Learn how to logically consolidate multiple data points, such as machine states and error codes, into a single machine state and standardize these over multiple data models like the Weihenstephan Standard, PackML, etc.
In this course you will learn how to connect InfluxDB with the UNS using Benthos.
Learn the basics of data contextualization with Node-RED to process simulated sensor data and fit it into the United Manufacturing Hub datamodel.
In this course, we'll explore how to use MongoDB to track products across production lines. We'll show you some experimental techniques that can help you understand the potential of MongoDB in this area especially if you have unstructured data.
Learn how to connect MongoDB Compass with the UMH: Download & install MongoDB Compass, forward & copy mongodb pod's port in UMHLens, create new connection, replace port, get mongodb password, authenticate, & connect.
Create a Benthos deployment in UMHLens/OpenLens and configure it to push kafka messages into MongoDB.
Easily install MongoDB using Helm on your Terminal or UMHLens. Check for Bitnami in the repo list, add if necessary, and install MongoDB in United Manufacturing Hub.
An overview of the benefits and limitations of using Kepware in IIoT, and how the United Manufacturing Hub (UMH) can help maximize its benefits.
Learn how to export and import nodes/flows in Node-RED easily with these step-by-step instructions.
Learn how to set up Node-RED flows to monitor a simulated production line's state, machine processing speed, and production counts using MQTT and UMH data model conversion.
Learn how to create a Node-RED flow with simulated OPC-UA data using node-red-contrib-opcua and MQTT. Follow these step-by-step instructions to build the flow and check the data in Grafana.
Discover how you can share your ideas, contribute to our blog, and connect with us on other platforms.
