Research on Data Processing of Railway Bridge Deformation Monitoring Based on Wavelet Analysis
In order to solve the noise problem in the automatic deformation monitoring data of railway bridges,the acquired automatic deformation monitoring data was denoised based on a wavelet threshold denoising method.The method could remove noise from automated deformation monitoring data and extract the real deformation trend information of railway bridges.Firstly,a set of simulated deformation monitoring signals containing noise was constructed by the Matlab.The influences of different wavelet basis functions and different wavelet decomposition levels on the denoising results were analyzed through simulation experiments.In order to achieve the optimal denoising result,the denoising quality comprehensive evaluation index T was used to select the optimal wavelet basis function and wavelet decomposition level.Finally,to prove the reliability of the denoising method,the denoising method was used to denoise the automatic deformation monitoring data of a railway bridge static level.The results show that this method can effectively remove the noise in the measured data and retain the detailed characteristics of the bridge pier deformation.
railwaywavelet analysisthreshold denoisingcomprehensive evaluation indicatorsautomated monitoring data
NETL
NSTL
万方数据
