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Modeling the Digital Twin

by Exosite, on May 23, 2017

Digital twins are all the rage in technology at the moment, especially featured at this year’s Hannover Messe. We’ve covered what a digital twin is and why it’s important, so now we’ll get into how to mold it into what you need it to be.

To begin modeling an asset in an IoT solution, it may be tempting to pick up a development board or measurement device, inspect its array of sensors and outputs, and reflect these attributes into the properties of a digital twin.

However, instead of centering the model of a digital twin on the development board, measurement device, or sensor, think instead of the entire asset being measured (like an air compressor system for example). This is comparable to a blood pressure cuff on a patient in a doctor’s office; what is being measured isn’t the sensor or measurement device itself, but a particular dimension of the patient’s overall health and well-being.

In thinking about digital twins in this way, it becomes natural to enhance and augment the model with further metadata, nearby environmental conditions, maintenance data from similar equipment, service history, account information for the company that owns it, manufacturing born-on data and related configurations, and data from other web services that together can create a rich and comprehensive representation of the physical device.

There are 4 things to keep in mind when developing a digital twin that incorporate additional sources of data:

  1. Modeling the asset, not the sensors.
  2. Separating condition from control.
  3. Leveraging data to provide meaningful views.
  4. Oversample first, optimize second.

To read the full white paper describing the digital twin concept and how best to go about creating them for your connected devices, click below. If you want to get your devices online and start modeling, feel free to try out our enterprise IoT platform, Murano.

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