January 2022

27 www.drivesncontrols.com January 2022 controller located in the field to generate insights. This information can be supplied to the right personnel, close to the source for fast, informed action. Saving bandwidth Edge computing is essentially a distributed computing paradigm that brings computing processing and data storage closer to the location where it is needed, to improve response times and save bandwidth. As devices get smarter, they produce more analytics to generate insights into equipment health and performance. Edge computing technology does this at or near the source of the data, instead of relying on the cloud and the computing power within data centres. With the latest edge controllers, embedded processing brings those insights closer to the plant floor, while also making them more widely available via the cloud. If you have ever thought of IIoT and edge computing as concepts that you might want to adopt in the future, think again. Edge computing, as realised by true edge control technology, makes IIoT a reality for every plant and enterprise today. Easy to integrate into existing plants without needing to start from scratch again, edge computing is enabling manufacturers to embrace the benefits of IIoT, solve key problems simply and affordably, and then scale up. OEMs and manufacturers can use edge computing to evaluate equipment breakdowns and to eliminate common issues. The technology can provide feedback on machine performance to development teams to help them to optimise future products. It can be used to answer questions such as: n How is the machine actually being used? n What quality issues are there?, and n Can costs be reduced without affecting performance? Comparisons can be made between machines, processes and entire plants, as well as raw materials in terms of yield, quality and scrap. Machine use can be tracked, as can energy use, start-ups and changeovers, to help optimise performance and ensure compliance with safety and environmental requirements. The latest edge controllers – such as Emerson’s PACSystems RX3i system – offer both deterministic and non-deterministic control in a single compact device. They effectively incorporate “two sides of a brain”, with the left side being where intelligent sensor data is gathered and real-time deterministic control is provided. The right side, meanwhile, has a software stack, running on Linux, that delivers functions such as data processing and analytics, dashboards, data-logging, and remote monitoring and diagnostics. Imagine a single controller – technically an industrial PC with a multicore architecture – then separate it into two halves. The left half performs the typical functions of a controller, including reading inputs, executing logic in real-time and writing outputs. This all takes place in the programming environment typical of a PLC, with an I/O network, redundancy possible between controllers, and connections with HMIs, Scada and DCS. In addition, the left side is also able to prevent any problems involving the right side from affecting the control functions. The right side, which runs a Linux-based open operating system, has the ability to manage multiple loops and routines locally, collect data and interface with standard programmes for the IT world, such as Python or Java. It can include a Web server and secure communication protocols to the cloud such as MQTT. Above all, it has the ability to process large amounts of data locally, through algorithms available on the controller and offering the possibility of external optimisation. For the first time in automation, two worlds are truly connected and interacting: the data on the left side, providing the basis for the processing on the right side. Automatic optimisation algorithms – possibly connected to a cloud – produce results that also serve to further optimise the control part on the left side. Exchanging data between PLC and Scada systems, running optimisation routines and using calculation results to improve control parameters were possible before, but with the latest edge controllers this can be done much faster to improve the control logic of the machine or process itself. Detecting anomalies A typical edge application is detecting anomalies. This requires historical data from a database such as InfluxDB or SQL Lite, and a machine-learning (ML) algorithm created in Python, or other tools such as Promethius. A selected data sample is taken and cleaned, removing all outliers, and then the ML program is trained. The test dataset can then be applied, and eventually the live data from the machine and relevant instruments. If an anomaly occurs, the ML algorithm can spot and record it, or raise an alarm for an equipment operator. This helps issues to be identified before they become real problems. It allows machine or operation shutdowns to be scheduled, the necessary parts ordered, and downtime minimised, ultimately cutting costs. The need for digital transformation is now much more apparent, with business owners able to see the return on investments from IIoT supported by edge computing. IIoT allows companies to access, analyse and historise previously isolated data that is critical to operational improvement. Edge controllers provide an affordable, manageable way to bring the IIoT down to the machine edge to enable organisations to start to solve big data problems one step at a time. With computing now being faster, smaller and cheaper than ever, and data transfer being widespread with costs reducing, it is now easy to implement “little data” projects, along with ways to make mission-critical capabilities like remote monitoring a reality. n For more information visit www.emerson.com/edge-computing EDGE COMPUTING n Programmable automation controllers with built-in edge technology can be used for applications such as local Web-based HMIs, datalogging, co-processing and remote alerts