Essential Digital Twins for Modern Industry

Introduction

Digital Twins concept stay more and more popular last several year and has different points of view in modern industry.
In a set of articles I will try to explain the digital twins concept, business cases, main platforms and after that will go deep into 
Digital Twins for IoT, AI and especially Azure Digital Twins Service – the Microsoft Digital Twins PaaS, realized on Microsoft Azure

State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) – all part of Industry 4.0 are one of the main focus of modern  business.
Digital Тwin is a significant enabler for Industry 4.0 and especially for Internet of Things related initiatives.

Although digital twins have been around for several decades, the rapid rise of the internet of things (IoT) is that they have become more widely considered as a tool of the future. Digital twins are getting attention because they also integrate things like artificial intelligence (AI) and machine learning (ML) to bring data, algorithms, and context together.

History of Digital Twin

Despite that the terminology has changed over time, the basic concept of the Digital Twin model has remained fairly stable from its inception in 2002. It is based on the idea that a digital informational construct about a virtual or a physical system could be created as an entity on its own. This digital information would be a “twin” of the information that was embedded within the virtual or physical system itself and be linked with that system through the entire lifecycle of the system[1].

This conceptual model was used in the first executive Product Lifecycle Management Systems (PLM) courses at the University of Michigan in early 2002, where it was referred to as the Mirrored Spaces Model[1].  

Figure 1. Dr. Michael Grieves, University of Michigan, Lurie Engineering Center, Dec, 3, 2001 [1]

Digital Twins is represented as a concept for PLM systems in the beginning of 21 century, but it is now achieving real value and presence in the industrial space. In fact, it is now recognized as a key part of the Industry 4.0 roadmap.

One of the primary reasons digital twin technology is rapidly being adopted is there are multiple use cases across the industrial enterprise: engineering, manufacturing and operations, and maintenance and service. Digital twins are made possible (and improved) by a multitude of Industry 4.0 technologies – IoT, AR, CAD, PLM, AI, edge computing, to name a few – to create a powerful tool that’s driving business value.[3]

What is Digital Twin?

Digital Twin is the exact representation of, for example, a building as digital data. One example could be a database that knows everything that happened during the construction phase of a building, like:

  1. A timeline of every status change reported for all activities executed to deliver the project.
  2. Who reported them?
  3. Issues and Obstructions that needed to be faced during the construction process.
  4.  When those have been resolved and by whom.

Figure 2. Digital Twin Model, described with Sablono [4].

Such a database should also be considered a Digital Twin. Sure, since it really is the exact representation of the buildings construction phase after all.

Digital Twins and Existing Technologies for Information Modelling 

Nowadays, there are many different approaches for information modelling in specific business domains. Most of existing technologies are focused on modelling of physical and virtual systems, but not to be a “replica” of the system during the maintenance, which also can be updated to have identical behavior as the original systems. One typical example is Building Information Modelling (BIM).

BIM vs. Digital Twins

  1. BIM Is For Design and Construction
  2. BIM Isn’t Designed for Real-Time Operational Response
  3. BIM Focuses on Buildings Rather Than People
  4. Digital Twin can give you information about the current state of build subsystems
  5. Digital Twin a model that evolves over time to deliver more value with each new stage of the asset’s lifecycle
  6. In the future, Digital Twin will certainly supersede BIM software even at the design and build phase of an asset’s lifecycle.

 

 

Figure 3. BIM vs. Digital Twins

 

Essential components to create a digital twin of building 

Figure 4. Essential components to create a digital twin of building and difference with BIM

 

Components of BIM and digital twin for buildings

Figure 5. A detailed comparison of BIM and digital twin of building

 

Digital Twins vs, Simulation Models

It is essential to understand the difference between a digital twin and simulation models, which are often used in healthcare, fin-tech and engineering.

There is a difference between a digital twin and simulation models, which have been used for decades, and may use the same type of sensor data, though not always. But simulations generate and manipulate data as part of the simulation. The whole point of a simulation is to project what CAN happen, not what is happening at the moment.

