EMD自相关阈值去噪法在水电机组振动信号中的研究

发布时间：2018-06-17 04:38

本文选题：经验模态分解 + 小波阈值去噪　；参考：《中国农村水利水电》2017年07期

【摘要】：经验模态分解(EMD)可将信号分解成若干不同频率的固有模态函数(IMF),传统EMD去噪法只对高频IMF分量进行处理,但缺乏有效的IMF选择依据。自相关能量准则可以定量筛选出含有噪声的IMF分量,利用改进的小波阈值去噪法对这些含噪IMF进行处理,然后重构所有IMF分量得到去噪后的信号。将这种EMD自相关阈值去噪法用于水电机组振动信号中,通过实例仿真,将去噪前后的信噪比和均方根误差进行比较,结果表明这种去噪方法能有效用于水电机组振动信号中。
[Abstract]:Empirical mode decomposition (EMD) can decompose the signal into a number of inherent mode functions with different frequencies. The traditional EMD denoising method only deals with the high frequency IMF components, but it lacks an effective basis for IMF selection. The autocorrelation energy criterion can be used to quantitatively screen out the IMF components with noise. The improved wavelet threshold de-noising method is used to process these noised IMF components, and then all the IMF components are reconstructed to get the de-noised signals. The EMD autocorrelation threshold denoising method is applied to the vibration signal of hydroelectric generating set. The comparison of SNR and RMS error before and after denoising is carried out by an example. The results show that this method can be used effectively in the vibration signal of hydropower unit.
【作者单位】：梯级水电站运行与控制湖北省重点实验室(三峡大学);
【基金】：教育部留学回国人员科研启动基金(KJ2015QT007)
【分类号】：TV734.21

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