights to help miners improve their operations,” Kerkhoff said.
As mining companies continue to look for ways to lower costs, improve outputs and reduce carbon emissions, AVEVA and CereBulb have created a platform that is well and truly lifting the data smog to reveal a new horizon of insights.
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