Industry 4.0 is upon us – a vision for how smart manufacturing is transforming how we create and service machines. These could be parts of planes, trains, vehicles or even wind turbines; and through smart management and innovative use of data, fleet managers and field service workers are able to uncover new insights into the performance of these powerful machines.
Take an aircraft jet engine, for instance. In a 30-minute flight from Frankfurt to Berlin, a single jet engine will produce approximately 1.5Tb of data – enough information to fill 94 iPhones! While the sheer volume of data brings complexities in its own right, it is also incredibly difficult to transfer data to the ground from an airborne aircraft in real-time. But if an engineer in the terminal could be alerted about an error in the engine before it touched down, a replacement part could be sourced and delivered as quickly as possible, reducing the amount of time the aircraft had to spend on the ground. “Aircraft on Ground” (AOG) time is critical for the aerospace industry. The plane is only making the airline any money when it’s in the air, and therefore any time spent stranded on the ground due to technical problems costs them dearly. According to Airbus China, the daily cost of a grounded A380 runs to $1,250,000 and this becomes a more grave issue when you consider the implications across an entire fleet.
Through advanced technologies such as the Internet of Things (IoT), Big Data, cloud computing, High Performance Computing (HPC), additive manufacturing and Unified Communications (UC), the industry can start to take advantage of new solutions to help them reduce maintenance time and return their aircraft back to the skies.
Together with our industry partners, Siemens, Worldline, Bull Sequana and SAP Hana. we have developed an end-to-end IoT solution based on Predictive- Maintenance-as-a-Service. Using the Communication Platform developed by Worldline, jet engine data could be transferred from the air to the ground in real-time. This could then be processed by Atos Codex, Atos’ suite of cognitive analytics solutions, and stored on Bull Sequana, the Exascale class HPC by Atos. Once Bull Sequana has performed complex analysis on the extensive data sets, only the most important data would be transferred to the SAP Hana Cloud platform and passed on to the fleet manager, giving them the necessary insight to assess what needs doing next. A spare part could be ordered and 3D printed, a much faster process than conventional methods. Alternatively, an onsite engineer could be supported by a mixed reality solution, such as Microsoft HoloLens, so that they could be instructed to make the repair by a remote expert.