As discussed in Digital Twin 101, a Digital Twin is the virtual representation of a real-world physical object. Digital Twins help manage complexity by providing a semantically rich abstraction layer, especially for systems with a high level of functional complexity and heterogeneity. As an AIoT project lead, one should start by looking at the question "Is a Digital Twin needed, and if so - what kind of Digital Twin?", before defining the Digital Twin implementation roadmap.
Is a Digital Twin needed?
The decision of whether and when to apply the Digital Twin concept in an AIoT initiative will depend on at least two key factors: Sensor Data Complexity/Analytics Requirements and System Complexity (e.g., the number of different machine types, organizational complexity, etc.).
If both are low, the system will probably be fine with using Digital Twin more as a logical design concept and applying traditional data analytics. Only with increasing sensor data complexity and analytics requirements will the use of AI be required.
High system complexity is an indicator that dedicated Digital Twin implementation should be considered, potentially utilizing a dedicated DT platform. The reason is that a high system complexity will make it much harder to focus on the semantics. Here, a formalized DT can help.
If So, What Kind of Digital Twin?
Since Digital Twin is a relatively generic concept, the concrete implementation will heavily depend on the type of data that will be used as the foundation. Since Digital Twins usually refer to physical assets (at least in the context of our discussions), the potential data can be identified along the lifecycle of a typical physical asset: design data or digital master data, simulation data, manufacturing/production data, customer data, and operational data. For the operational data, it is important to differentiate between data related to the physical asset itself (e.g., state, events, configuration data, history) versus data relating to the environment of the asset.
Depending on the application area, the Digital Twin (DT) can have a different focus. The Operational DT will mainly focus on operational data, including the internal state and data relating to the environment. PLM-focused DT will combine the product/asset design perspective with the operational perspective, sometimes also adding manufacturing-related data. The simulation-focused DT will combine design data with operational data and apply simulation to it. And finally, the holistic DT will combine all of the above.
The Digital Twin concept is quite versatile, and can be applied to many different use cases. The following table provides an overview of four concrete examples and how they are mapped to the DT categories introduced earlier.
The drone-based building facade inspection is covered in detail in the TÜV SÜD case study. The physics simulation example is covered in the Digital Twin 101 section. The following provides an overview of the pneumatic system example, as well as the elevator example.
Operational DT: Pneumatic System
Leakage detection for pneumatic systems is a good example for an operational Digital Twin. Pneumatic systems provide pressured air to implement different use cases, e.g., the drying of cars in a car wash, eliminating bad grains in a stream of grains analyzed using high-speed video data analytics, or cleaning bottles in a bottling plant. Experts estimate that pneumatic systems consume 16 billion kilowatt hours annually, with a savings potential of up to 50% (mader.eu). In order to address this savings potential, an AIoT-enabled leakage detection system can help to identify and fix leakages at customer sites. One such solution is currently developed by the AIoT Lab. This solution is based on a combination of ultrasound sensors and edge-ML for sound pattern analysis. The solution can be used on-site to perform an analysis of the customer`s pneumatic application for leakages. The results can then be used by a service technician to fix the problems and eliminate the leakages.
The foundation for the leakage detection system is an operational Digital Twin. Since customers usually don not provide detailed design information about their own systems, the focus here is to obtain as much information during the site visit and build up the main part of the Digital Twin dynamically while being on site.
The system is based on Digital Twin data in four domains:
- Domain I includes the components of the AIoT solution itself, e.g., the mobile gateways and ultrasound sensors. This DT domain is important to support the system administration, e.g., OTA-based updates of the ML models for sound detection.
- Domain II includes the pneumatic components found on-site, including pressure generators, pressure tanks, valves, etc. The definitions of these components are provided via the product catalogue, and can be selected dynamically on-site.
- Domain III includes the fuselage and how it is mapped to the applications of the customer. Key parts of the customer equipment must be identified and included in the DT model for documentation purposes. Usually, only those parts of the customer equipment are captured that are involved with any of the leakages found.
- Domain IV includes the leakages that are identified during the on-site assessment. These leakages are also captured as Digital Twins, including information about the related sound patterns, as well as the position of the leakage relative to DT information from domains II and III.
The creation of the Digital Twins happens along these domains: DT data in domain I are created once per test equipment pack. Domains II-IV are created dynamically and per customer site.
