What is digital twin and how to test Digital Twin Applications?

What is Digital Twin?

A digital twin is a virtual representation of a physical object, process, or system. It allows businesses to simulate and analyze the system’s real-time performance. As a result, it can help companies to improve the design of new products, services, and business models, optimize the performance of existing systems, and reduce costs. In simple terms, a digital twin is like a virtual copy of a real-world object, design, or process that can be used for testing, monitoring, and analyzing its performance in a safe and controlled environment. The term Digital Twin might be new, but the concept of Digital Twin dates back to as old as Apollo 11 Program. The difference is at that time, NASA had an actual physical twin of the spacecraft, i.e., the actual physical copy of the spaceship on earth reflecting the remote spaceship’s state.

Today, businesses are using digital twins for:

Design and development: Digital twins can simulate and test new products and systems before they are built in the physical world. An example is NVIDIA Omniverse which is used to construct a digital factory. This helps identify and address potential issues early on, leading to improved design and development.
Optimization: Digital twins can be used to monitor and analyze the performance of existing systems in real time. This can help businesses to identify areas for improvement and optimize the performance of the actual systems.
Predictive maintenance: Digital twins can predict when equipment or systems will need maintenance, allowing businesses to schedule maintenance conveniently on demand and reduce downtime.
Cost savings: By using digital twins, businesses can reduce the need for physical prototypes and testing. This can lead to cost savings and improved efficiency.
Safety and risk management: Digital twins can be used to simulate and test scenarios that would be difficult or impossible to replicate in the physical world, such as extreme conditions or emergency situations. This can help businesses to identify and address potential safety hazards and risks.
Remote collaboration: Digital twins can be accessed remotely, allowing teams to collaborate and work together more effectively, regardless of location.

Impact of Digital Twin across Industry

Digital twin technology is being increasingly adopted across a wide range of industries, including:

Manufacturing: Digital twin technology is being used in manufacturing to simulate and test the performance of new products and systems. It can also be used to optimize the performance of existing systems and to predict when equipment will need maintenance.
Aerospace and defense: Digital twin technology is being used in aerospace and defense to simulate and test the performance of new aircraft and spacecraft. It can also be used to optimize the performance of existing systems and to predict when equipment will need maintenance.
Automotive: Digital twin technology is being used in automobiles to simulate and test the performance of new vehicles. It can also be used to optimize existing systems’ performance and predict when equipment will need maintenance.
Healthcare: Digital twin technology is being used in healthcare to simulate and test the performance of new medical devices and equipment. It can also be used to optimize existing systems’ performance and predict when equipment will need maintenance.
Building and construction: Digital twin technology is being used in building and construction to simulate and test the performance of new buildings and structures. It can also be used to optimize existing systems’ performance and predict when equipment will need maintenance.
Energy and utilities: Digital twin technology is being used in energy and utilities to simulate and test the performance of new power generation systems and to optimize the performance of existing systems.

As digital twin technology continues to advance, it is expected to play an even more significant role in product development and testing in these industries and many others, as well as in other areas such as Smart City, Smart Grid, and IoT-enabled systems.

Digital Twin Architecture

A digital twin application consists of many subcomponents. The data originating from actual physical asset is sent via network to a cloud platform, which analyses the streaming data to update the digital model. The data coming from sensors is fused with Process Engineering, Asset information, 3D CAD/CAM models, and exposed via APIs to other interested applications. The service management integration ensures that twin could automatically raise service request when a predictive maintenance alert is raised. A dashboard/UI application provides a human interface to show the status of the digital twin and allow control actions for actual physical asset. Lastly, a simulation platform like IOTIFY could directly interface with cloud platform to mimic data coming from physical devices, thereby reducing hardware dependencies for faster development and test.

Digital twin standardization and initiatives

There are various standardization activities in progress related to digital twins. For example, Aspect Object technology, standardized in IEC-81346, defines the so-called aspects needed to structure information related to various views (eg, product, function or location) of an industrial system. IEC 62832 defines a digital factory framework with the representation of the factory’s assets at its center, although this representation is not called a digital twin. Microsoft has announced a new framework for defining digital twins with DTDL

Over the past few years, more initiatives have appeared: IEEE P2806 aims to define the system architecture of digital representations of physical objects in factory environments, focusing on connectivity requirements and industrial artificial intelligence data attributes. Likewise, ISO/AWI 23247 drives the use of digital twins for manufacturing by defining a reference architecture.

While companies often offer digital twins as isolated solutions, many use cases could benefit from interactions between digital twins from different vendors. The German platform “Platform Industrie 4.0” launched Asset Administration Shell as the industrial digital twin for smart manufacturing to foster interoperability across the value stream.

As well as the IIC and Platform Industrie 4.0, other groups exist – for example, the Industrial Digital Twin Association (a user organization for Platform Industrie 4.0 with open source intentions); the Digital Twin Consortium, which drives consistency in vocabulary, architecture, security and interoperability; the Open Manufacturing Platform, which aims to offer platform-agnostic solutions; and the GAIA-X project, with interoperability at the level of information models and the digital twins as a cornerstone of its vision.

