Applications of Data Processing

Detailed topics of the lectures:

Sample applications of intelligent data processing:
Digital twin concept and its application possibilities. (1 week)
The concept of predictive maintenance and its application possibilities. (1 week)
The concept of sensorless measurement technology, analytical redundancy, their application in fault-tolerant systems or in cost-sensitive systems. (1 week)
The concept of HIL/SIL/MIL simulation, the modeling tasks for the simulated system. (1 week)
Modeling/identification:
Modeling and identification of linear dynamic systems. Parametric and non-parametric identification. Time and frequency domain matching. (1.5 weeks)
Modeling of nonlinear systems. Static nonlinearity, model fitting, compensation based on lookup table and interpolation in non-stored points. Nonlinear dynamic systems. (1 week)
Stimulus signal design for identification of linear and non-linear systems. (0.5 weeks)
Information processing:
Filter-based methods of sensor fusion. Consideration of the sensor's finite bandwidth and transmission characteristics during fusion. (1 week)
Inverse filtering, compensation of the frequency-dependent distortion of the measuring system in poorly conditioned cases. The concept of regularization. Application of regularization to solve ill-conditioned matrix equations. (1.5 weeks)
Prediction, replacement of missing data based on previous samples of time series. (1 week)
Order analysis concept and methods. (1 week)
Pattern recognition methods. (0.5 weeks)
Information reduction:
Concept of model-based information reduction, compressed sensing, application possibilities. (1 week)

Detailed topics of the practices:
1.    Identification of linear systems. Using an identification toolbox in a simulation environment
2.    Stimulus signal design using identification toolbox
3.    Complementary filter design. Filter design taking into account the dynamic properties of the sensor.
4.    Solving ill-conditioned inverse problems in a simulation environment
5.    Estimation of quantities that cannot be measured directly (measuring techniques without sensors)
6.    Prediction
7.    Order analysis in a simulation environment