Industrial Control by Edge Computing for Automation Technology
Motivation
Due to high latencies between the machine/plant level, the cloud and the resulting restrictions, many users do not use cloud-based solutions. Waiting for an answer from the cloud, especially with existing real-time requirements in production environments, is not an option. As a result, less machine data reaches the cloud, creating challenges for the development of new services such as predictive maintenance and machine-as-a-service.
The solution to this circumstance requires a rethink from a central, cloud-based system architecture to a distributive edge cloud driven architecture. The so-called edge devices represent a novel, fully integrated generation of Internet gateways that create the missing link between the proprietary world of sensors, actuators and products and the cloud.
IoT-Technologies in Production Engineering
The use of real-time Edge Cloud technologies enables classic applications in control technology to transfer critical data from the field level. The edge devices used for this application can be implemented on low performance hardware (e.g. Arduino, Raspberry Pi etc.).
The combination of local pre-processing and cloud-based computing power allows new types of applications and business cases in production technology. The potential lies in the transfer of control tasks from complex industrial controls to the edge cloud through the integration of a large number of edge devices and the use of device and digital shadows.
This also enables the manageable development of control algorithms based on high-level languages and comprehensive software deployment worldwide.
Industry-Scenario – Significant increase of OEE
Despite generally robust automation technology, dimensional deviations within product lines lead to frequent system downtimes and manual "post-teaching" of robots. The approach is to use a sensitive robot to send a measured real trajectory using Edge Devices to both the cloud and the downstream robot for further processing of the product. Both mechanisms and the process control are based on device and product shadows, which can be exchanged thanks to Edge technology. Edge detection can then be used to check the quality.
In this context, an industrial scenario was developed as an example, the so-called Industrial Control by Edge Computing for Automation Technology (ICECAT). Consisting of 6 Edge Devices for controlling three robots, a camera, sensors and general process control, it examines the capabilities of Edge technologies based on the Greengrass (GG) technology from Amazon Web Services.
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