Bridge Signal Denoising Method Combined VMD Parameters Optimized by Aquila Optimizer with Wavelet Threshold

When the bridge structure is in the condition assessment and health monitoring, the bridge signals obtained are susceptible to the interference of the external environment, and it is difficult to reflect the real response of the bridge structure. Aiming at the critical problem of bridge signal corrupted with environmental noise, a denoising method combining Aquila Optimizer (AO), Variational Mode Decomposition (VMD) and wavelet threshold denoising is proposed. First, the AO algorithm is used to optimize the hyperparameters of the VMD, and the VMD is utilized to adaptively decompose the noisy signal. Secondly, the modes with smaller variance contribution rate are removed. Finally, the remaining modes are processed by wavelet threshold, and then the denoised signal can be achieved by reconstruction. The simulated signal and the measured signal of bridge dynamic strain are analyzed respectively. The results show that the proposed denoising method can effectively filter out the interference noise signal. Moreover, it has been verified that the performance of the proposed method is better than the conventional denoising methods, i. e., EMD, wavelet threshold combined denoising and EEMD wavelet threshold combined denoising. The results provide a meaningful reference for the denoising of bridge signals © 2022 Xi'an Highway University. All rights reserved.