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Unknown Excitation Modal Parameter Identification Technology Research

Author: ZhangZhen
Tutor: ZhengMin
School: Nanjing University of Aeronautics and Astronautics
Course: Vehicle Operation Engineering
Keywords: Unknown incentive EMD Parameter Identification Process Neural Network Time-varying systems Hilbert - Huang Transform
CLC: TB535
Type: Master's thesis
Year: 2009
Downloads: 78
Quote: 1
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Abstract


Vibration modal parameter identification in the defense industry , aerospace , mechanical engineering, energy, transport and civil engineering construction and other fields have a wide range of applications . Modal parameter identification method can generally be divided into traditional modal parameter identification method and unknown excitation modal parameter identification method , due to the traditional modal parameter identification method needs while taking advantage of stimulus and response signals for identification, so that in the application there are a lot of limitations . The unknown excitation modal parameter identification method based only on the unknown excitation system response can be carried modal parameter identification . This paper studies how to separate the response data using the excitation of the unknown time-varying system parameter identification . In signal processing , the singular value decomposition , digital filtering, correlation coefficients and other methods combined pretreatment , then its EMD decomposition, the IMF component for parameter identification . In the parameter identification , the use of two solutions : one is based on Hilbert - Huang transform parameter identification method ; two EMD decomposition is proposed based on the combination of neural networks and process parameter identification method . In order to verify the effectiveness of the proposed various methods , using MATLAB software SIMULINK established three degrees of freedom simulation of time-varying model . ADAMS, established a simply supported beam model to simulate experimental verification, the results for a variety of programs are compared and analyzed . Studies have shown that various programs used in this paper are able to more effectively carry out the unknown excitation modal identification . In addition, the results can be seen from the identification of the two methods will be subject to empirical mode decomposition EMD process of edge effects , in contrast, based on empirical mode decomposition EMD combined with the process neural network identification method is more affected small .

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CLC: > Industrial Technology > General industrial technology > Acoustic engineering > Vibration, noise and its control > Vibration and noise control and its use
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