Model-Based State Diagnosis
The safe and economical operation of complex dynamic systems requires not only their stable control but also methods for online monitoring. This applies to practically all branches of industry and their applications, such as vehicles, traction batteries, fuel cells, power plants, cooling systems and many other industrial processes. An important discipline of control engineering in this context is model-based condition diagnosis. The methods of condition diagnosis or condition monitoring can be seen as dual to control methods.
Usually, only a few state variables are known or even measurable for a concrete system. Methods of state estimation now aim at determining these internal states and/or also unknown external influences acting on the system on the basis of these few measurements. In principle, this is done in a similar way as in the control system: A so-called observer algorithm continuously intervenes in a simulation model in a corrective manner so that its simulated outputs match the actual system outputs as well as possible. Under suitable conditions, this model then provides the otherwise inaccessible inner states of the system.
Such methods support us in the most diverse areas of life, for example in GPS navigation, in the control of electric motors or in the diagnosis of aircraft engines. But the wave-like course of the COVID-19 pandemic can also be analysed and predicted through condition monitoring.
Research Projects at our Institute
Hametner, Christoph, Martin Kozek, Lukas Böhler, Alexander Wasserburger, Zhang Peng Du, Robert Kölbl, Michael Bergmann, Thomas Bachleitner-Hofmann, and Stefan Jakubek. "Estimation of exogenous drivers to predict COVID-19 pandemic using a method from nonlinear control theory, opens an external URL in a new window." Nonlinear Dynamics 106, no. 1 (2021): 1111-1125.
Hametner, Christoph, Lukas Böhler, Martin Kozek, Johanna Bartlechner, Oliver Ecker, Zhang Peng Du, Robert Kölbl, Michael Bergmann, Thomas Bachleitner-Hofmann, and Stefan Jakubek. "Intensive care unit occupancy predictions in the COVID-19 pandemic based on age-structured modelling and differential flatness, opens an external URL in a new window." Nonlinear Dynamics (2022): 1-19.
Pernsteiner, Dominik, Alexander Schirrer, Lukas Kasper, René Hofmann, and Stefan Jakubek. "Data-based model reduction for phase change problems with convective heat transfer, opens an external URL in a new window." Applied Thermal Engineering 184 (2021): 116228.
Pernsteiner, Dominik, Alexander Schirrer, Lukas Kasper, René Hofmann, and Stefan Jakubek. "State estimation concept for a nonlinear melting/solidification problem of a latent heat thermal energy storage, opens an external URL in a new window." Computers & Chemical Engineering 153 (2021): 107444.
Ritzberger, Daniel, Christoph Hametner, and Stefan Jakubek. "A real-time dynamic fuel cell system simulation for model-based diagnostics and control: Validation on real driving data, opens an external URL in a new window." Energies 13, no. 12 (2020): 3148.
Böhler, Lukas, Daniel Ritzberger, Christoph Hametner, and Stefan Jakubek. "Constrained extended Kalman filter design and application for on-line state estimation of high-order polymer electrolyte membrane fuel cell systems, opens an external URL in a new window." international journal of hydrogen energy 46, no. 35 (2021): 18604-18614.
Fuhrmann, Florian, Alexander Schirrer, and Martin Kozek. "Model-predictive energy management system for thermal batch production processes using online load prediction, opens an external URL in a new window." Computers & Chemical Engineering 163 (2022): 107830.
Hametner, Christoph, Stefan Jakubek, and Wenzel Prochazka. "Data-driven design of a cascaded observer for battery state of health estimation, opens an external URL in a new window." IEEE Transactions on industry applications54.6 (2018): 6258-6266.