基于ICEEMDAN和分布熵的SS-Y伸缩仪信号随机噪声压制方法

A Random Noise Suppression Method for SS-Y Extensometer Signals Based on ICEEMDAN and Distributive Entropy

结合改进的自适应噪声完备集合经验模态分解(ICEEMDAN)与分布熵(DistEn),提出一种无需自定义算法参数、去噪效果较好的伸缩仪信号随机噪声压制方法。首先将伸缩仪信号进行ICEEMDAN处理,得到若干个本征模态函数(IMF);然后计算各IMF分量的分布熵值,根据不同分布熵值的大小和表征的分量信号混乱程度,有针对性地对各IMF进行取舍;最后进行线性重构。设计仿真信号去噪实验和SS-Y伸缩仪信号去噪实验,结果表明,基于ICEEMDAN-DistEn去噪模型的伸缩仪信号重构还原度较好,去噪效果显著,明显优于CEEMDAN-DistEn、小波去噪和卡尔曼滤波等去噪模型。

We combine improved complete empirical mode decomposition with adaptive noise(ICEEMDAN)and distributive entropy(DistEn)to propose a method to suppress the random noise of the extensometer signal without customizing the parameters and with good denoising effect.Firstly,the signal of the extensometer is processed by ICEEMDAN,and several intrinsic mode functions(IMF)are obtained.Then the distributive entropy value of each IMF component is calculated,and according to the magnitude of different distributive entropy values and the degree of chaos of the characterized component signals,each IMF is targeted to be traded off.Finally,linear reconstruction is performed.Designing simulated signal denoising experiments and SS-Y extensometer signal denoising experiments,the results show that the scaler signal reconstruction based on the ICEEMDAN-DistEn denoising model has significantly better reduction and denoising effect than several denoising models such as CEEMDAN-DistEn,wavelet denoising and Kalman filtering.