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Real-Time OPC-UA Tags Monitoring in the the Cloud With AWS IoT

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Real-Time OPC-UA Tags Monitoring in the the Cloud With AWS IoT

Check out this post to learn more about real-time monitoring in the cloud with AWS IoT.

· IoT Zone ·
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Wouldn’t it be nice to see your field data in (near) real-time? This will allow you to be able to build powerful dashboard in matter of minutes to monitor you process, or start digitizing your visual management practices.

We all have of favorite OPC server depending on the task, but for me, it is Kepware without a doubt. No. I’m not sponsored; I just love it ! The nice thing about OPC server is usually that they cover way more communication protocols and allow us to connect to otherwise non OPC compatible machines.

Now, with all these collecting capabilities, we need to be able to store, analyze, and display such data. There is, of course, some powerful tool to do this, such as historians like Osi Pi, but what would be nice is if we could set up a scalable solution within minutes with unlimited integration, and that’s where this cloud solution comes handy.

Lucky for us, Kepware has got us covered and is already providing an IoT Gateway that will allow us to choose a tag that should be forwarded to the cloud.

It’s one thing to be able to send data to the cloud in near real-time, but what’s important is the ability to do stream processing of these data.

What is stream processing ? Well, it’s the inverse of your regular report generation that is running every X hour, or over night, you look through your data as it comes in over a time period and compute key metrics or detect events based on value changes

Above is an IoT and data stream analytics architecture example.

To achieve our dashboarding goal and real-time analytics, we will need to set up a set of AWS services like the Kinesis one shown in the illustration. I did a small change to this architecture by using an elastic search service and Kibana for the dashboarding.

Above is an Kinesis analytics overview.

Kinesis analytics is an important part of this solution as it will allow you to compute KPIs like MTBF or OEE in near real time easily. You could also decide to detect some events based on the stream of data and alert the right team in time to fix the issue and start performing predictive maintenance.

Above is an example dashboard produced with Kibana.

All our analytics will be streamed to an Elasticsearch service from AWS; this may not be the production tool you are looking for if you have a complex application and dashboarding need. But for monitoring data and putting together a powerful dashboard quickly, Kibana, which is on top of Elasticsearch, will do the work for you.

As part of running this architecture, I conducted a small experiment using the simulation mode of Kepware. The resulting visualization looks like this:

Yes, it is not as fancy as what you can find on the Kibana website, but it is a simple visualization of your field data in a near real-time way. The setup of the solution takes around one hour with the detailed steps and may be a bit more than two when trying it for the first time.

But in a matter of days, you could produce a set of dashboards to actually collect your data and process it visible from anywhere — with no installation needed aside from your OPC server.

I hope you enjoyed this introduction to AWS IoT/Stream analysis. I’m working on putting together a how-to series on the topic with more advanced configuration, like event detection and notification.

Stay tuned!

Topics:
iiot ,aws ,opc ,opc ua ,dashboard

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