Daniel Burkhardt, Chief Product Owner, AIoT Lab: We have the goal of providing a solution architecture that enables ML model reuse and holistic AIoT DevOps. The implementation of leakage detection based on a Digital Twin of a pneumatic system provided us with relevant insights about the requirements and design principles for achieving this goal. In comparison to typical software development, reuse and AIoT DevOps require design principles such as continuous learning, transferability, modularization, and openness. Realizing these principles will guarantee the ease of use of AIoT for organizations with, e.g., no technological expertise, which in the long term leads to more detailed and meaningful Digital Twins and thus more accurate and valuable analytics.
Holistic Digital Twin: DT and Elevators
A good example of the use of a holistic Digital Twin approach is elevators, since they have a quite long and complex lifecycle that can benefit from this approach. What is interesting here as well is the combination of the elevator lifecycle in combination with the building lifecycle, since most elevators are deployed in buildings. The example in the following shows how a standard elevator design is fitted into a building design. This is a complex process, that needs to take into consideration the elevator design specification, building design, elevator shaft design, and required performance parameters.
The CAD model and EBOM data of the elevator design can be a good foundation for the digital twin. To support efficient monitoring of the elevator during the operations phase, an increasing number of advanced sensors have been applied. These include, for example, sensors to monitor elevator speed, braking behavior, positioning of the elevator in the elevator shaft, vibrations, ride comfort, doors, etc. Based on these data, a dashboard can be provided that provides reports for the physical conditions and the elevator utilization.
One pain point for building operators is the usually mandatory on-site inspections by a third party inspection service. Using advanced remote monitoring services based on a digital twin of the elevator, some countries are already allowing combination or remote and on-site inspections. For example, instead of 12 on-site inspections per year, this could be reduced to 4 on-site inspections with 8 inspections being performed remotely. This helps save costs and reduces operations interruptions due to inspection work.
The Digital Twin concept helps brings together all relevant data, and allows semantic mappings between data from different perspectives and created during different stages of the lifecycle.
Digital Twin Roadmap
From the execution perspective, a key question is how to design a realistic roadmap for the different types of Digital Twins we have looked at here. The following provides two examples, one from the automotive perspective and one from the building perspective.
Operational Digital Twin (Vehicle example)
Let us assume an OEM wants to introduce the Digital Twin concept as part of their Software-defined Vehicle initiative. Over time, all key elements of the vehicle should be represented on the software layer as Digital Twin components. How should this be approached?
Importantly, this should be done step by step or more precisely use case by use case. Developing a Digital Twin for a complex physical product can be a huge effort. The risk of doing this without specific use cases and interim releases is that the duration and cost involved will lead to a cancellation of the effort before it can be finished. This is why it is better to select specific use cases, then develop the Digital Twin elements required for them, release this, and show value creation along the way. Over time, the Digital Twin can then develop to an abstraction layer that will cover the entire asset, hopefully enabling reuse for many different applications and use cases.
Holistic Digital Twin (Building example)
A good example of use of a holistic Digital Twin concept from design to operation and maintenance is the digital building lifecycle:
- During the building design phase, the BIM (Building Information Model) approach can help optimize the design with simulation and automated validation. This way, aspects such as future operational sustainability and capacity can be evaluated. Automated design validation provides a higher level of planning safety.
- During the building construction process, AIoT-enabled solutions such as robot-based construction progress monitoring can provide transparency and reliability. Meeting budgets and timelines can be better ensured.
- Sub-systems like elevators can also be integrated into the Digital Twin approach, as discussed in the previous section.
- Finally, building inspection can be supported by solutions such as the Drone-based façade inspection. The results of the façade inspection can be mapped back to the Digital Twin, augmenting the planning data with real-world as-is data.
The decision for a BIM / Digital Twin-based approach for building and construction is strategic. Upfront investments will have to be made, which must be recuperated through efficiency increased further downstream. The holistic Digital Twin approach here is promising, but requires a certain level of stringency to be successful.
The following short interview with Dominic Kurtaz (Managing Director for Dassault Systèmes in Central Europe) highlights the experience that a global PLM company is currently making with its customers in the area of AIoT and Digital Twins.
Dirk Slama: Welcome Dominic. Can you briefly introduce your company?
