Our focus is on real-time plant operation and drive technology in production and power generation.
With physical knowledge, but also based on pure measurement data, we create digital twins for multiphysical, dynamic systems. In the data-driven acquisition of information, we can compensate for the interference superimpositions of measurement data that occur in reality by means of suitable mathematical methods in order to achieve better analyses and forecasts of system behaviour.
The digital twin forms the basis for many applications:
- Quality analysis and prognosis of product properties (e.g. electric motors or extruders)
- Condition monitoring and predictive maintenance for production plants and energy producers (e.g. combined heat and power plants or wind turbines)
- Energy efficiency and flexibility analyses of production processes for demand-side management
- Controller design and validation of electronic control units using Hardware-In-The-Loop.
We support our customers in the conceptual design, data analysis and operation of the analysis, forecasting or control systems. We implement new concepts, e.g. the use of low latencies using 5G communication for data transmission between sensors, controllers and actuators.
To address these issues, we use methods from systems and control theory as well as machine learning, especially deep learning.