The next level of analysis occurs at the mine’s operating center, where thousands of tags from all concentrators arrive every 15 seconds. These tags are orchestrated with data from dozens of other mining processes and used to analyze the grinding circuit’s performance and push operational adjustments back to the grinding circuits’ control systems.
As in the wind example, the remote monitoring center receives data from dozens of mines across the globe, this time at 10-minute intervals. At the center, teams of data scientists and engineers utilize massive cloud computing systems to analyze years of historical data to find process or asset anomalies, predict outcomes, and further optimize operations. Finally, concentrator data are blended with data from the mining company’s enterprise systems to provide key performance and operations analytics relevant to the CFO, VP of Operations, and other key stakeholders.
Big Data at Work: The optimized transportation company
Additional requirements for an industrial big data platform can be seen in the transportation industry. While wind farms monitor and optimize fixed assets, assets in the transportation industry – such as aircraft and locomotives – are designed to be in motion for the majority of their operational lives. Like wind farms and mines, data is transmitted in varying cadences and quantities depending on the analytic, and where it is performed – at the machine or enterprise. Unlike wind farms and mines, however, the communications between these mobile assets and their analytical systems often take place only when the asset has arrived at a destination. This changes how the data and analytics are used.
For an aircraft engine, onboard intelligence must be able to act immediately, without human input, and with extreme degrees of reliability (99.999999999%) and confidence when a potentially significant event is detected or predicted. But, this autonomy of action must be augmented by an understanding of when an escalation is needed. In such a case, on-board analytics for mobile assets must be able to transmit the essential tags4 via satellite communications to the operations center while in transit. This means that the industrial big data platform must support high levels of mission-criticality, redundancy, and confidence in order to conform to the real time, in-transit requirements of a mobile asset.
For the transportation industry’s communications needs, the industrial big data platform has to be able to transfer large quantities of data during what may be a brief refueling or reloading stop. A modern aircraft engine, which generates hundreds of tags every ten milliseconds, can generate one terabyte of sensor data per flight, which means a fleet of hundreds of airplanes will continually create peaks and valleys in an airline’s data volumes. While downloading may take place only once or twice a day, the analytics at the airline’s flight operations center and an OEM’s remote monitoring center must be able to flag potentially anomalous information as it is being downloaded and analyze it in real time. This allows the airline to optimize its maintenance functions, keeping the fleet as close to plan as possible.
Mobile assets operate in continually changing environments, which impact asset degradation at differing rates (e.g., flying through hot, sandy deserts degrades aircraft engine fan blades faster than flying through mild climates), and their locations have to be accounted for in operations and maintenance functions. Unlike fixed assets, these assets have to travel to a maintenance center, as opposed to having maintenance performed on site, adding further complexity to scheduling, parts availability, and other maintenance and operations factors. And significant events must be acted on in real time, while the enormous amounts of data collected by these assets means that data communications must be managed in order to optimize relatively scarce in-motion network bandwidths.