中国机械工程 ›› 2016, Vol. 27 ›› Issue (03): 337-342.

• 机械基础工程 • 上一篇    下一篇

基于小波半软阈值消噪的盲源分离方法

孟宗1,2;马钊1;刘东1;李晶1   

  1. 1.河北省测试计量技术及仪器重点实验室(燕山大学),秦皇岛,066004
    2.国家冷轧板带装备及工艺工程技术研究中心,秦皇岛,066004
  • 出版日期:2016-02-10 发布日期:2016-02-03
  • 基金资助:
    国家自然科学基金资助项目(51575472);河北省自然科学基金资助项目 (E2015203356);河北省高等学校科学研究计划资助重点项目(ZD2015049);河北省留学人员科技活动择优资助项目(C2015005020)

Blind Source Separation Based on Wavelet Semi-soft Threshold Denoising

Meng Zong1,2;Ma Zhao1;Liu Dong1;Li Jing1   

  1. 1.Key Laboratory of Measurement Technology and Instrumentation of Hebei Province,Yanshan University,Qinhuangdao,Hebei,066004
    2.National Engineering Research Center for Equipment and Technology of Cold Rolling Strip,Qinhuangdao,Hebei,066004
  • Online:2016-02-10 Published:2016-02-03

摘要:

为了有效提取含噪机械故障信号中的故障特征信息,研究了一种基于小波半软阈值消噪的盲源分离方法。利用小波半软阈值对故障信号进行消噪处理;采用联合近似对角化算法对信号进行盲源分离;考虑在噪声干扰下预消噪常常不足以消除全部噪声,因此在盲源分离后再进行适当的消噪处理,以提高其分离性能。实验验证了所提出方法的有效性和可行性。

关键词: 盲源分离, 小波, 半软阈值, 故障诊断

Abstract:

In order to extract fault feature informations from the mechanical malfunction signals with noise, a method of blind source separation was proposed based on wavelet semi-soft threshold denoising. First, wavelet semi-soft threshold was used to filter the failure signals. Then, joint approximate diagonalization was used as blind source separation method to separate signals. Pretreatment was often not enough to eliminate all noises, therefore, it was necessary to denoise again to improve the separation performance. Finally, the feasibility and validity of this method was verified by experiments.

Key words: blind source separation, wavelet, semi-soft threshold, fault diagnosis

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