 

Industry areas for application of Digital Twins 

The digital twins concept is applicable for probably most of the industry areas for modelling of physical or virtual systems

  • Manufacturing Industry (all sub-areas)
  • Healthcare industry
    • Modelling of parts of human body
    • Modeling of person in the context of system, which can be analyzed for specific anomalies.
    • Modeling of processes
  • Automotive industry
  • Fin-Tech Industry
  • Logistics
  • Building industry
    • Smart spaces
    • Construction
    • Energy Models for Optimization
    • Materials testing

 

Digital Twins concept offers also many additional opportunities, like better marketing and sales, based on customer relationships and product maintenance on Digital Twins.

Figure 6. Digital Twins Benefits 

Digital Twins for Healthcare

There are wide options to use Digital Twins for humans: to go beyond gathering and analyzing data, it has the potential to change medicine as we know it by designing a digital model to align the moving parts of a whole system. That applies to both individuals as well as the healthcare system. 

Probably on of the main aim of Digital Twins is to realize Personalized Medicine by identifying deviations from normal. It is questionable how feasible this is at our current level of knowledge. 
This will help to trigger and analyze anomalies related to to specific anomalies, including also symptoms for viruses (for example like COVID-19) which will help very fast and precise to identify specific cases.

From another hand, there are projects to use digital twin techniques for PARTS of the human body, such as the heart.

A digital twin can be defined as a lifelong, rich data record of a person combined with AI-powered models that can ‘interrogate’ the data to answer clinical questions.

All advantages about Digital Twins and healthcare should be considered in the contexts of privacy and regulations.

Digital Twins and Cloud Computing

Design and implementation of Digital Twins is quite challenging because you need to have flexible extensible model, which can trigger different events and to call specific business logic. 

Development of a specific solution for one industry from scratch is too expensive.

There are 2 groups of solutions, focused on the following goals:

  1. A common solution, which can be extended to cover many use cases for different business domains (Microsoft Azure Digital Twins Service) 
  2. Specific solution for group of systems or subsystems like IoT device management (AWS device shadows, Azure IoT Hub Device Twins , Google Cloud IoT Core device registry, Bosch IoT Things)

Solutions, focused on device management and device registry are already well known for IoT solutions.
Digital Twins services for general purpose (like Azure Digital Twins Service) are relative new and offer huge opportunities for mass introduction of Digital Twins in many solutions for all industries.

In the next blogs we will compare Digital Twins solutions of the main software vendors and will have deep dive into Azure Digital Twins Service,

References

1.(PDF) Origins of the Digital Twin Concept. Available from: https://www.researchgate.net/publication/307509727OriginsoftheDigitalTwinConcept/stats [accessed Mar 14 2020].

2. Piascik, R., J. Vickers, D. Lowry, S. Scotti, J. Stewart. and A. Calomino (2010). Technology Area 12: Materials, Structures, Mechanical Systems, and Manufacturing Road Map, NASA Office of Chief Technologist. 

3. P. de Wilde, “Ten questions concerning building performance analysis”, Building and Environment, vol. 153, pp. 110-117, 2019. Available: 10.1016/j.buildenv.2019.02.019 [Accessed 25 November 2019].

4. D. Jung, “Why BIM and Digital Twin Technology shouldn’t be confused”, Sablono – Die Plattform zur Baufortschrittskontrolle, 2019. [Online]. Available: https://www.sablono.com/de/blog/bim-and-digital-twin-technology/. [Accessed: 25- Nov- 2019].

(PDF) Origins of the Digital Twin Concept. Available from: https://www.researchgate.net/publication/307509727OriginsoftheDigitalTwinConcept/stats [accessed Mar 14 2020].

If you want more information feel free to contact me at michael@mateev.net Follow my blog : https://mmateev.wordpress.com/ .

You can learn more also if you follow me on Twitter @mihailmateev , and stay in touch on Facebook, Google+, LinkedIn and Bulgarian BI and .Net User Group !

About Mihail Mateev

am a Microsoft Regional Director currently living in Sofia, Bulgaria. My interests range from technology to entrepreneurship. I am also interested in programming, web development, and education. Technical Consultant, Community enthusiast, PASS Regional Mentor for Central Eastern Europe, chapter lead, Microsoft MVP – Microsoft Azure. Organizer of SQLSaturday, Azure Bootcamp, IoT and JavaScript conferences. My experience is in various areas related to Microsoft technologies, including Windows Platform, ASP.Net MVC, MS SQL Server and Microsoft Azure. I have a PhD in cloud computing and am a university lecturer on Smart Homes and Smart Energy IoT Solutions
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