一种自适应频率窗经验小波变换的滚动轴承故障诊断方法

邓飞跃; 强亚文; 杨绍普; 郝如江; 刘永强

doi:10.7652/xjtuxb201808004

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一种自适应频率窗经验小波变换的滚动轴承故障诊断方法

更新时间：2025-07-09

- 一种自适应频率窗经验小波变换的滚动轴承故障诊断方法
- A Fault Diagnosis Method of Rolling Element Bearings with Adaptive Frequency Window Empirical Wavelet Transform
- 西安交通大学学报 2018年52卷第8期页码：22-29
- 作者机构：
  
  石家庄铁道大学机械工程学院,石家庄,050043
- 作者简介：
- 基金信息：
  
  : 2017-12-20。作者简介: 邓飞跃(1985—),男,讲师。基金项目: 国家自然科学基金资助项目(11572206,51375319,11790280);河北省高等学校科学技术研究项目(QN2017137)
- DOI：10.7652/xjtuxb201808004
  中图分类号： TH133
- 网络首发：2018-08-10，
  
  纸质出版：2018
- 稿件说明：
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邓飞跃, 强亚文, 杨绍普, 等. 一种自适应频率窗经验小波变换的滚动轴承故障诊断方法[J]. 西安交通大学学报, 2018,52(8):22-29.

A Fault Diagnosis Method of Rolling Element Bearings with Adaptive Frequency Window Empirical Wavelet Transform[J]. 2018, 52(8): 22-29.
邓飞跃, 强亚文, 杨绍普, 等. 一种自适应频率窗经验小波变换的滚动轴承故障诊断方法[J]. 西安交通大学学报, 2018,52(8):22-29. DOI： 10.7652/xjtuxb201808004.

A Fault Diagnosis Method of Rolling Element Bearings with Adaptive Frequency Window Empirical Wavelet Transform[J]. 2018, 52(8): 22-29. DOI： 10.7652/xjtuxb201808004.

摘要

为解决强背景噪声下经验小波变换(EWT)难以准确提取滚动轴承故障特征的问题

提出了一种自适应频率窗EWT方法。首先对轴承故障振动信号进行傅里叶变换

引入一个带宽可变的滑动频率窗对其频谱进行分割; 然后利用水循环优化算法(WCA)

通过所提出的包络谱谐波噪声比指标

自适应确定滑动频率窗位置; 最后进行EWT筛选出最佳的模态分量信号

通过包络解调分析提取轴承故障特征信息。采用所提方法对滚动轴承故障实验信号进行分析

结果表明

该方法可以有效用于滚动轴承微弱故障特征的提取

而传统EWT方法因为受强背景噪声影响较大

无法准确提取故障特征信息。

Abstract

A novel method

called adaptive frequency window EWT

is proposed to solve the problem that the empirical wavelet transform(EWT)method is difficult to extract fault features of rolling element bearings under strong background noise. Firstly

the Fourier transform is applied to vibration signals of bearing with faults and then a moving and flexible frequency window is introduced to segment the Fourier spectrum of signals. Secondly

the water cycle optimization algorithm(WCA)is used to adaptively determine the position of frequency window through a proposed envelope spectrum harmonic-to-noise ratio. Finally

the best mode signal is generated using the frequency window EWT

and the bearing fault features are extracted through envelope demodulation analysis of the signal. Experimental results show that the proposed method effectively improves the weak fault detection of bearing

while the traditional EWT method is difficult to extract fault features of bearings because of the interference of strong background noise.

关键词

Keywords

references

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