29 November 2005Abstract:
The complexity of industrial systems and the mathematical models to describe them increases. In many cases point sensors are no longer sufficient to provide controllers and monitoring instruments with the information necessary for operation. The need for other types of information, such as audio and video, has grown. Suitable applications range in a broad spectrum from microelectromechanical systems and bio-medical engineering to papermaking and steel production.
This thesis is divided into five parts. First a general introduction to the field of vision-based and sound-based monitoring and control is given. A description of the target application in the steel industry is included.
In the second part, a recursive parameter estimation algorithm that does not diverge under lack of excitation is studied. The focus is on the stationary properties of the algorithm and the corresponding Riccati equation.
The third part compares the parameter estimation algorithm to a number of well-known estimation techniques, such as the Normalized Least Mean Squares and the Kalman filter. The benchmark for the comparison is an acoustic echo cancellation application. When the input is insufficiently exciting, the studied method performs best of all considered schemes.
The fourth part of the thesis concerns an experimental application of vision-based estimation. A water model is used to simulate the behaviour of the steel bath in a Linz-Donawitz steel converter. The water model is captured from the side by a video camera. The images together with a nonlinear model is used to estimate important process parameters, describing the heat and mass transport in the process. The estimation results are compared to those obtained by previous researchers and the suggested approach is shown to decrease the estimation error variance by 50%.
The complexity of the parameter estimation procedure by means of optimization makes the computation time large. In the final part, the time consumption of the estimation is decreased by using a smaller number of data points. Three ways of choosing the sampling points are considered. An observer- based approach decreases the computation time significantly, with an acceptable loss of accuracy of the estimates.
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