What Is IoT TwinMaker?

AWS IoT TwinMaker is designed to create real-time digital representations of physical systems, allowing for continuous updates that assist operators in monitoring factories, buildings, or industrial facilities.

Understanding Digital Twins

The concept of a Digital Twin is relatively new, and extensive efforts are underway to define it precisely. A thorough review of 93 papers was conducted in 2020 (Jones et al., 2020), followed by an even broader examination of 150 papers in 2021 (Semeraro et al., 2021).

Essential components of a digital twin typically include:

• A physical object or system
• A model representing the object or system
• A continuously evolving dataset related to the object
• A mechanism for dynamically updating the model based on the data

While not universally agreed upon, many consider the following an essential requirement:

• A way to update the physical object or system based on the model

According to IBM's definition, a digital twin is:

"A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to assist in decision-making." (IBM, 2022)

In a 2021 study (Semeraro et al., 2021), researchers defined a digital twin as:

"A set of adaptive models that emulate the behavior of a physical system in a virtual setting, continuously updated with real-time data throughout its lifecycle. The digital twin mirrors the physical system to forecast failures and identify opportunities for improvement, offering real-time recommendations for optimization or risk mitigation."

Below is a visual representation illustrating the connection between digital and physical twins.

Diagram by the Author. Based on (Jones et al., 2020) Figure 7

For further insights, refer to "What is (and what isn’t) a Digital Twin?"

AWS TwinMaker Overview

AWS IoT TwinMaker is an AWS IoT service that facilitates the development of operational digital twins for both physical and digital systems. It generates digital visualizations using data collected from various real-world sensors, cameras, and enterprise applications, enabling users to monitor their physical operations effectively.

A digital twin serves as a live digital representation of a system, encompassing all its physical and digital elements. It is continuously updated with data to accurately reflect the structure, status, and behavior of the system, ultimately driving business results.

Users interact with the data from their digital twin through a dedicated user interface application.

Concepts of AWS IoT TwinMaker

WORKSPACE:

In AWS IoT TwinMaker, a Workspace acts as the primary container for any Digital Twin application.

ENTITY:

The physical system is modeled using an entity-component model structured as a graph. Entities represent physical equipment, concepts, or processes, and each entity encompasses a collection of associated Components that provide measurements and static data.

COMPONENTS:

Components deliver both static context and dynamic data for an entity. Common built-in Component types include:

• Document
• Time series
• Alarm
• Video
• Custom Visualization

AWS IoT TwinMaker offers visualization tools that enhance 3D models with real-time data alongside traditional dashboards.

SCENES:

A scene is a 3D representation augmented with real-time data, developed using either glTF (GL Transmission Format) or GLB format 3D models. Multiple scenes can be integrated into a workspace.

DASHBOARDS:

AWS IoT TwinMaker features integration with Grafana (also available as an AWS managed service) for creating dashboard visualizations.

VIDEO:

This service allows video streams to be incorporated into TwinMaker scenes and Grafana dashboards.

Availability and Future Directions

Currently, the IoT TwinMaker service is only available in select regions:

• Europe (Ireland)
• Asia Pacific (Singapore)
• US East (North Virginia)
• US West (Oregon)

As mentioned earlier, IoT TwinMaker is in public preview, and its features and interfaces may evolve in future updates.

Summary & Conclusion

AWS IoT TwinMaker presents an intriguing service that indicates AWS's intentions to expand into areas traditionally dominated by industrial automation vendors. However, questions arise as to whether this service genuinely enables the creation of Digital Twins, given that standard definitions require a physics-based model of physical objects. While AWS IoT TwinMaker provides visualization capabilities, it lacks explicit support for model development.

The product strategy seems somewhat ambiguous, with notable overlaps between IoT TwinMaker and SiteWise. Given that this service primarily offers visualization, it raises concerns about its unique contributions compared to existing web-based SCADA systems.

Furthermore, the target audience for this product remains unclear, and the absence of configuration tools may pose significant challenges. Creating visualizations of industrial operations typically falls to process automation engineers, who may not feel comfortable navigating a JSON-based configuration.

Industrial processes can involve tens, hundreds, or even millions of data points, raising questions about the management of such vast amounts of information without adequate tools.

Lastly, the terminology concerning Entities and Components could benefit from alignment with ISA 88 and ISA 95 standards, which provide a standardized modeling language for industrial facilities.

Exploration Opportunities

If you're interested in a more detailed review of this service, including tutorials, please leave a comment below. Additionally, check out iTwins.js, an open-source data platform for federating and visualizing infrastructure, which serves as a potential foundation for constructing a Digital Twin.

To Learn More

Thank you for reading; I hope you found this article informative. For additional insights, consider exploring:

• "What is (and what isn’t) a Digital Twin"
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References

Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52.

Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021). Digital twin paradigm: A systematic literature review. Computers in Industry, 130, 103469.