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= Overview =
= Introduction =


There are multiple "flavors" of digital twins. The Platform Industrie 4.0 is putting the Asset Administration Shell at the core of its Digital Twin strategy <ref name="aashell" />. Many PLM companies are including the 3D CAD data as a part of the Digital Twin. Some advanced definitions of Digital Twin also include physics simulation. The [https://www.digitaltwinconsortium.org Digital Twin Consortium] defines digital twin as follows: ''"A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action. Digital twins use real-time and historical data to represent the past and present and simulate predicted futures. Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems"'' <ref name="dtdef" />.
There are multiple "flavors" of digital twins. The Platform Industrie 4.0 is putting the Asset Administration Shell at the core of its Digital Twin strategy <ref name="aashell" />. Many PLM companies are including the 3D CAD data as a part of the Digital Twin. Some advanced definitions of Digital Twin also include physics simulation. The [https://www.digitaltwinconsortium.org Digital Twin Consortium] defines digital twin as follows: ''"A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action. Digital twins use real-time and historical data to represent the past and present and simulate predicted futures. Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems"'' <ref name="dtdef" />.

Revision as of 20:03, 11 September 2021

More...More...More...More...More...More...Digital Twin 101

Digital Twins can be used in order to create a digital representation of the physical entities. They can help with managing complexity and establishing a semantic layer on top of the more technical layers. This in turn can make it easier to realize business goals and implement AI/ML solutions using machine data. The following provides an overview, some concrete examples, as well as a discussion in which situations the Digital Twin approach should be considered for an AIoT initiative.

Introduction

There are multiple "flavors" of digital twins. The Platform Industrie 4.0 is putting the Asset Administration Shell at the core of its Digital Twin strategy [1]. Many PLM companies are including the 3D CAD data as a part of the Digital Twin. Some advanced definitions of Digital Twin also include physics simulation. The Digital Twin Consortium defines digital twin as follows: "A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action. Digital twins use real-time and historical data to represent the past and present and simulate predicted futures. Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems" [2].

In The AIoT Playbook we are building on the definition of the Digital Twin Consortium. A key benefit of the Digital Twin concept is to manage complexity via abstraction. Especially for complex, heterogeneous portfolios of physical assets, the Digital Twin concept can help to better manage complexity by providing a layer of abstraction, e.g. through well-defined Digital Twin interfaces and relationships between Digital Twins. Both the I4.0 AdminShell as well as the Digital Twins Definition Language (DTDL)[3] are providing support in this area.

Depending on the approach chosen, Digital Twin interface definitions often extend the concept of well-established component API models by adding Digital Twin specific concepts like telemetry events and commands. Relationships between Digital Twin instances can differ. A particularly important one is the aggregation relationship, because this will often be the foundation of managing more complex networks of heterogeneous assets.

The goal of many Digital Twin projects is to create semantic models, which allow to better understand the meaning of information. Ontologies are a concept where re-useable, industry-specific libraries of Digital Twin models are created and exchanged to support this.

Digital Twin - Overview

Example

A good example for a Digital Twin is a system which makes route recommendations to drivers of electric vehicles, including stop points at available charging stations. For these recommendations, the system will need a representation of the vehicle itself (including charging status), as well as the charging stations along the chosen route. If this information is logically aggregated as a Digital Twin, the AI in the backend can then use this DT in order to make the route calculation, without having to worry about technical integration with the vehicle and the charging stations in the field.

Similarly, the feature responsible for reserving a charging station after a stop has been selected can benefit if the charging station is made available in the form of a Digital Twin - allowing to make the reservation without having to deal with the underlying complexity of the remote interaction.

The Digital Twin in this case is providing a higher level of abstraction than would be made available, for example, via a basic API architecture. This is especially true if the Digital Twin is taking care of data synchronization issues.

