Application of Wavelet Soft Threshold Denoising Algorithm Based on EMD Decomposition in Vibration Signals

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI:10.1109/ICSAI48974.2019.9010456

Biao Sun, Shaoping Zhou, Congyi Wang

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引用次数: 6

Abstract

This paper proposes the wavelet threshold noise reduction algorithm based on Empirical Mode Decomposition (EMD) to solve the problems of centrifugal pump vibration signal complex, exist various frequency band non-single interference signal and useful signal amplitude is small, etc. The algorithm combines the adaptive characteristics of EMD decomposition and the time-frequency localization characteristics of wavelet threshold denoising algorithm. While simplifying the noise reduction process, it can effectively deal with non-single interference signals in each frequency band. In order to verify the applicability of this algorithm, it is compared with wavelet threshold noise reduction algorithm and spatial-temporal filtering analysis method. Finally, the influence of soft and hard threshold functions on the noise reduction effect is analyzed. The experimental results show that the wavelet soft threshold denoising algorithm based on EMD decomposition has better noise reduction effect when the centrifugal pump vibration signal is used as the noise reduction object.

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基于EMD分解的小波软阈值去噪算法在振动信号中的应用

针对离心泵振动信号复杂、存在多频带非单干扰信号和有用信号幅值小等问题，提出了基于经验模态分解(EMD)的小波阈值降噪算法。该算法结合了EMD分解的自适应特性和小波阈值去噪算法的时频局部化特性。在简化降噪过程的同时，可以有效地处理各频段的非单一干扰信号。为了验证该算法的适用性，将其与小波阈值降噪算法和时空滤波分析方法进行了比较。最后，分析了软、硬阈值函数对降噪效果的影响。实验结果表明，以离心泵振动信号作为降噪对象时，基于EMD分解的小波软阈值去噪算法具有较好的降噪效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 6th International Conference on Systems and Informatics (ICSAI)

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