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改进经验小波变换在齿轮低频微弱故障特征提取中的应用

杜思雨 冷军发 绳飘 荆双喜 罗晨旭

杜思雨, 冷军发, 绳飘, 荆双喜, 罗晨旭. 改进经验小波变换在齿轮低频微弱故障特征提取中的应用[J]. 机械科学与技术, 2021, 40(12): 1856-1862. doi: 10.13433/j.cnki.1003-8728.20200283
引用本文: 杜思雨, 冷军发, 绳飘, 荆双喜, 罗晨旭. 改进经验小波变换在齿轮低频微弱故障特征提取中的应用[J]. 机械科学与技术, 2021, 40(12): 1856-1862. doi: 10.13433/j.cnki.1003-8728.20200283
DU Siyu, LENG Junfa, SHENG Piao, JING Shuangxi, LUO Chenxu. Application of Improved Empirical Wavelet Transform in Gear Low Frequency Weak Fault Feature Extraction[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(12): 1856-1862. doi: 10.13433/j.cnki.1003-8728.20200283
Citation: DU Siyu, LENG Junfa, SHENG Piao, JING Shuangxi, LUO Chenxu. Application of Improved Empirical Wavelet Transform in Gear Low Frequency Weak Fault Feature Extraction[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(12): 1856-1862. doi: 10.13433/j.cnki.1003-8728.20200283

改进经验小波变换在齿轮低频微弱故障特征提取中的应用

doi: 10.13433/j.cnki.1003-8728.20200283
基金项目: 

国家自然科学基金项目 U1804134

河南省高等学校重点科研项目 19A440007

河南理工大学博士基金项目 B2017-28

详细信息
    作者简介:

    杜思雨(1991-), 硕士研究生, 研究方向为机械信号处理与故障诊断, fengzhixianlian@foxmail.com

    通讯作者:

    冷军发, 副教授, 硕士生导师, lengjf@hpu.edu.cn

  • 中图分类号: TH165.3

Application of Improved Empirical Wavelet Transform in Gear Low Frequency Weak Fault Feature Extraction

  • 摘要: 齿轮振动信号的经验小波变换频谱分割, 可能会将啮合频率及其边频带划分到不同频带上, 导致频带划分不合理, 分离出的调幅-调频(AM-FM)分量不理想。针对上述主要问题, 提出了一种采用频谱趋势进行频谱边界划分的改进经验小波变换方法, 将齿轮啮合频率与其相应的边频带划分到同一频带内, 得到比较理想的AM-FM分量, 实现了依据齿轮振动信号频谱局部特征的自适应分解。同时, 对提取的AM-FM分量进行自相关分析以进一步增强改进经验小波变换的低频微弱故障特征提取效果。通过仿真与试验分析, 验证了提出方法在齿轮低频微弱故障特征提取中的有效性及优势。
  • 图  1  傅里叶轴分割图

    图  2  含噪声仿真信号的时域波形

    图  3  含噪声仿真信号频谱边界划分

    图  4  EWT与改进EWT对仿真信号的分解对比

    图  5  局部断齿振动信号时域波形及其频谱划分

    图  6  改进EWT提取的前6个IMFs分量

    图  7  IMFs分量经自相关除噪后的结果

    图  8  EWT提取的IMF2~IMF6分量经除噪后的结果

    图  9  改进EWT提取的IMF3的频谱及除噪后的包络谱

    图  10  自相关除噪分量IMF5及IMF9的包络谱

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出版历程
  • 收稿日期:  2020-06-10
  • 刊出日期:  2021-12-05

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