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:
Digital twin technology is being increasingly adopted across a wide range of industries, including:
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.
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.
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.
Testing a digital twin implementation is vital for several reasons:
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.
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.
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]