基于SAS降噪和谱峭度的特征频率提取方法

凌俊 肖; 勇 吕; 锐 袁

doi:10.16578/j.issn.1004.2539.2019.12.020

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基于SAS降噪和谱峭度的特征频率提取方法

试验分析 | 更新时间：2022-10-25

- 基于SAS降噪和谱峭度的特征频率提取方法
- Feature Frequency Extraction Method based on SAS Noise Reduction and Spectral Kurtosis
- 机械传动 2019年43卷第12期页码：109-115
- 作者机构：
  
  1.武汉科技大学冶金装备及其控制教育部重点实验室，湖北武汉 430081
  2.武汉科技大学机械传动与制造工程湖北省重点实验室，湖北武汉 430081
- 作者简介：
  
  肖凌俊（1980— ），男，湖北武汉人，博士研究生，讲师，研究方向为信号分析与处理、机电设备状态监控与故障诊断等。
  吕勇（1976— ），男，湖北鄂州人，教授，博士生导师，研究方向为机电系统建模及仿真远程监测与诊断软件系统开发非线性信号处理等。
- 基金信息：
  
  国家科学自然基金(51475339;51405353)
- DOI：10.16578/j.issn.1004.2539.2019.12.020
  中图分类号：
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肖凌俊,吕勇,袁锐.基于SAS降噪和谱峭度的特征频率提取方法[J].机械传动,2019,43(12):109-115.

Xiao Lingjun,Yong Lü,Yuan Rui.Feature Frequency Extraction Method based on SAS Noise Reduction and Spectral Kurtosis[J].Journal of Mechanical Transmission,2019,43(12):109-115.
肖凌俊,吕勇,袁锐.基于SAS降噪和谱峭度的特征频率提取方法[J].机械传动,2019,43(12):109-115. DOI： 10.16578/j.issn.1004.2539.2019.12.020.

Xiao Lingjun,Yong Lü,Yuan Rui.Feature Frequency Extraction Method based on SAS Noise Reduction and Spectral Kurtosis[J].Journal of Mechanical Transmission,2019,43(12):109-115. DOI： 10.16578/j.issn.1004.2539.2019.12.020.

摘要

提出了稀疏辅助平滑降噪（SAS）与谱峭度（SK）结合的滚动轴承故障特征的提取方法。首先，利用稀疏辅助平滑降噪（SAS）方法对仿真信号进行预降噪，通过均方根误差（RMSE）指标对SAS降噪与低通滤波的降噪性能实施评价；然后，将SAS降噪和谱峭度方法结合应用于故障滚动轴承的振动信号的分析中，提取到清晰的特征频率。用峭度值指标将所提方法同小波方法、EMD方法进行对比，证实了所提方法在特征提取上的优势；通过仿真和数据实验，证明了所提方法的有效性。

Abstract

A fault feature extraction method for rolling bearings based on sparse assisted smoothing (SAS) noise reduction and spectral kurtosis (SK) is proposed. Firstly, the simulation signal is de-noised by sparse assisted smoothing (SAS), and the performance of SAS de-noising and low-pass filtering is evaluated by RMSE index. Then, SAS de-noising and spectral kurtosis method are applied to fault diagnosis of rolling bearings, and clear characteristic frequency is extracted. Then the kurtosis index is used to compare the proposed method with the wavelet de-noising and EMD methods. The advantages of the proposed method in feature extraction are verified. The effectiveness of the proposed method is verified by simulation and data experiments.

关键词

稀疏辅助平滑谱峭度特征提取

Keywords

Sparse assisted smoothingSpectral kurtosisFeature extraction

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