Emerson unveils edge software for advanced machine analytics

Emerson’s PACEdge.

Technology and engineering firm, Emerson, has launched its PACEdge industrial edge platform, designed to help manufacturers accelerate digital transformation projects by enabling users to quickly create and scale up apps. 

The PACEdge platform is said to simplify application development by bringing together open-source tools into a flexible, integrated and secure platform for utilising machine data and analytics. The PACEdge release coincides with the launch of Emerson’s PACSystems RXi2-BP edge computer, a small form-factor industrial PC that enables high-performance analytics to be run close to the machine.

Emerson said the PACEdge platform helps users securely collect, analyse, store and serve up machine data near the source or across enterprise systems. End users can build and deploy applications for a wide range of uses including monitoring energy, machinery health and productivity. Emerson’s modular, pre-configured development packages containerise applications to allow developers to start in a pilot environment with a few units, and then quickly scale to hundreds or thousands of units.

Derek Thomas, VP of marketing and strategy for Emerson’s machine automation solutions business, said: “Many of today’s edge solutions offer limited connectivity and toolsets, making it difficult to extend across assets, machines or plants.

“The PACEdge platform provides a complete solution that enables manufacturers to start right at the machine with the connectivity and flexibility needed to scale up as they progress on their digital transformation journeys.”

Using modern IIoT protocols such as OPC UA, MQTT and REST, the PACEdge platform provides access to data from industrial control systems and field devices, as well as IT systems and cloud services for plant or enterprise data aggregation. With drag-and-drop programming and embedded web interfaces and visualization, users can use this information to quickly create applications and dashboards to analyse and view operational data, such as overall equipment effectiveness, compressed air usage, energy consumption, acceleration and vibration and other sensor data. 

Advanced applications can combine these outputs with external data sources (i.e., weather, public utility rates) and machine learning algorithms to drive better asset health and performance by detecting potential failures sooner. Operators in the field have immediate access to diagnostic and production information, allowing them to make better decision faster. 

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