Building a Hierarchy of Digital Twins

Especially when modeling or tracking complex systems, digital twin models can be organized in a hierarchy at multiple levels of abstraction, from device handling to strategic analysis and control. Consider a streaming analytics application that analyzes telemetry from the components of a wind turbine. This application can receive telemetry from each component and combine this with relevant contextual data, such as the component’s specifications, service history, and dynamic state, to enhance its ability to predict impending failures. The following diagram illustrates how the digital twin model correlates telemetry from three components of a hypothetical wind turbine (blades, generator, and control panel) and delivers it to associated real-time digital twin instances, where message-processing code analyzes the telemetry and generates feedback and alerts:

In this hypothetical example, the blades and generator work together to generate power managed by the control panel. Taking advantage of a hierarchical organization illustrated below, the real-time digital twin instances for the blades and generator could feed telemetry to a higher-level real-time digital twin instance called the Blade System that manages the rotating components within the tower and their common concerns, such as avoiding over-speeds, while not dealing with the detailed issues of directly managing these two components. Likewise, the real-time digital twin instance for the blade system and the control panel feed telemetry to a yet higher-level instance called Master Control, which coordinates the overall wind turbine’s operation and generates alerts as necessary. Note that the Blade System and Master Control instances are defined by the hierarchy and are not “twins” of physical data sources.

By partitioning the application into a hierarchy of digital twin models, the code can be modularized and thereby simplified with a clean separation of concerns and well-defined interfaces for testing.