The 3 Types of Digital Twin Maturity Models
by Exosite, on June 6, 2017
Maturity models for digital twins can be classified based on the level of information known about an asset and its environment (maturity). The maturity model shows how a digital twin can improve over time as more data (and derivative knowledge) is accumulated.
The following describes a maturity model for digital twins and ways to classify them based on the level of information known about an asset and its environment (maturity). The maturity model shows how a digital twin can improve over time as more data (and derivative knowledge) is accumulated. The remainder of this section describes the tiers or types of twins that can be utilized, and the qualities and constraints that delimit each type.
Digital Twin Maturity
The scope of data that is recorded and retained within a digital twin determines what can be known about an asset’s state and condition.
Partial
The minimal digital twin typically contains a small number of data sources, such as temperature, pressure, and device state. Partial digital twins can be useful to monitor a key metric or state from a low-power or resource-constrained asset, such as a connected light bulb that simply reports its current power state. This level is also seen in proof-of-connectivity development, as it enables quick development of device-to-platform functionality.
A partial digital twin contains enough data sources to create derivative data. For example, if pressure is down but temperature is up, and linear regression identifies a correlation, a corresponding inference about the health of the asset can be made.
Clone
The clone form of a digital twin contains all meaningful and measurable data sources from an asset. This level is applicable when a connected asset is not power- or data-constrained, and is useful in prototyping and data characterization phases of IoT development.
Augmented
The augmented digital twin enhances the data from the connected asset with derivative data, correlated data from federated sources, and/or intelligence data from analytics and algorithms.
Read the full white paper describing the digital twin and how best to go about creating them for your IoT devices by clicking below. If you want to connect your devices online and start modeling, feel free to try out our enterprise IoT platform, Murano.