Edge Computing
Edge computers in production
Pure cloud computing was not practical enough for the production-related industry, so another solution had to be found. This was the birth of edge computing. This means that intelligence and real-time control are being introduced in the machinery, not at the data center.
Artificial intelligence (AI) was the trending topic at Hannover Messe 2019, the showcase for the performance and future of German industry. In the run-up to the event, the digital association Bitkom presented a study according to which 12% of all German industrial companies are already using AI in the context of Industry 4.0. According to the study, companies hope to make the greatest progress in the use of artificial intelligence by increasing productivity (47%), predictive maintenance (39%) and process optimization (33%). According to the Bitkom study, one in four machines in production is already connected to the internet; in 10 percent of companies, more than half of all machines are connected(source).
Computing power and storage capacity are now readily available thanks to cloud computing. This means that immense amounts of data can be searched and analyzed with algorithms in a matter of seconds. It is now impossible to imagine IT without cloud computing. Operational technology (OT) has so far been deprived of these innovations and their many advantages.
Cloud computing has not yet been able to realize its full potential when it comes to machine control in complex production processes: latency times were still too long, available bandwidths were still too unreliable, and outsourcing data to external clouds was a security risk per se for many. Pure cloud computing was not practical enough for the production-related industry, so another solution had to be found.
This was the birth of edge computing. The edge computers required for this are suitable for machine control, visualization and communication and now also for integrated analytics and AI applications, including preventive maintenance and machine learning functions. Edge computers are therefore a key component in fulfilling the hopes of more than just German industry (and confirming Bitkom's forecasts).
Digital twins and container technology enable cloud capability
Close to the control processes and largely integrated into the IT network, data processing close to the machine was to take place without any loss of time via the internet to the cloud and back. Nevertheless, the company naturally wanted to benefit from the advantages of the cloud for the OT level as well, and the principle of digital twins was born: a real machine is digitally replicated in the cloud.
In the case of remote production machines, the next time there is reliable contact between the edge computer and the cloud, the new data is exchanged between the real machine on the production line and the digital twin in the cloud and flows into the machine control system or into the cloud as new evaluations. Container technology makes it possible to exchange not only data, but also entire processes such as data pre-processing, by moving them from the cloud to the edge computer. Despite this, only small amounts of data are exchanged and latency times are significantly reduced. Another trend is the hybrid cloud, i.e. a local, on-premises cloud for time-critical and sensitive data and an off-premises/public cloud for downstream big data evaluations.
While increasingly powerful hardware is also becoming a matter of course in embedded computing, it is supported by the software required for edge computing. For pre-processing, filtering and containerization between cloud and machine, Kontron is expanding its portfolio of standard, modified standard and user-specific hardware to include software components based on the SUSiEtec IoT software framework for cloud connectivity.
More innovations thanks to new standards
Just a few years ago, it was unthinkable that the industry would one day be discussing whether standard IT Ethernet would also be suitable for deterministic machine communication. Thanks to corresponding standardization efforts such as Open Platform Communications Unified Architecture (IEC62541 OPC UA) and technical developments such as Time Sensitive Networking (IEEE 802.1 TSN), the rigid boundaries between OT and IT are being harmonized and made more permeable. In future, machine actuators and sensors can be connected directly to the edge computer and there will be continuous, deterministic communication right through to IT via TSN and OPC UA. Thanks to standardization, the user data can then be processed directly in the cloud.
Edge computers currently have to meet several requirements:
- Today, they must be the gateway that ensures a secure connection between the technologies at different levels of the automation pyramid, operating technology and information technology.
- They must be prepared to ensure communication in a cloud, whether embedded (on-premises/private) or public (from an external provider), today and even more so in the future.
- They must also have sufficient performance and storage capacity for current and future edge architectures to complete a wide range of tasks quickly on site at the machine. They not only take over tasks previously performed by the PLC (programmable logic controller), but can also be used to control several independent machines and processes through the virtualization of control computers.
- They must be prepared for current and future industry standards in order to integrate seamlessly into existing and future IT and OT infrastructures.
TSN-capable via plug-and-play with TSN network card
Kontron was one of the first suppliers to launch ready-to-use products for Time Sensitive Networking (TSN) with the OPC-UA (Open Platform Communications Unified Architecture) standard. A standard network card for TSN brings any expandable PC into a TSN network in which data packets are delivered in a time-sensitive and deterministic manner. Industrial computers in which several network cards can be inserted can therefore already be used for machine control. Expandable Kontron industrial computers pre-configured with the appropriate software can optionally be supplied with several TSN interfaces.
Kontron also offers a Computer-on-Module (COM) based on the SMARC 2.0 standard and an edge computer with the NXP Layerscape LS1028 multi-core processor with integrated TSN switch and up to five TSN-capable 1 Gb/s Ethernet ports for the implementation of TSN connections.
OPC UA and TSN conquer the field level
For edge computers, this means that their task as a gateway is becoming increasingly less demanding, as TSN relies on standard computer and internet technology. The "intelligence at the edge of the network" can therefore be used for other, more complex tasks in machine control on site. Another task of the intelligent edge computer will be to pre-filter data so that only data required in the cloud reaches it, for example for evaluation in management systems such as business intelligence or enterprise resource planning applications. For these tasks, S&T Technologies, a sister company of Kontron, offers the IoT software framework SUSiEtec, in which these tasks can be realized in customer-specific projects.
AI applications at the intelligent edge
For special AI tasks, such as machine learning or image processing, edge computers must be expandable, for example with GPGPU (General Purpose Computation on Graphics Processing Unit), FPGA (Field Programmable Gate Array) or chips such as the Intel Movidius Myriad X VPU neural compute engine.
Neural network processors such as the Intel Movidius Vision Processing Unit (VPU) can be used for edge AI applications such as deep learning. They relieve the CPU and accelerate the inference process many times over. Intel Movidius VPUs are implemented by Kontron either via plug-in modules or as part of projects on carrier boards or SBCs.
S&T Technologies has developed an AI plugin for the SUSiEtec software framework specifically for image recognition using Edge AI and has created a special consulting offer. This includes a prepared training environment consisting of an embedded server together with corresponding pre-installed open source software, such as Keras and Tensorflow with a focus on visual machine learning. This package alone saves companies around six weeks of preparation and familiarization time.
Special, simplified software interfaces, which are also provided via SUSiEtec, ensure that .NET and Java developers can also use the corresponding OpenVINO middleware from Intel for the inference part of classic training results in a simplified manner. Unlike with native C programming, only a few lines of code are required in .NET and Java with SUSiEtec AI.
Performance "From Edge to Fog to Cloud" plus new applications
For Industrial IoT and Industry 4.0 concepts, intelligent edge computers bring computing and storage power directly to the machine. Due to both established and new technologies such as cloud computing, container software, OPC UA over TSN and artificial intelligence, the areas of responsibility are changing. The previous pure gateway computers are becoming powerful real-time computers on the machine. This ensures a seamless transition between OT and IT and also makes computing-intensive applications such as artificial intelligence efficient, secure and productive in the manufacturing industry without a direct cloud connection.
Reiner Grübmeyer, Head of Product Management Systems & Software at Kontron / ag















