Systems Engineering with the Digital Twin

Systems Engineering and the problem with “Traceability”

Especially in the development of complex products, the mechanics, electrics, electronics and software must fit together seamlessly. Nevertheless, Time2Marketshould be shortened and the production should start smoothly.

The interdisciplinary approach of Systems Engineering is designed to ensure exactly that and to answer a wide range of questions, such as:

Which requirements were realized by which parts or software components?
What costs arise from changes, how do they affect the production or the supply chain?
Which functions were tested and how were they tested and how does my product behave in real operation?

This "traceability” along the value chain plays an important role for Smart Systems Engineering. But how can it be achieved in historically grown, heterogeneous IT landscapes

Digital Thread/ Digital Twin provide answers to the challenges

The Digital Thread bundles and connects the relevant information in a dynamic data model

Example use case: Evaluation of a change request in the design department.

Many aspects have to be considered, e.g.

Which components must be redesigned?
Are other components affected?
Are there implications for production or logistics?
How can the evaluation of change requests be accelerated and reliably mastered?

The relevant information has to be compiled from heterogeneous IT applications in a

complex and time-consuming process:

Mechanical parts from PLM systems
Costs from calculation tools
Production data from ERP systems, etc.

All this information, including the relationships along the value chain, forms the digital image, the so-called "digital thread". In connection with the physical counterpart, the real object with its state and behaviour, the digital twin finally emerges.

Further exemplary use cases in engineering:

Cross-domain engineering change management with integrated impact analysis
Efficient, traceable product protection
Certification/ fulfillment of legal obligations to provide evidence
Fast retrieval of product information (distributed across different systems) in a standardized front-end
Coverage analysis for verification and validation
Monitoring of compliance with specification requirements in the development process (e.g. weight)
Continuous monitoring of construction and operating status for the control of over-the-air updates

T-Systems Solution

Using proven T-Systems integration solutions, we extract data and relationships from the back-end systems using "minimally invasive intervention".

We link the information to semantic networks and uncover unknown relationships such as cost drivers. In our intuitive cockpits, the required data systems can be quickly found and used across all systems.

Enriched with data from production, sales or service, the Digital Twin offers many potentials for optimization.

By connecting an IoT platform, sensor values can be used directly from the field. Thus, the Digital Twin provides faster insights into the use and behaviour of the product/component and enables new data-driven business models.

"Dark Side of the Moon" - How does my product/ component behave?

T-Systems uses an example of a vehicle brake to show what a Digital Twin can look like. Sensors directly on the brake provide current data such as the speed of rotation, hydraulic brake pressure, force on the brake pedal or the level of brake fluid. This information is stored and analyzed using an IoT platform and visualized in a modern dashboard. The condition and behaviour of the vehicle brake can be used for a direct comparison with the values calculated in the engineering department.

Experience it yourself with our interactive web demonstrator.

In combination with modern Augmented Reality solutions, there are further possible applications, e.g. early team or cross-location design reviews.

Example Design Review with Augmented Reality

Flash is not available.

In combination with Artificial Intelligence/ mechanical data analysis, deviations/ anomalies can be automatically detected or predictions/ predictions derived. Thanks to extended integration, e.g. with simulation tools, virtual sensors can represent an extended context and thus provide information that cannot be measured directly (e.g. the temperature of a brake disc).

More about this in a more detailed conversation. Please contact us.