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‎articles/iot/tutorial-iot-industrial-solution-architecture.md

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@@ -61,63 +61,6 @@ The following table describes the key components in this solution:
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> [!NOTE]
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> In a real-world deployment, something as critical as opening a pressure relief valve would be done on-premises. This is just a simple example of how to achieve the digital feedback loop.
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## UA Cloud Library
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To read OPC UA Information Models directly from Azure Data Explorer, you can import the OPC UA nodes defined in an OPC UA Information Model into a table. You can use the imported information for lookup of more metadata within queries.
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First, configure an Azure Data Explorer callout policy for the UA Cloud Library by running the following query on your Azure Data Explorer cluster. Before you start, make sure you're a member of the **AllDatabasesAdmin** role in the cluster, which you can configure in the Azure portal by navigating to the **Permissions** page for your Azure Data Explorer cluster.
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```kql
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.alter cluster policy callout @'[{"CalloutType": "webapi","CalloutUriRegex": "uacloudlibrary.opcfoundation.org","CanCall": true}]'
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```
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Then, run the following Azure Data Explorer query from the Azure portal. In the query:
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- Replace `<INFORMATION_MODEL_IDENTIFIER_FROM_THE_UA_CLOUD_LIBRARY>` with the unique ID of the Information Model you want to import from the UA Cloud Library. You can find this ID in the URL of the Information Model's page in the UA Cloud Library. For example, the ID of the station nodeset that this tutorial uses is `1627266626`.
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- Replace `<HASHED_CLOUD_LIBRARY_CREDENTIALS>` with a basic authorization header has of your UA Cloud Library credentials. Use a tool such as https://www.debugbear.com/basic-auth-header-generator to generate the hash. You can also use the following bash command: `echo -n 'username:password' | base64`.
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```kql
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let uri='https://uacloudlibrary.opcfoundation.org/infomodel/download/<INFORMATION_MODEL_IDENTIFIER_FROM_THE_UA_CLOUD_LIBRARY>';
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let headers=dynamic({'accept':'text/plain', 'Authorization':'Basic <HASHED_CLOUD_LIBRARY_CREDENTIALS>'});
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evaluate http_request(uri, headers)
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| project title = tostring(ResponseBody.['title']), contributor = tostring(ResponseBody.contributor.name), nodeset = parse_xml(tostring(ResponseBody.nodeset.nodesetXml))
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| mv-expand UAVariable=nodeset.UANodeSet.UAVariable
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| project-away nodeset
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| extend NodeId = UAVariable.['@NodeId'], DisplayName = tostring(UAVariable.DisplayName.['#text']), BrowseName = tostring(UAVariable.['@BrowseName']), DataType = tostring(UAVariable.['@DataType'])
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| project-away UAVariable
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| take 10000
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```
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To view a graphical representation of an OPC UA Information Model, you can use the [Kusto Explorer tool](/azure/data-explorer/kusto/tools/kusto-explorer). To render station model, run the following query in Kusto Explorer. For best results, change the `Layout` option to `Grouped` and the `Labels` to `name`:
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```kql
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let uri='https://uacloudlibrary.opcfoundation.org/infomodel/download/1627266626';
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let headers=dynamic({'accept':'text/plain', 'Authorization':'Basic <HASHED_CLOUD_LIBRARY_CREDENTIALS>'});
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let variables = evaluate http_request(uri, headers)
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| project title = tostring(ResponseBody.['title']), contributor = tostring(ResponseBody.contributor.name), nodeset = parse_xml(tostring(ResponseBody.nodeset.nodesetXml))
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| mv-expand UAVariable = nodeset.UANodeSet.UAVariable
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| extend NodeId = UAVariable.['@NodeId'], ParentNodeId = UAVariable.['@ParentNodeId'], DisplayName = tostring(UAVariable['DisplayName']), DataType = tostring(UAVariable.['@DataType']), References = tostring(UAVariable.['References'])
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| where References !contains "HasModellingRule"
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| where DisplayName != "InputArguments"
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| project-away nodeset, UAVariable, References;
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let objects = evaluate http_request(uri, headers)
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| project title = tostring(ResponseBody.['title']), contributor = tostring(ResponseBody.contributor.name), nodeset = parse_xml(tostring(ResponseBody.nodeset.nodesetXml))
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| mv-expand UAObject = nodeset.UANodeSet.UAObject
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| extend NodeId = UAObject.['@NodeId'], ParentNodeId = UAObject.['@ParentNodeId'], DisplayName = tostring(UAObject['DisplayName']), References = tostring(UAObject.['References'])
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| where References !contains "HasModellingRule"
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| project-away nodeset, UAObject, References;
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let nodes = variables
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| project source = tostring(NodeId), target = tostring(ParentNodeId), name = tostring(DisplayName)
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| join kind=fullouter (objects
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| project source = tostring(NodeId), target = tostring(ParentNodeId), name = tostring(DisplayName)) on source
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| project source = coalesce(source, source1), target = coalesce(target, target1), name = coalesce(name, name1);
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let edges = nodes;
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edges
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| make-graph source --> target with nodes on source
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```
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:::image type="content" source="media/concepts-iot-industrial-solution-architecture/station-graph.png" alt-text="Graph of the station Info Model." lightbox="media/concepts-iot-industrial-solution-architecture/station-graph.png" border="false" :::
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## Production line simulation
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The solution uses a production line simulation made up of several stations, using the station OPC UA information model, and a simple manufacturing execution system (MES). Both the stations and the MES are containerized for easy deployment.
