A Signal Noise Reduction Method for Modal Parameter Identification

Abstract

The invention discloses a signal denoising method for modal parameter identification. Step 1: construct a Hankel matrix by using the noise-containing structural impulse response signal; Order index, use the order-determining index to determine the model order; Step 3: Use the order-determining index and the structural low-rank approximation to process the Hankel matrix, and obtain the reconstruction matrix after the low-rank approximation; Step 4: Repeat steps 2 and 3 until Satisfy the convergence criterion, so as to obtain the denoised signal. Step five: use the noise reduction signal to identify the modal parameters. The beneficial effect of the present invention is that the Frobenius norm of the Hankel matrix deviation before and after the noise reduction can approach to the minimum by setting the convergence standard and the low-rank approximation of the structure, that is, the precision of the noise reduction signal can be improved.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a signal noise reduction method for modal parameter identification. Background technique [0002] At present, in order to ensure the safe service of large engineering structures such as bridges and offshore platforms, the structural health detection technology based on structural impulse response signals has developed rapidly. The modal parameter identification is the basic and key link of the detection technology. Therefore, it is very important to improve the accuracy of modal parameter identification. [0003] Due to the influence of test conditions, instruments and equipment, human operation, etc., there are always some uncertainties in the field vibration test experiment process, and the measured signal will inevitably be interfered by background noise. Although measures such as averaging, filtering, and masking can be used to reduce noise during data acquisition, it is im...

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Problems solved by technology

However, the result of such identification will produce false modes, and will lead to a decrease in computational efficiency, especially when the signal-to-noise ratio of the signal is low, how to distinguish a large number of false modes from real modes will become very difficult

[0004] At present, in the prior art, signal noise reduction based on low-rank approximation technology is proposed. The low-rank approximation technology uses the Hankel matrix sub-diagonal average method to identify the modal parameters of the signal after denoising. This method has a certain degree of The accuracy of modal parameter identification is improved, but the Frobenius norm of the Hankel matrix deviation before and after noise reduction in this denoising method is not the minimum value, that is, the final result is not the mathematically optimal solution, that is, the signal denoising effect there is room for improvement

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