Digital Twin Example

Digital Twin and AIoT

In an AIoT initiative, the Digital Twin concept can play an important role in providing a semantic abstraction layer. IoT plays the role of providing connectivity services. AI, on the other hand, can play two roles:

  • Reconstruction: AI can be an important tool for the reconstruction process, i.e. the process which is reconstructing the virtual representation based on the raw data from the sensors.
  • Application: Once the Digital Twin is reconstructed, AI and be applied to the semantically rich representation of the Digital Twin to support the business goals
Digital Twin and AIoT

Example 1: Electric Vehicle

The first example to demonstrate this concept is building on the EV scenario from earlier on. In addition, the DT concept is now also applied to the Highly Automated Driving Function of the vehicle, which includes short term trajectory and long term path planning.

For the short term planning, a digital twin of the vehicle surroundings is created (here, the AI is supporting the reconstruction of the DT). Next, an AI is using the semantically rich interfaces of the digital twin of the vehicle surroundings to perform the short term trajectory planning. This AI will also take the long-term path into consideration, e.g. to determine necessary turns on a highway.

Digital Twin and AIoT - Example

Example 2: Particle Collider

The second example is a particle collider, such as the Large Hadron Collider at CERN.

The particle collider is using a 3D grid of ruggedized radio activity sensors in a cavern of the collider to capture radio activity after the collision. This data is fed into a hugely complex tier of compute nodes, which are applying advanced analytics concepts in order to create a digital reconstruction of the particle collision. This Digital Twin is then the foundation of the analysis of the physical phenomena that could be observed.

AIoT & Digital Twin: Particle Collider Example

DT Resolution and Update Frequency

As mentioned earlier, key questions which must be answered by the solution architect concern the DT resolution and update frequency.

A good example here is a DT for a soccer game. Depending on the role of the different stakeholders, they would have different requirements regarding resolution and update frequency. For example, a betting office might only need the final score of the game. The referee (well, plus everybody else playing or watching) needs more detailed information about whether the ball has actually crossed the line of the goal, in case of a shot on the goal. The audience usually wants an even higher "resolution" for the Internet live feed, including all significant events (goals, fouls, etc.). The team coach might require a detailed heat map of the position of each player during every minute of the game. And finally the team physician wants additional information about the biorhythm of each player during the entire game.

Digital Twin: Soccer Example

Mark Haberland is the CEO of Clariba. The company is offering custom tracking and analytics solutions for soccer teams. He is sharing the following insights with us: Football clubs around the world are striving to achieve a competitive advantage and increase performance using the immense data available from the use of digital technology in every aspect of the sport. Continued innovation in the application of sensors, smart video analytics with edge computing, drones, and even robotics, streaming data via mesh WIFI networks and 5G connectivity is providing incredible new capabilities and opportunities for real-time insights. Harnessing this ever-increasing amount of data will allow forward looking clubs to experiment, to innovate and develop new algorithms to achieve the insights needed to increase player and team performance to win on the pitch. Investing in new technologies, building in-house capabilities and co-innovating with partners such as universities, and specialized companies savvy in AIoT will be a differentiating factor for Football organisations that want to lead the way.

Football dashboard example

So there are a number of key questions here: How high should the resolution of the DT be? And how can a combination of sensors and reconstruction algorithm deliver on this resolution?

For the individual goal recognition, a dedicated sensor could be embedded in the ball, with a counterpart in the goal posts. This would require a modification of the ball and the goals posts, but would allow for very straight-forward reconstruction, e.g. via a simple rule.

Things become more complicated for the reconstruction process if a video camera is used instead. Here, AI/ML could be utilized, e.g. for goal recognition.

For the biorhythm, chances are that a specialized type of sensor will be somehow attached to the player's body, e.g. in his shorts or t-shirt. For the reconstruction process, probably advanced analytics will be required.

Digital Twin: Soccer Example (Details)

Advanced Digital Twins: Physics Simulation and Virtual Sensors

Physics Simulation and Virtual Sensors

References