Digital Twins
Note: To access the Digital Twins feature, you need a Scale or Growth Litmus license.
The Digital Twins feature allows you to create virtual representations of industrial assets, so that you can monitor, analyze, and optimize the performance of physical objects. Digital Twins are used throughout the product lifecycle to simulate, predict, and optimize the product and production system before investing in physical prototypes and assets. They are also used to monitor the performance of physical assets in real-time and to optimize operations, so that you can use it for monitoring and maintenance activities such as insights, alerts, and feedback loop.
When setting up Digital Twins models, you will do the following:
- Configure static attributes that represent the meta information of the Digital Twin.
- Configure transformation rules of input data in JSON to the desired JSON format. This format can contain XPath, or it can use $key.
- Define the hierarchy of the resulting object.
- Optionally, you can import and export the model from a JSON file. You can also clone the model as needed.
- While updating the model, keep in mind that the version of the model updates accordingly.
When setting up Digital Twins instances, you will do the following:
- Select the time interval to publish data to the message broker. During the interval, values are collected and transformed according the rules configured in the model before publishing.
- Update static attribute values as needed.
- Set up input topics for dynamic attributes.
Once the instance is set up, you can start and stop it as needed. If the model associated with the instance is updated, you will have to manually upgrade the instance as needed with the updated configuration of the model.
Models are the data structures for Digital Twin instances. You can configure the following parameters in a model.
- Attributes: Models store the dynamic and static attributes that will be used by instances for identification and data collection.
- Dynamic attributes: Variable data for the model. For example, Temperature, Vibration, and Downtime.
- Static attributes: Metadata of the model. For example, Device ID, Serial, and Model.
- Transformations: You can transform the data collected in instances by converting raw JSON data to a desired format by defining the JSON-based schema.
- Schema: Provides a logical representation of dynamic and static attributes. You can drag and drop attributes into the hierarchy as needed and add folders to create a custom data structure.
Instances are the physical objects that contain static and dynamic data from different sources. Every instance has to be associated with a model. The attributes configured in the model determine the data that will be collected by the instance.
Every instance generates a new data topic that is published to the internal message broker. This data topic can be leveraged within Litmus Edge.
You can configure instances by doing the following:
- Modify the configurations of static attributes in the instance.
- Map one-to-one relationships with data topics and dynamic attributes by configuring topic names for dynamic attributes in the instance.
- Use the Data Access tab to view real-time data.
- Send instance data to northbound integrations by referring to the Data Access topic name.
See Use Digital Twins to Contextualize Data to review how to leverage Digital Twins.
To access Digital Twins:
- Log in to Litmus Edge.
- From the Navigation panel, select Digital Twins. The Models pane appears.
