大跨径桥梁变形监测数据的组合降噪方法研究

Study on Combined Noise Reduction Method of Deformation Monitoring Data of Long-span Bridges

为了准确提取桥梁GNSS监测数据中的有效变形特征,本文充分发挥自适应噪声完备集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)与小波变换(Wavelet Transform,WT)在信号降噪中的优势,将二者结合进行桥梁GNSS监测数据降噪。首先通过CEEMDAN方法将原始监测数据分解为若干个本征模态函数(Intrinsic Mode Function,IMF),并通过相关系数识别出有效IMF分量,包含噪声的IMF分量以及无效IMF分量;其次使用WT软阈值降噪方法对包含噪声的IMF分量进一步降噪;最后重构降噪后IMF分量与有效IMF分量。通过仿真实验数据与苏通大桥实测GNSS数据对本文方法的有效性与优越性进行检验,结果表明,本文方法具有良好的降噪效果,能够有效提取桥梁的真实变形信息。

In order to accurately extract the effective deformation features in the bridge GNSS monitoring data,this paper gives full play to the advantages of adaptive noise complete ensemble empirical mode decomposition(CEEMDAN)and wavelet transform(WT)in signal denoising,and combines them to denoise the bridge GNSS monitoring data.Firstly,the original monitoring data is decomposed into several intrinsic mode functions(IMF)by CEEMDAN method,and the effective IMF component,IMF component containing noise and invalid IMF component are identified by correlation coefficient;Secondly,WT soft threshold denoising method is used to further denoise the IMF component containing noise;Finally,the IMF component and effective IMF component after noise reduction are reconstructed.The effectiveness and superiority of this method are tested by the simulation experimental data and the measured GNSS data of Sutong Bridge.The results show that this method has good noise reduction effect and can effectively extract the real deformation information of the bridge.