Abstract
In dynamic analysis of structures, the accuracy of the mathematical model plays a crucial role. However, because of several uncertainties like local nonlinearities, welding points, bolted joints, material properties and geometric tolerances, the mathematical model will contain differences compared to the manufactured product. Hence, it is essential to update mathematical models by using vibration test data taken from the structure. This paper presents a new approach to model updating via utilizing neural networks and genetic optimization algorithms. The key point in this new approach is that the model updating capability of neural networks is improved by a genetic optimization algorithm by guiding the optimization problem with results obtained from neural network identification. Employing the nominal mathematical model created for a particular structure, a data set of selected mode shapes and natural frequencies is created by a number of simulations performed by perturbing selected updating parameters randomly. A neural network is then created and trained with this data set. Upon training the network, it is used to update the initial model with the test data. The results are then improved further by using the “network updated mathematical model” as an initial model and updating it again by employing a genetic optimization algorithm. The most important advantage of the proposed approach is the possibility of using different number of degrees of freedom for each mode shape; as a result, additional flexibility is introduced to the approach, since the proposed method can be used with incomplete test data. The application and capabilities of the proposed approach is illustrated via real test data taken from a GARTEUR test bed, where it is seen that the proposed method updates mathematical models associated with such complex structures efficiently.
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References
Baruch, M., Itzhack, Y. B., “Optimal Weighted Orthogonalization of Measured Modes”, AIAA Journal, vol. 16, no. 4, pp. 346-351, 1978.
Berman, A., Nagy, E. J., “Improvement of a Large Analytical Model Using Test Data”, AIAA Journal, vol. 21, no. 8, pp. 1168-1173, 1983.
Sidhu, J., Ewins, D. J., “Correlation of Finite Element and Modal Test Studies of a Practical Structure”, Proceedings of the 2nd IMAC, pp. 394-401, Los Angeles, CA, 1986.
Carvalho, J., Datta, B. N., Gupta, A., Lagadapati, M., “A Direct Method for Model Updating with Incomplete Measured Data and without Spurious Modes”, Mechanical Systems and Signal Processing, vol. 21, pp. 2715-2731, 2007.
Lin, R. M., Ewins, D. J., “Model Updating Using FRF Data”, Proceedings of the 15th International Seminar on Modal Analysis, pp. 141-162, Leuven, Belgium, 1990.
Kozak, M. T., Öztürk, M., Özgüven, H. N., “A Method in Model Updating Using Miscorrelation Index Sensitivity”, Mechanical Systems and Signal Processing, vol. 23, no. 6, pp. 1747-1758, 2008.
Levin, R.I., Lieven, N. A. J., “Dynamic Finite Element Model Updating Using Neural Networks”, Journal of Sound and Vibration, vol. 210, no. 5, pp. 593-607, 1998.
Chang, C. C., Chang, T. Y. P., Xu, Y. G., “Adaptive neural networks for model Updating of Structures”, Smart Materials and Structures, vol. 9, pp. 59-68, 2000.
Chang, C. C., Chang, T. Y. P., Xu, Y. G., “Selection of Training Samples for Model Updating Using Neural Networks”, Journal of Sound and Vibration, vol. 249, pp. 867-883, 2002.
Lu, Y., Tu, Z., “A two-level neural network approach for dynamic FE model updating including damping”, Journal of Sound and Vibration, vol. 275, pp. 931-952, 2004.
Zapico, J. L., Gonzalez-Buelga, A., Gonzalez, M. P., Alonso, R., “Finite Element Model Updating of a Small Steel Frame Using Neural Networks”, Smart Materials and Structures, vol. 17, pp. 045016, 2008.
Yumer, M. E., “On the Non-linear Vibration and Mistuning Identification of Bladed Disks”, MSc Thesis, Middle East Technical University, 2010.
Yumer, M. E., Cigeroglu, E., Özgüven, H. N., “Mistuning Identification of Bladed Disks Utilizing Neural Networks”, Proceedings of ASME Turbo Expo 2010, Glasgow, United Kingdom, 2010.
Bishop, C. M., “Neural Networks for Pattern Recognition”, Oxford university Press, Oxford, UK, 1998.
Casella, G., Lehmann, E. L., “Theory of Point Estimation”, Springer, New York, USA, 1999.
The Mathworks, Inc., “Optimization Toolbox User’s Guide, Version 4”, Natick, MA, 2009.
Link, M., Friswell, M., “Working Group 1: Generation of Validated Structural Dynamic Models – Results of a Benchmark Study Utilizing the GARTEUR SM-AG19 Test-bed”, Mechanical Systems and Signal Processing, vol. 17, no. 1, pp. 9-20, 2003.
Goge, D., Link, M., “Results Obtained by Minimizing Natural Frequency and Mode Shape Errors of a Beam Model”, Mechanical Systems and Signal Processing, vol. 17, no. 1, pp. 21-27, 2003.
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Yumer, M.E., Cigeroglu, E., Özgüven, H.N. (2011). Model Updating with Neural Networks and Genetic Optimization. In: Proulx, T. (eds) Advanced Aerospace Applications, Volume 1. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9302-1_24
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DOI: https://doi.org/10.1007/978-1-4419-9302-1_24
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