Abstract
The measured frequency response functions (FRFs) in the modal test are usually contaminated with noise that significantly affects the modal parameter identification. In this paper, a modal peak-based Hankel-SVD (MPHSVD) method is proposed to eliminate the noise contaminated in the measured FRFs in order to improve the accuracy of the identification of modal parameters. This method is divided into four steps. Firstly, the measured FRF signal is transferred to the impulse response function (IRF), and the Hankel-SVD method that works better in the time domain rather than in the frequency domain is further applied for the decomposition of component signals. Secondly, the iteration of the component signal accumulation is conducted to select the component signals that cover the concerned modal features, but some component signals of the residue noise may also be selected. Thirdly, another iteration considering the narrow frequency bands near the modal peak frequencies is conducted to further eliminate the residue noise and get the noise-reduced FRF signal. Finally, the modal identification method is conducted on the noise-reduced FRF to extract the modal parameters. A simulation of the FRF of a flat plate artificially contaminated with the random Gaussian noise and the random harmonic noise is implemented to verify the proposed method. Afterwards, a modal test of a flat plate under the high-temperature condition was undertaken using scanning laser Doppler vibrometry (SLDV). The noise reduction and modal parameter identification were exploited to the measured FRFs. Results show that the reconstructed FRFs retained all of the modal features we concerned about after the noise elimination, and the modal parameters are precisely identified. It demonstrates the superiority and effectiveness of the approach.
Highlights
In modal testing, the measured frequency response functions (FRFs) signals are usually contaminated with noise [1, 2], and the noise will interfere with the accurate extraction of modal features
The measured FRF signals are usually contaminated with noise [1, 2], and the noise will interfere with the accurate extraction of modal features
The modal identification based on modal peak-based Hankel-singular value decomposition (SVD) (MPHSVD) is described in detail, and the MPHSVD method is the Hankel-SVD filter with the modal peak-based component signal selection method, which is used for eliminating the noise of the single FRF signal
Summary
The measured FRF signals are usually contaminated with noise [1, 2], and the noise will interfere with the accurate extraction of modal features. Instead of using the biggest different point of the singular values to select the number of useful components, some new feature selection methods are based on the decomposed component signals from Hankel-SVD. The modal identification based on MPHSVD is described in detail, and the MPHSVD method is the Hankel-SVD filter with the modal peak-based component signal selection method, which is used for eliminating the noise of the single FRF signal. N is equal to m + n − 1, and hij represents the element of the jth antidiagonal line of matrix Ai. en, we define the first row of Ai as Li and define the last column without the first element of Ai as Ri. e elements of Li and Ri can form a component signal hi(t), which is correlated with its singular value σi: Li = hi. Where the averaging of the lth antidiagonal line means adding up the elements whose coordinate summation in Ai is equal to l + 1 and dividing the total element number dl to get the element hil. e red lines in the equation mean averaging, and the equation means averaging each antidiagonal line of Ai to get the elements of hi
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