Abstract
In this study, a new approach is proposed for identification of structural nonlinearities by employing neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the type of nonlinearity associated with the corresponding degree of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, a feed forward regression neural network is trained, which parametrically identifies the related nonlinearity. The application of the proposed approach is demonstrated on an example system with nonlinear elements. The proposed approach does not require data collection from the degrees of freedoms related with nonlinear elements, and furthermore, the proposed approach is sufficiently accurate even in the presence of measurement noise.
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© 2013 The Society for Experimental Mechanics, Inc.
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Koyuncu, A., Cigeroglu, E., Yumer, M.E., Özgüven, H.N. (2013). Localization and Identification of Structural Nonlinearities Using Neural Networks. In: Kerschen, G., Adams, D., Carrella, A. (eds) Topics in Nonlinear Dynamics, Volume 1. Conference Proceedings of the Society for Experimental Mechanics Series, vol 35. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6570-6_9
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DOI: https://doi.org/10.1007/978-1-4614-6570-6_9
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