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- i=418 - actual cycle time
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- i=434 - pressure
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## Digital feedback loop with UA Cloud Commander and UA Cloud Action
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### Digital feedback loop with UA Cloud Commander and UA Cloud Action
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The solution uses a digital feedback loop to manage the pressure in a simulated station. To implement the feedback loop, the solution triggers a command from the cloud on one of the OPC UA servers in the simulation. The trigger activates when simulated time-series pressure data reaches a certain threshold. You can see the pressure of the assembly machine in the Azure Data Explorer dashboard. The pressure is released at regular intervals for the Seattle production line.
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## Install the production line simulation and cloud services
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### Install the production line simulation and cloud services
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Select the **Deploy** button to deploy all required resources to your Azure subscription:
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Your VM is now ready to run the production line simulation.
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## Run the production line simulation
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### Run the production line simulation
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In the VM, open a Windows command prompt, enter *wsl*, and press **Enter**. Navigate to the `/mnt/c/ManufacturingOntologies-main/Tools/FactorySimulation` directory and run the **StartSimulation** shell script:
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> [!TIP]
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> To prevent WSL and K3s from automatically shutting down, keep your WSL command prompt open.
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## UA Cloud Library
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To read OPC UA Information Models directly from Azure Data Explorer, you can import the OPC UA nodes defined in an OPC UA Information Model into a table. You can use the imported information for lookup of more metadata within queries.
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First, configure an Azure Data Explorer callout policy for the UA Cloud Library by running the following query on your Azure Data Explorer cluster. Before you start, make sure you're a member of the **AllDatabasesAdmin** role in the cluster, which you can configure in the Azure portal by navigating to the **Permissions** page for your Azure Data Explorer cluster.
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```kql
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.alter cluster policy callout @'[{"CalloutType": "webapi","CalloutUriRegex": "uacloudlibrary.opcfoundation.org","CanCall": true}]'
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```
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Then, run the following Azure Data Explorer query from the Azure portal. In the query:
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- Replace `<INFORMATION_MODEL_IDENTIFIER_FROM_THE_UA_CLOUD_LIBRARY>` with the unique ID of the Information Model you want to import from the UA Cloud Library. You can find this ID in the URL of the Information Model's page in the UA Cloud Library. For example, the ID of the station nodeset that this tutorial uses is `1627266626`.
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- Replace `<HASHED_CLOUD_LIBRARY_CREDENTIALS>` with a basic authorization header has of your UA Cloud Library credentials. Use a tool such as https://www.debugbear.com/basic-auth-header-generator to generate the hash. You can also use the following bash command: `echo -n 'username:password' | base64`.
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```kql
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let uri='https://uacloudlibrary.opcfoundation.org/infomodel/download/<INFORMATION_MODEL_IDENTIFIER_FROM_THE_UA_CLOUD_LIBRARY>';
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let headers=dynamic({'accept':'text/plain', 'Authorization':'Basic <HASHED_CLOUD_LIBRARY_CREDENTIALS>'});
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evaluate http_request(uri, headers)
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| project title = tostring(ResponseBody.['title']), contributor = tostring(ResponseBody.contributor.name), nodeset = parse_xml(tostring(ResponseBody.nodeset.nodesetXml))
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| mv-expand UAVariable=nodeset.UANodeSet.UAVariable
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| project-away nodeset
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| extend NodeId = UAVariable.['@NodeId'], DisplayName = tostring(UAVariable.DisplayName.['#text']), BrowseName = tostring(UAVariable.['@BrowseName']), DataType = tostring(UAVariable.['@DataType'])
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| project-away UAVariable
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| take 10000
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```
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To view a graphical representation of an OPC UA Information Model, you can use the [Kusto Explorer tool](/azure/data-explorer/kusto/tools/kusto-explorer). To render station model, run the following query in Kusto Explorer. For best results, change the `Layout` option to `Grouped` and the `Labels` to `name`:
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```kql
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let uri='https://uacloudlibrary.opcfoundation.org/infomodel/download/1627266626';
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let headers=dynamic({'accept':'text/plain', 'Authorization':'Basic <HASHED_CLOUD_LIBRARY_CREDENTIALS>'});
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let variables = evaluate http_request(uri, headers)
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| project title = tostring(ResponseBody.['title']), contributor = tostring(ResponseBody.contributor.name), nodeset = parse_xml(tostring(ResponseBody.nodeset.nodesetXml))
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| mv-expand UAVariable = nodeset.UANodeSet.UAVariable
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| extend NodeId = UAVariable.['@NodeId'], ParentNodeId = UAVariable.['@ParentNodeId'], DisplayName = tostring(UAVariable['DisplayName']), DataType = tostring(UAVariable.['@DataType']), References = tostring(UAVariable.['References'])
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| where References !contains "HasModellingRule"
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| where DisplayName != "InputArguments"
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| project-away nodeset, UAVariable, References;
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let objects = evaluate http_request(uri, headers)