Dominic Kurtaz: Dassault Systèmes consists of 20,000 inspired people around the world, developing software solutions and supporting clients in the manufacturing, healthcare and life science sector, as well as the infrastructure sector. We help to digitally design and manufacture more than 1 in 4 of the physical products you touch every day, with a focus on how they are being used by the end users and consumers. We believe that the virtual world can enhance and improve the overall physical world toward a more sustainable world, which I think is probably a good segue to the whole topic of AIoT.
Dirk: In this context, AIoT and Digital Twins can play an important role as enablers. What kind of activities and investments are you currently seeing in this space?
Kurtaz: When people think of AIoT or IoT, they immediately think of operational performance measurements with sensors, predictive maintenance, and so on. Which of course is a very valid application, but we need to think far beyond that. This is why I like this concept of a holistic Digital Twin. We need to take a step back from IoT right now. When you are looking at the Experience Economy, you will see that the value that we perceive as customers and consumers is going increasingly away from the actual product itself. Today, it is often much more about the end-to-end experience: how the product is perceived how we select it, how we are using it, and how we dispose and recycle the product. The end-to-end life cycle experience is clearly important. From my experience, we need to look at the IoT through the eyes of the customer and the eyes of the consumer. First, we have to understand how business strategies and business execution with AIoT can truly support and improve those aspects.
Second, I believe that the Digital Twin is truly becoming pervasive across industries and all products. Take, for example, one of the most mundane products that we experienced in our lives - the light bulb. If you go back 10 years, it was just this item at the end of the shopping list that you grab off from the shop shelf without thinking much about it - you bought it, you screwed it in, you turned it on and off, and hopefully you would never have to think about that product again for the next years…until it breaks.
Today, this is fundamentally changing. I am not just buying a commodity product for my house anymore. I am buying something that is part of a connected ecosystem. I can set different moods at home using different light configurations. I can use smart lighting as part of my home security system. From a business perspective, this is a game changer. Light bulb manufacturers are no longer just producing light bulbs – today, they are connected to their customers. In the past, we did not know our customers or how they were using our products. Today, – enabled by the IoT – I can have a direct relationship with my customer. This will change things on many levels and opens up new business models.
Thus far, we have only seen the tip of the iceberg: although many of the enabling technologies are reaching a good level of maturity, the actual implementations are often still very immature and limited to those basic connectivity features – but not delivering the holistic Digital Twin experience. For example, I have recently bought a new kitchen, including connectivity to my smart home. Now I can control and integrate it into my own kitchen facilities. This is really good and interesting as well as delivering additional features but I was not able to experience and understand the value that it can really bring until after I had purchased all of those IoT enabled and connected products. And in today's world, I should have been able to use a Digital Twin of the product prior to my buy to fully understand not just the product, but the behavior, the context, the operational aspect of that post my buying - and that is simply not yet possible. Take, as another example, mobility. As a customer, I should be able to experience all these new features such as advanced driver assistance, before I acquire the physical product – enabled by a holistic Digital Twin. I really want to be able to experience in the virtual world how these products are going to behave, before using them in the physical world. This is also very helpful for product development, because it allows us to validate the customer experience in the virtual world – before making expensive investments in physical prototypes.
From what I am seeing from our customers, this is not just a hype or a fad. I think it is absolutely mission critical for anybody who is designing and manufacturing products, and dealing with the digital experience of those products. We see this across all industries where we are operating: manufacturing, healthcare, life science, and infrastructure.
Dirk Slama: What are your recommendations from the implementation perspective?
Dominic Kurtaz: You need a clear focus on the end user experience that you are trying to deliver. This will determine the holistic design philosophy you need to apply. Many companies have started with Big Data, and they are now drowning in it. The problem is to find and connect the data that are relevant for the end user experience. The connection of digital, semantic models with data will open up potential for all industries. Of course, this has to be done step-by-step, use case by use case – building up the holistic Digital Twin with a clearly value-driven approach.
Another key aspect is the alignment between the digital supply chain and the physical supply chain. For the IT, we have Continuous Integration and Continuous Delivery (CI/CD). For the physical product, we have simultaneous engineering and closed loop PLM. The challenge is now to close the even bigger loop around all of this– bringing IT DevOps together with physical product engineering. This is exactly where AIoT and Digital Twin will play an important role. AIoT enables new digital/physical product features. And the Digital Twin is the semantic interface between the digital and the physical world. During design and development, the Digital Twin helps create the required interfaces at the technical and the organizational levels. During runtime, it enables a new customer experience.