Modal parameter recognizing method based on multi-input multi-output signal noise reduction

Abstract

The invention relates to a modal parameter recognizing method based on multi-input multi-output signal noise reduction. The method is characterized by comprising the steps of step 1, performing block-Hankel matrix conversion on noise-containing multi-point pulse responding signals to obtain a first block-Hankel matrix; step 2, processing the first matrix by utilizing structural low rank approximating according to the rank of the first matrix to obtain a first non-block-Hankel matrix after the low rank approximating; step 3, replacing the elements of each block in a second matrix with math average values of the block elements on a back-diagonal to obtain a second block-Hankel matrix; step 4, repeating the steps 2 and 3 until convergence, thus obtaining the noise-reduced multi-point pulse responding signal; and step 5, performing modal parameter recognizing. The modal parameter recognizing method based on multi-input multi-output signal noise reduction can save machine time and improve recognizing accuracy.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a modal parameter identification method based on multiple-input multiple-output signal noise reduction. Background technique [0002] Traditional modal testing and analysis is based on the input and output data of the system, from which the frequency response function (frequency domain) or impulse response function (time domain) is estimated, and then the modal parameters are identified. However, for actual large-scale and complex structures, artificial excitation (input) is not an easy task, and the implementation of experiments is difficult and costly, and the input information of the structural system is often impossible to obtain, so the output response information can only be used to determine the modal parameters. identify. [0003] Based on the number of input and output reference points, it is divided into single-input single-output (SISO), single-input multiple-output (...

AI Technical Summary

Problems solved by technology

However, the noise mode cannot be completely eliminated by the stability diagram method, especially as the order of the model increases, some false modes tend to tend to be stable, and it is difficult to correctly identify the real modal parameters of the structure with the stability diagram

Moreover, the stability diagram method largely depends on the user's experience and judgment, so the calculation efficiency will be reduced due to the influence of human factors, especially when the signal-to-noise ratio of the signal is low, how to distinguish a large number of false modes and Real modals will become difficult

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