AWS IoT TwinMaker
A service that builds digital twins of physical facilities, integrating 3D scenes with real-time data for equipment status monitoring and simulation
Overview
AWS IoT TwinMaker is a service that virtually recreates physical spaces such as factories, buildings, and industrial facilities as digital twins. It integrates heterogeneous data sources - 3D models, IoT sensor data, video feeds, maintenance history - to visualize equipment status in real time. The entity-component model flexibly defines equipment hierarchy and attributes, with integrated 3D scene and data dashboard display available through Grafana plugins or custom web applications.
Entity-Component Model and Workspace Design
TwinMaker's data model consists of three layers: Workspaces (logical containers per project), Entities (representations of physical objects), and Components (data connections attached to entities). For example, within a "Factory A" workspace, you create a "Pump A-001" entity and attach components for temperature/pressure data from IoT SiteWise and camera footage from Kinesis Video Streams. 3D model files are kept in the resource library in the S3 bucket linked to the workspace and are used in scenes. Pre-defining component types enables reuse of data connection patterns when instantiating large numbers of identical equipment. Entities can be organized into custom hierarchies through parent-child relationships, so a structure like factory → floor → line → machine becomes the hierarchy of the digital twin.
Integrated Display of 3D Scenes and Real-Time Data
TwinMaker's Scene Composer imports 3D models (glTF and other formats) to visually recreate equipment layouts. Data overlays such as tags (color-coded temperature display, alarm status icons, numeric labels, and so on) are set at locations in the 3D scene to tie sensor data and other data to equipment locations. Using the TwinMaker plugin for Grafana, you can place 3D scene panels and time-series graph panels on the same dashboard, building a linked UI where clicking equipment in the 3D model displays corresponding sensor data graphs. AWS provides this plugin for both Grafana and Amazon Managed Grafana.
Applications in Simulation and Predictive Maintenance
TwinMaker's digital twin can serve not only as a visualization of current state but also as a foundation for future state prediction and simulation. Inference results from predictive maintenance models built with SageMaker can be attached as components to entities, displaying prediction information like "this equipment has an 85% probability of failure within 72 hours" on the 3D scene. Another configuration runs real-time anomaly detection with Flink applications and reflects the results in TwinMaker alarm components. For simulating equipment layout changes or new equipment additions, one approach is to combine TwinMaker with tools from AWS Partners that provide simulation. For building management, one option is to convert BIM (Building Information Modeling) and similar files to glTF format before bringing them into TwinMaker.
References (Official AWS Resources)
The primary sources for this page are the official AWS website and documentation. Check the official pages below for the latest specifications and pricing.
- AWS IoT SiteWise official page
- AWS IoT Core official page
- Amazon Managed Grafana official page
- AWS Documentation (official)
If this page and the official documentation disagree, treat the official documentation as authoritative.