Create Digital Twins of Real-world Systems More Quickly, Easily

Amazon Web Services Inc. (AWS), an Amazon.com Inc. company, has announced the general availability of AWS IoT TwinMaker, a new service that makes it faster and easier for developers to create digital twins of real-world systems like buildings, factories, industrial equipment, and production lines.

Amazon Web Services Inc. (AWS), an Amazon.com Inc. company, has announced the general availability of AWS IoT TwinMaker, a new service that makes it faster and easier for developers to create digital twins of real-world systems like buildings, factories, industrial equipment, and production lines.

Digital twins are virtual representations of physical systems that use real-world data to mimic the structure, state, and behavior of the objects they represent and are updated with new data as conditions change. AWS IoT TwinMaker makes it easy for developers to integrate data from multiple sources like equipment sensors, video cameras, and business applications—and combines that data to create a knowledge graph that models the real-world environment. With AWS IoT TwinMaker, many more customers can use digital twins to build applications that mirror real-world systems that improve operational efficiency and reduce downtime. There are no upfront commitments or fees to use AWS IoT TwinMaker, and customers only pay for accessing the data used to build and operate digital twins. To get started with AWS IoT TwinMaker, visit aws.amazon.com/iot-twinmaker.

Industrial companies collect and process vast troves of data about their equipment and facilities from sources like equipment sensors, video cameras, and business applications (e.g., enterprise resource planning systems or project management systems). Many customers want to combine these data sources to create a virtual representation of their physical systems (called a digital twin) to help them simulate and optimize operational performance. But building and managing digital twins is hard even for the most technically advanced organizations. To build digital twins, customers must manually connect different types of data from diverse sources (e.g., time-series sensor data from equipment, video feeds from cameras, maintenance records from business applications, etc.). Then customers have to create a knowledge graph that provides common access to all the connected data and maps the relationships between the data sources to the physical environment. To complete the digital twin, customers have to build a 3D virtual representation of their physical systems (buildings, factories, equipment, production lines, etc.) and overlay the real-world data on to the 3D visualization—and then ensure the digital twin is kept up to date as conditions change. Once they have a virtual representation of their real-world systems with real-time data, customers can build applications for plant operators and maintenance engineers who can leverage machine learning and analytics to extract business insights about the real-time operational performance of their physical systems. Because the work required is complex, the vast majority of organizations are unable to use digital twins to improve their operations.

AWS IoT TwinMaker makes it significantly faster and easier to create digital twins of real-world systems. Using AWS IoT TwinMaker, developers can get started quickly building digital twins of devices, equipment, and processes by connecting AWS IoT TwinMaker to data sources like equipment sensors, video feeds, and business applications. AWS IoT TwinMaker contains built-in connectors for Amazon Simple Storage Service (Amazon S3), AWS IoT SiteWise, and Amazon Kinesis Video Streams (or customers can add their own connectors for data sources like Amazon Timestream, Snowflake, and Siemens MindSphere) to make it easy to gather data from a variety of sources. AWS IoT TwinMaker automatically creates a knowledge graph that combines and understands the relationships of the connected data sources, so it can update the digital twin with real-time information from the system being modeled. Customers can import existing 3D models (e.g., CAD and BIM files, point cloud scans, etc.), directly into AWS IoT TwinMaker to easily create 3D visualizations of the physical system and overlay the data from the knowledge graph on to the 3D visualizations to create the digital twin. Once the digital twin has been created, developers can use an AWS IoT TwinMaker plugin for Amazon Managed Grafana to create a web-based application that displays the digital twin on the devices plant operators and maintenance engineers use to monitor and inspect facilities and industrial systems. For example, developers can create a virtual representation of a metals processing plant by associating data from the plant’s equipment sensors with real-time video of the various machines in operation and the maintenance history of those machines. Developers can then set up rules to alert plant operators when anomalies in the plant’s furnace are detected (e.g., temperature threshold has been breached) and display those anomalies on a 3D representation of the plant with real-time video from the furnaces, which can help operators make quick decisions on predictive maintenance before a furnace fails.

AWS IoT TwinMaker is generally available in the Eastern and Western regions of the U.S., Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Frankfurt), and Europe (Ireland) with availability in additional AWS Regions coming soon.

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