FEATURE Machine Building
Industrial edge computing for
comprehensive analytics Silvia Gonzalez from Emerson explains how manufacturers can start using edge controllers and industrial PCs to provide insights from existing or new production machines
T
he perception for many manufacturers is that analytics initiatives are enterprise-wide endeavours, hosted in the
manufacturing execution system (MES) domain. This causes concern about the available resources, time and cost related to a major IT project, especially if the company’s skillset is orientated towards plant-level operational technology (OT). However, these concerns can be alleviated by edge analytics. Using modern edge controllers, industrial PCs (IPCs) and OT- focused software, users, systems integrators and OEMs can build practical analytics systems from the machine upwards, instead of from the enterprise down. This approach can provide immediate returns, such as delivering overall equipment eff ectiveness (OEE) metrics, and off ers the opportunity for deeper analytics and the possibility to scale up across the enterprise. Moving computer processing from the enterprise to the edge makes a lot of sense. Most of the important data is available at the machine itself, and data from smart sensors and other external systems located nearby can also be incorporated. It can be expensive and complex to transmit and store high-fi delity data at the enterprise level for analysis. If traditional automation systems are used, every new sensing point must be manually confi gured and mapped through multiple communication links and systems to reach the enterprise level. Important data can lose timeliness, whilst unimportant data consumes bandwidth and storage. Key metrics such as OEE (availability, performance and quality), runtime hour, throughput, scrappage, energy consumption and machine status can all be processed at the edge. Both raw data and resultant calculations can be accessed directly, with
30 May 2022 | Automation
consolidated results transmitted to the cloud for eventual higher-level analysis. Edge processing is ideal for understanding the health and performance of each machine. This initial high-value step can then be expanded, with analysis performed for an entire production line or fl eet of equipment installed at diff erent plants.
Edge controllers and IPCs It is possible to perform some analytics functions using traditional programmable logic controllers (PLCs) and human-machine interfaces (HMIs), but, in general, these devices are designed just to provide real- time control. For analytics tasks at the edge, there is need for better processing capability, support for more OT and IT communication protocols and tailored software. These facets are provided by dedicated edge controllers and IPCs, which are designed to help users add Industrial Internet of Things (IIoT) and edge analytics to new or existing machines and operations. To achieve this, edge controllers include not only PLC functionality, but also an independent and integrated on-board operating system able to execute advanced visualisation, analytics and communication tasks. IPCs, which are standalone computers, can also perform these duties.
Both types of edge computing platforms
are designed to cope with environments with extreme temperatures and high levels of vibration. What diff erentiates edge controllers and IPCs from standard PLCs and HMIs is their ability to run software applications and suites that provide IIoT data connectivity to controllers, smart sensors and condition-monitoring equipment, communication gateways to other peer or higher-level systems, visualisation and dashboards showing machine status and
analytics, and analytical computations for OEE and energy sustainability. With the right hardware and software in place, any machine can be IIoT-enabled. Because end users typically operate many machines, often distributed across many sites, any machine-level IIoT solution must be scaleable to the plant and then the enterprise. An open, modular, scaleable and fl exible software platform is ideal to implement IIoT and deliver various deployment scenarios. Complementary software packages with extended features can provide plant-level analytics and energy-effi ciency evaluation. Connectivity, communication, visualisation and analytical software are available individually or in a suite of products, and sometimes pre-loaded for convenience on edge controllers and IPCs. Confi guration wizards enable projects to be completed in minutes to calculate key performance indicators (KPIs), OEE and downtime. Wizards are tools providing a step-by- step procedure, guiding users to import the specifi c information and parameters needed to create an entire OEE application Dashboards are used to visualise machine and production information, including total run- and downtime – estimated versus actual cycle times, and the number of units produced. This information is also stored in a database and forms the basis of detailed reports. By keeping analytics local, decisions can be made at the plant level, but information can also be available via the cloud, so that it can be incorporated with other data sets or analysed at the fl eet or enterprise level.
CONTACT:
Emerson
www.Emerson.com/PACSystems
automationmagazine.co.uk
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