Identification of Non-Classical Boundary Conditions with the Aid of Artificial Neural Networks
Name
Mairit Vikat
Abstract
In the present thesis, an overview of the Euler-Bernoulli beam theory and the basics of artificial neural networks were presented. The main emphasis was on the practical implementation of training the artificial neural networks for predicting the stiffness parameters of the support conditions of the vibrating beams.
The main purpose of the current paper was to study the frequencies of vibrating Euler-Bernoulli beams with different non-classical support conditions, and to analyze the efficiency of predicting the support condition coefficients (either translational or rotational). The calculated natural frequencies of the vibrating beams were used as the input for training the neural networks. The results were computed for various cases, using different numbers of input frequencies (three, four, five, six, or nine) besides the different support conditions.
The results of the predictions were analyzed in two different parts: the efficiency of prediction in case of beams with elastic support at the boundaries, and the efficiency of prediction in case of beams with intermediate elastic support.
The analysis of the efficiency of prediction in case of beams with elastic support at the boundaries showed that the overall efficiency of the predictions was substantially high and the identified results were quite similar to the expected outcomes. The best average results among all conditions were received with the beam clamped or simply supported at left end and translationally and rotationally restrained at right end. But even in the worst cases, most of the results were considerably nice.
The analysis of the efficiency of predicting the rotational coefficient at the intermediate support in case of beams with intermediate elastic support showed that the results greatly depend on the generation of the training and test sets. If the training data contains noise, then the efficiency of the prediction is rather low, but it could be improved by modifying the training and test data sets. Also, alternative methods should be elaborated to extract features for parameter identification of vibrating systems.
Graduation Thesis language
English
Graduation Thesis type
Master - Information Technology
Supervisor(s)
Helle Hein
Defence year
2012