FEATURE EXTRACTION METHOD OF SOUND SIGNAL TO ROLLING BEARING BASED ON BLIND SOURCE SEPARATION AND MORLET WAVELET

LI JingJiao; CHEN EnLi; LIU YongQiang

doi:10.16579/j.issn.1001.9669.2018.03.004

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FEATURE EXTRACTION METHOD OF SOUND SIGNAL TO ROLLING BEARING BASED ON BLIND SOURCE SEPARATION AND MORLET WAVELET

更新时间：2022-09-22

- FEATURE EXTRACTION METHOD OF SOUND SIGNAL TO ROLLING BEARING BASED ON BLIND SOURCE SEPARATION AND MORLET WAVELET
- Journal of Mechanical Strength Vol. 40, Issue 3, Pages: 528-533(2018)
- 作者机构：
  
  1. 石家庄铁路职业技术学院
  2. 石家庄铁道大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2018.03.004
  CLC：
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LI JingJiao, CHEN EnLi, LIU YongQiang. FEATURE EXTRACTION METHOD OF SOUND SIGNAL TO ROLLING BEARING BASED ON BLIND SOURCE SEPARATION AND MORLET WAVELET. [J]. 40(3):528-533(2018)
DOI：

LI JingJiao, CHEN EnLi, LIU YongQiang. FEATURE EXTRACTION METHOD OF SOUND SIGNAL TO ROLLING BEARING BASED ON BLIND SOURCE SEPARATION AND MORLET WAVELET. [J]. 40(3):528-533(2018) DOI： 10.16579/j.issn.1001.9669.2018.03.004.

摘要

声音信号的测试与分析是滚动轴承故障检测与诊断的一种新方法,但其信噪比较低,因此提出了基于盲源分离技术和自适应Morlet小波变换诊断轴承声学信号故障的新方法。首先利用小波包将单通道的声音信号分离成2个虚拟通道的声音信号,再用盲源分离技术将信号进行源信号的提取,然后利用最小Shannon熵对Morlet小波的形状参数进行优化,找到与所测声音信号特征成份最匹配的小波,再对小波系数矩阵进行奇异值分解,通过奇异值与变化尺度的关系曲线得到最佳小波变换尺度,最后对滚动轴承故障信号进行Morlet小波变换进行故障特征提取。结果表明:该方法能有效地从强噪声背景下提取出轴承声学信号的故障。

Abstract

It is a new method of sound signal testing and analysis,but the SNR is too low,so we raise a feature extraction method of sound signal to rolling bearing based on blind source separation and adaptive Morlet wavelet. Firstly,the wavelet packet is used to change the single channel of the voice signal separated into two virtual channels,then using BSS to extract the source of signal. Minimum Shannon entropy is used to optimize the Morlet wavelet shape factor,in order to match with the impact component. Then,an abrupt information detection method based on the transitional stage of singular curve of wavelet coefficient matrix is used to choose the appropriate scale for the wavelet transformation. Finally,the fault feature of the signal can be extracted using this method. The experimental results shows that the method could extract sound signal fault feature more effectively.

关键词

声学信号盲源分离小波变换Shannon熵故障诊断

Keywords

Sound signalBSSWavelet transformShannon entropyFault diagnosis

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