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基于改进傅里叶模态分解和频带熵的滚动轴承故障诊断方法

刘俊锋 俞翔 万海波

刘俊锋, 俞翔, 万海波. 基于改进傅里叶模态分解和频带熵的滚动轴承故障诊断方法[J]. 中国舰船研究, 2022, 17(2): 190–197 doi: 10.19693/j.issn.1673-3185.02359
引用本文: 刘俊锋, 俞翔, 万海波. 基于改进傅里叶模态分解和频带熵的滚动轴承故障诊断方法[J]. 中国舰船研究, 2022, 17(2): 190–197 doi: 10.19693/j.issn.1673-3185.02359
LIU J F, YU X, WAN H B. Rolling bearing fault diagnosis method based on modified fourier mode decomposition and band entropy[J]. Chinese Journal of Ship Research, 2022, 17(2): 190–197 doi: 10.19693/j.issn.1673-3185.02359
Citation: LIU J F, YU X, WAN H B. Rolling bearing fault diagnosis method based on modified fourier mode decomposition and band entropy[J]. Chinese Journal of Ship Research, 2022, 17(2): 190–197 doi: 10.19693/j.issn.1673-3185.02359

基于改进傅里叶模态分解和频带熵的滚动轴承故障诊断方法

doi: 10.19693/j.issn.1673-3185.02359
基金项目: 国家自然科学基金资助项目(51679245)
详细信息
    作者简介:

    刘俊锋,男,1997年生,硕士生。研究方向:机械故障诊断。E-mail:228302204@qq.com

    俞翔,男,1978年生,博士,副教授。研究方向:船舶工业技术,武器工业与军事技术,工业通用技术及设备。E-mail:yuxiang898@sina.com

    万海波,男,1987年生,博士,讲师。研究方向:工业通用技术及设备,船舶工业技术。E-mail:general3000@126.com

    通信作者:

    俞翔

  • 中图分类号: U664.21;TH133.33

Rolling bearing fault diagnosis method based on modified fourier mode decomposition and band entropy

知识共享许可协议
基于改进傅里叶模态分解和频带熵的滚动轴承故障诊断方法刘俊锋,等创作,采用知识共享署名4.0国际许可协议进行许可。
  • 摘要:   目的  针对多分量、强背景噪声下滚动轴承故障特征提取困难的问题,提出一种将改进傅里叶模态分解(MFMD)和频带熵(FBE)分析相结合的滚动轴承故障特征提取方法。针对傅里叶分解(FDM)在强背景噪声下边界频率偏移和过分解等问题,提出频带熵和包络谱相结合的敏感频带和敏感模态分量选取方法。  方法  首先,通过FBE分析选取频带熵区域的极小值,将其作为敏感频带的中心频率并确定敏感频带边界;然后,在敏感频带区间内对信号进行带限傅里叶模态分解,从而获得若干个相互正交的傅里叶本征模态函数(FIMF)及其边际希尔伯特谱;其次,根据FIMFs与原信号频带熵的区域从属关系,选取可以反映故障特征的敏感FIMFs;最后,对所选取的FIMFs进行包络谱分析并提取故障特征。  结果  轴承仿真和实验结果表明,该方法可以实现轴承故障的精确诊断。  结论  研究成果可为滚动轴承的健康状态评估提供参考。
  • 图  1  模拟信号的时域波形与频谱

    Figure  1.  Time domain waveform and spectrum of analog signal

    图  2  FDM和MFMD方法的信号分解结果

    Figure  2.  Signal decomposition results of FDM and MFMD methods

    图  3  模拟信号的时频分布

    Figure  3.  Time-frequency distribution of analog signals

    图  4  轴承故障仿真信号的时域波形与频谱

    Figure  4.  Time domain waveform and spectrum of bearing fault simulation signal

    图  5  轴承故障仿真信号的时频分布

    Figure  5.  Time-frequency distribution of bearing fault simulation signal

    图  6  故障诊断流程

    Figure  6.  Fault diagnosis process

    图  7  原始信号不同窗长下的频带熵分析

    Figure  7.  Frequency band entropy analysis of original signal with different window lengths

    图  8  敏感区间内的信号分解结果

    Figure  8.  Signal decomposition results in the sensitive interval

    图  9  FIMFs的FBE和包络谱分析

    Figure  9.  FBE and envelope spectrum analysis of FIMFs

    图  10  小波包络谱的分析结果

    Figure  10.  Results of wavelet envelope spectrum analysis

    图  11  EMD分解和包络谱的分析结果

    Figure  11.  Results of EMD decomposition and envelope spectrum analysis

    图  12  轴承故障模拟平台与模型

    Figure  12.  Bearing fault simulation platform and model

    图  13  轴承振动信号的短时傅里叶变换

    Figure  13.  Short time Fourier transform of bearing vibration signal

    图  14  轴承振动信号的频带熵分析

    Figure  14.  Frequency band entropy analysis of bearing vibration signal

    图  15  敏感区间内的信号分解结果

    Figure  15.  Signal decomposition results in the sensitive interval

    图  16  FIMFs的FBE和包络谱分析

    Figure  16.  FBE and envelope spectrum analysis of FIMFs

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出版历程
  • 收稿日期:  2021-04-20
  • 修回日期:  2021-08-03
  • 网络出版日期:  2022-04-06
  • 刊出日期:  2022-04-20

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