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| project title = tostring(ResponseBody.['title']), contributor = tostring(ResponseBody.contributor.name), nodeset = parse_xml(tostring(ResponseBody.nodeset.nodesetXml))
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| mv-expand UAObject = nodeset.UANodeSet.UAObject
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| extend NodeId = UAObject.['@NodeId'], ParentNodeId = UAObject.['@ParentNodeId'], DisplayName = tostring(UAObject['DisplayName']), References = tostring(UAObject.['References'])
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| where References !contains "HasModellingRule"
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| project-away nodeset, UAObject, References;
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let nodes = variables
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| project source = tostring(NodeId), target = tostring(ParentNodeId), name = tostring(DisplayName)
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| join kind=fullouter (objects
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| project source = tostring(NodeId), target = tostring(ParentNodeId), name = tostring(DisplayName)) on source
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| project source = coalesce(source, source1), target = coalesce(target, target1), name = coalesce(name, name1);
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let edges = nodes;
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edges
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| make-graph source --> target with nodes on source
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```
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:::image type="content" source="media/concepts-iot-industrial-solution-architecture/station-graph.png" alt-text="Graph of the station Info Model." lightbox="media/concepts-iot-industrial-solution-architecture/station-graph.png" border="false" :::
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## Deploy Azure IoT Operations on the edge
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Before you deploy, confirm that you started the production line simulation. Then, follow these steps in [Azure IoT Operations deployment details](/azure/iot-operations/deploy-iot-ops/overview-deploy).
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You can use VM you deployed earlier to run the production line simulation to deploy Azure IoT Operations.
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### Configure your Azure IoT Operations deployment
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You can configure your Azure IoT Operations deployment by using the [operations experience](https://iotoperations.azure.com/) web UI. Add the asset endpoints, assets and data flows to route and process the data from the production line simulation.
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<!-- TODO: Complete this section with details of require resources including:
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opc.tcp://assembly.munich/
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opc.tcp://test.munich/
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opc.tcp://packaging.munich/
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opc.tcp://assembly.seattle/
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opc.tcp://test.seattle/
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opc.tcp://packaging.seattle/
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-->
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## Use cases condition monitoring, OEE calculation, anomaly detection, and predictions in Azure Data Explorer
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To learn how to create no-code dashboards for condition monitoring, yield or maintenance predictions, or anomaly detection, see the [Azure Data Explorer documentation](/azure/synapse-analytics/data-explorer/data-explorer-overview). There's also a [sample dashboard](https://github.com/digitaltwinconsortium/ManufacturingOntologies/blob/main/Tools/ADXQueries/dashboard-ontologies.json) you can deploy. To learn how to deploy a dashboard, see [Visualize data with Azure Data Explorer dashboards > create from file](/azure/data-explorer/azure-data-explorer-dashboards#to-create-new-dashboard-from-a-file). After you import the dashboard, update its data source. Specify the HTTPS endpoint of your Azure Data Explorer server cluster in the top-right-hand corner of the dashboard. The HTTPS endpoint looks like: `https://<ADXInstanceName>.<AzureRegion>.kusto.windows.net/`.
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## Use Azure Managed Grafana service
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You can also use Azure Managed Grafana to create a dashboard on Azure for the solution described in this article. Use Grafana within manufacturing to create dashboards that display real-time data. The following steps show you enable Grafana on Azure, and create a dashboard with the simulated production line data from Azure Data Explorer and the Azure Digital Twins service.
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You can also use Azure Managed Grafana to create a dashboard on Azure for the solution described in this article. Use Grafana within manufacturing to create dashboards that display real-time data. The following steps show you enable Grafana on Azure, and create a dashboard with the simulated production line data from Azure Data Explorer.
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The following screenshot shows the dashboard:
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1. Select the *samplegrafanadashboard.json* file that you downloaded and select **Save**. You get an error on the page, because two variables aren't set yet. Go to the settings page of the dashboard.
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1. Select **Variables** and update the two URLs with the URL of your Azure Digital Twins service.
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1. Navigate back to the dashboard and hit the refresh button. You should now see data (don't forget to hit the save button on the dashboard).
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The location variable on the top of the page is automatically filled with data from Azure Digital Twins (the area nodes from ISA95). Here you can select the different locations and see the different datapoints of every factory.
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1. If data isn't showing up in your dashboard, navigate to the individual panels and see if the right data source is selected.
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### Configure alerts
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Within Grafana, it's also possible to create alerts. In this example, we create a low OEE alert for one of the production lines.
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Within Grafana, it's also possible to create alerts. In this example, we create a low OEE alert for one of the production lines.
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1. Sign in to your Grafana service, and select **Alert rules** in the menu.
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