PACEDGECORE is Emerson's PACEdge industrial edge computing platform — a pre-integrated software stack that runs on PACSystems edge controllers and industrial PCs, bringing IIoT analytics, visualization, and connectivity to the machine level. Rather than assembling an edge solution from individual open-source components (installing Docker, configuring databases, wiring up OPC UA connectors, building dashboards), PACEdge delivers the entire toolchain pre-configured on Ubuntu Server 20.04 LTS, ready to connect to plant-floor devices and start collecting data.

What does an edge platform solve that cloud-only analytics cannot?

Cloud-only IIoT architectures send raw machine data to a cloud service for storage, analysis, and visualization. This works when bandwidth is abundant, latency tolerance is high, and internet connectivity is reliable — conditions rarely met on the plant floor. A vibration analysis application sampling accelerometer data at 10 kHz cannot stream that volume to the cloud economically. A quality-inspection algorithm triggering a reject gate must decide in milliseconds, not the round-trip time to a cloud data center. PACEdge addresses both constraints by running analytics at the edge — on an industrial PC mounted in the same cabinet as the machine controller. Data is processed locally, decisions execute in real time, and only aggregated results or exception reports are forwarded to upstream systems. When internet connectivity drops, edge analytics continue uninterrupted — a critical distinction for production-critical applications.

What is in the pre-integrated toolset?

PACEdge bundles Docker and Portainer.io for containerized application deployment and management — allowing developers to package analytics applications as portable containers that run identically on a pilot RXi2-BP edge computer and on a fleet of 100 production units. Node-RED provides a browser-based, drag-and-drop programming environment for wiring data flows between industrial protocols (OPC UA, MQTT, Modbus TCP) and databases, dashboards, or cloud endpoints — no compiled-code development required for basic integration logic. Grafana delivers rich, interactive dashboards for time-series visualization. InfluxDB and Timescale handle high-ingest-rate time-series data (machine sensor streams), while MariaDB manages relational data (asset registries, configuration records). Python and Node.js runtimes support custom analytics applications — from simple statistical process control calculations to machine learning inference models. The Group Management feature allows a central administrator to push software updates, configuration changes, and new container deployments to up to 100 edge nodes from a single management console, transforming the edge fleet from a collection of individually-managed PCs into a centrally-governed distributed computing platform.