类EMD算法在桥梁振动数据降噪中的对比分析

Comparative analysis of EMD-like algorithm in noise reduction of bridge vibration data

在桥梁结构健康监测过程中,桥梁的振动信号易受外部环境影响而产生噪声,从而导致桥梁振动数据失真。针对此问题有着多种降噪算法,本文尝试使用经验模态分解(EMD)、集合经验模态分解(EEMD)和自适应噪声完全集合经验模态分解(CEEMDAN)3种算法分别对模拟仿真信号和桥梁振动信号数据进行降噪分析,进而探究3种算法的降噪性能。首先,使用3种算法分别处理原始信号数据,分离出不同的本征模态分量(IMF),并计算出其分量的方差比和相关系数;然后,利用参数筛选出有效分量进而重构信号;最后,以信噪比(SNR)与均方根误差(RMSE)作为评价指标,分析其降噪性能。结果表明CEEMDAN方法在3种方法中的降噪效果最好,而EEMD方法效果次之,EMD方法最弱。

In the process of bridge structural health monitoring,the vibration signal of the bridge is easily affected by the external environment to generate noise,which further leads to the distortion of the vibration data of the bridge.There are a variety of noise reduction algorithms for this problem.This paper lists three algorithms:empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD)and adaptive noise complete ensemble empirical mode decomposition(CEEMDAN).Signal and bridge vibration signal data are analyzed for noise reduction,and then the noise reduction performance of the three algorithms is explored.Firstly,three algorithms are used to process the original signal data separately,different intrinsic mode components(IMF)are separated,and the variance ratio and correlation coefficient of the components are calculated,and then the effective components are selected by parameters to reconstruct the signal,and finally the signal-to-noise ratio(SNR)and root mean square error(RMSE)were used as evaluation indexes to analyze its noise reduction performance.The results show that the CEEMDAN method has the best noise reduction effect among the three methods,while the EEMD method is the second,and the EMD method is the weakest.