The digitalisation of business and manufacturing resulting from IIoT and Industry 4.0 have a profound impact on factory automation. They envision networks of connected equipment collecting and analysing huge volumes of data to improve business insights and decision making. The aim is to speed up innovation whilst increasing productivity and competitiveness through lower manufacturing costs. As a result, edge processing has become a popular option for producers.More data means more processing and a cornerstone of this digital transformation is cloud computing for off-site storage and data processing. Importantly, cloud services are more scalable, reliable and affordable than on-site IT platforms, although they may raise concerns of ownership and data security.
Whilst the cloud is useful for storing and manipulating large data volumes and managing big data, there are drawbacks. These include latency i.e., the time is taken for data to move between the users and the host servers and bandwidth.
But data can amass at a phenomenal pace and cloud services may not support applications that demand a real-time response. What is important is recovering information from the data, and this has led to a growth in edge processing. Moreover, edge processing is more efficient than in the cloud and supports the convergence of information and operational technologies.
Edge processing/Edge computing
Edge processing (also called fog computing) takes place locally to the point of data collection. It is where users can analyse, aggregate, filter and pre-process data from intelligent devices directly from within their automation platform. This eliminates the lag and latency of the cloud without needing industrial hardware for harsh production environments. Moreover, it reduces potential security issues as the data remain on-site at all times.
Edge processing can run on hardware from the latest generations of PLCs, machine controllers, industrialised PCs and dedicated edge computers. Following analysis, information collected from the plant floor sensors, drives, PLCs, mechatronics systems and energy monitoring devices is available immediately to the processes that need it. This includes forwarding to higher-level enterprise systems for supply chain optimisation, improved production control and plant operation simulations.
Advantages of edge processing
Edge processors can also bridge data convergence between the IT and OT systems. It can also share data with both the cloud and the IT systems, and in some cases replace the need for cloud computing.
Keeping time-critical information within the automation environment and pre-processing information needed by higher-level or cloud systems reduces bandwidth requirements. It also provides greater assurances of security and removes issues of regional data handling and storage compliance regulations.
Edge computing offers other benefits when compared with using the cloud. For example, the lower bandwidth requirement allows more devices to operate at a lower cost and with less congestion. Furthermore, the information provided supports faster decision making and the development of new applications
Smarter machines with edge processing offer the opportunity for more flexible production planning. This includes small batch runs and the increased integration of robotics.
Access to more data enables machine builders to offer remote diagnostics and predictive maintenance packages to reduce downtime. Moreover, it presents machine builders with the opportunity to optimise machine performance to benefit the user in the development of new machines. Edge computing also enables them to monitor and record the machine’s health and performance.
The leading automation suppliers like Schneider Electric, Mitsubishi Electric and Siemens have white papers available, and more universities are seeing the opportunity to support local industry.
For a free introduction to Edge Computing ebook from the Dummies educational series available to download here.