Why test Digital Twin implementations?

Testing a digital twin implementation is vital for several reasons:

Functional Validation: Testing a digital twin implementation helps to ensure that the virtual model accurately represents the physical system or product. This can help to identify and address any discrepancies between the virtual and physical models.
Digital Twin Performance: Testing a digital twin implementation can help evaluate the virtual model’s performance and identify any potential issues or limitations. This can help to improve the overall performance of the actual system or product.
Reliability: Testing a digital twin implementation can help evaluate the virtual model’s reliability and identify any potential issues or limitations. This can improve the overall reliability of the actual system or product.
Scalability: Testing a digital twin implementation can help evaluate the virtual model’s scalability and identify any potential issues or limitations. This can help to improve the overall scalability of the actual system or product.
Integration: Testing a digital twin implementation can help evaluate the virtual model’s integration with existing systems and identify any potential issues or limitations.
Compliance: Testing a digital twin implementation can help to evaluate if the virtual model meets the standards and regulations for the industry and identify any potential issues or limitations.
Continuous Improvement: Testing a digital twin implementation can help to identify areas of improvement and continuously improve the overall performance, reliability, scalability, integration, and compliance of the existing system or product.

Digital Twin Testing Techniques

Model-based testing: This approach uses a mathematical model of the system to simulate its behavior. The model is then used to generate test cases and test the virtual twin. This approach is useful for testing the functionality and performance of the system, but it can be limited by the accuracy of the model.
Simulation-based testing: This approach uses a computer simulation of the system to simulate its behavior. The simulation is then used to generate test cases and test the virtual twin. This approach is useful for testing the functionality and performance of the system, but it can be limited by the accuracy of the simulation.
Emulation-based testing: This approach uses a hardware or software emulation of the system to simulate its behavior. The emulation is then used to generate test cases and test the virtual twin. This approach is useful for testing the functionality and performance of the system, but the accuracy of the emulation can limit it.

Comparison of Testing Techniques

Each of these techniques has its own advantages and limitations, depending on the complexity and nature of the system. Selecting the appropriate technique will depend on the specific requirements of the system, the available resources, and the goals of the testing. A hybrid approach, i.e. a combination of above mentioned techniques can also be used for Digital twin testing, for example Model-based testing for system performance and Simulation-based testing for environment testing, this can provide a comprehensive testing and validation of the digital twin. With tools like iotify.io, we are able to customize the test environment based on the test needs.

Challenges in testing Digital Twins

Though Digital Twin seems simple, there implementation may involve several vendors and interfaces across different technology stack. A comprehensive end to end test environment for Digital Twin testing is complex because of the following reasons: –

Data availability and quality: One of the main challenges in digital twin testing is ensuring that the data used to create the virtual model is accurate and of high quality. If the data is inaccurate or incomplete, it can lead to errors or inaccuracies in the virtual model and in the testing results. This may further be complicated due to non access of production environment.
Integration with existing systems: Digital twin testing can be challenging when it comes to integrating the virtual model with existing systems. This can include issues related to data transfer, communication, and compatibility.
Scalability: Digital twin testing can be challenging when it comes to scaling the virtual model to handle large amounts of data and to test the system under different scenarios.
Standards and regulations: Digital twin testing can be challenging to ensure that the virtual model meets the standards and regulations for the industry. This can include issues related to data privacy, security, and compliance.
Complexity: Digital twin testing can be challenging when it comes to dealing with complex systems and processes, for example, testing multi-component systems, testing systems with multiple interactions, and testing systems in real-time.
Computational resources: Digital twin testing can be challenging when it comes to the computational resources required to run the virtual model, this can be an issue for large and complex systems, and also for real-time testing.
Interpretation of results: Digital twin testing can be challenging when it comes to interpreting the results of the testing, especially when dealing with large amounts of data and complex systems.
Test Automation: Since testing Digital Twins involves usage of multiple tools, integrating them with each other is also quite challenging.

Digital Twin Testing Tools

IoTIFY: IoTIFY is industry’s leading IoT QA platform designed for Functional and performance testing of IoT cloud platforms. Due to it’s flexible design and capabilities, IoTIFY is best suited to test any digital twin application. For a comprehensive overview of IoTIFY’s capabilities, please visit iotify.io or check the documenation at docs.iotify.io

JMeter: JMeter is one of the oldest tool, originally designed for Web testing. However, due to support of protocols such as MQTT, CoAP and REST, it could also be utilized to test Digital Twins.

Custom Scripts: A python or Javascript based framework could be used to simulate end to end messaging flows in Digital Twins, however, automation and result analysis gets quite difficult with such frameworks.

Conclusion

Digital Twin enables seamless innovation in business application with predictive analytics, process improvement and asset utilization. Developing digital twins is also a quite complex process due to interaction with several technologies and frameworks. In order to test digital twins, a comprehensive platform is required. Digital twins could be tested with Model Based, Simulation based or Emulation based approach, however in real world use cases, a hybrid approach yields the best results. In order to learn more about Digital Twin testing, please contact us at [email protected]