基于多小波变换和奇异值分解的声发射信号降噪方法

Noise Reduction Method of Acoustic Emission Signal Based on Multi-wavelet Transform and Singular Value Decomposition

为了提高起重机故障诊断的准确率,本文将多小波变换(MWT)和奇异值分解(SVD)进行结合,提出了一个回转支承声发射信号降噪新方法.本文采用MWT对声发射信号进行多层次分解,并用SVD算法进行处理,提高了声发射信号的可识别度.最后,采用仿真信号和真实回转支承声发射信号进行测试,结果表明本文所用方法可以有效地去除信号中的噪声,提高信号的可分离程度,使回转支承的检测具有更高的诊断精度.

In order to improve the accuracy rate of crane slewing bearing fault diagnosis,this paper combines multi-wavelet transform(MWT)and singular value decomposition(SVD),and proposes a new slewing bearing acoustic emission signal noise reduction method.Multi-wavelet transform is used to decompose the acoustic emission signal at multiple levels,and the singular value decomposition algorithm is used for processing,which improves the recognizability of the acoustic emission signal.Finally,the simulation signal and the real acoustic emission signal of the slewing ring are used for testing.The results show that the method used in this paper can effectively remove the noise in the signal,improve the signal separation degree and improve the slewing ring fault diagnosis ability.