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VMD-SVD在机械故障信号欠定源数目估计中的应用

王俊雄 周俊

王俊雄, 周俊. VMD-SVD在机械故障信号欠定源数目估计中的应用[J]. 机械科学与技术, 2021, 40(12): 1871-1876. doi: 10.13433/j.cnki.1003-8728.20200302
引用本文: 王俊雄, 周俊. VMD-SVD在机械故障信号欠定源数目估计中的应用[J]. 机械科学与技术, 2021, 40(12): 1871-1876. doi: 10.13433/j.cnki.1003-8728.20200302
WANG Junxiong, ZHOU Jun. Application of VMD-SVD in Underdetermined Source Number Estimation of Mechanical Fault Signals[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(12): 1871-1876. doi: 10.13433/j.cnki.1003-8728.20200302
Citation: WANG Junxiong, ZHOU Jun. Application of VMD-SVD in Underdetermined Source Number Estimation of Mechanical Fault Signals[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(12): 1871-1876. doi: 10.13433/j.cnki.1003-8728.20200302

VMD-SVD在机械故障信号欠定源数目估计中的应用

doi: 10.13433/j.cnki.1003-8728.20200302
基金项目: 

国家自然科学基金项目 51875272

云南省人才培养项目 KKSY201501037

详细信息
    作者简介:

    王俊雄(1995-), 硕士研究生, 研究方向为信号处理与故障诊断, 193388262@qq.com

    通讯作者:

    周俊, 讲师, 硕士生导师, km_zhoujun@foxmail.com

  • 中图分类号: TH17

Application of VMD-SVD in Underdetermined Source Number Estimation of Mechanical Fault Signals

  • 摘要: 针对在观测信号数目小于机械故障振动信号源数目的欠定情况下, 源信号的个数难以估计的问题, 提出一种变分模态分解(Variational mode decomposition, VMD)和奇异值分解(Singular value decomposition, SVD)相结合的盲源数目估计方法。首先利用VMD对振动信号进行分解, 得到若干本征模态函数分量(Intrinsic mode function, IMF), 然后对IMF进行重新组合得到多维观测信号的协方差矩阵, 最后依据奇异值分解的结果来对信号源数目进行最终确定。仿真信号分析验证了该方法的有效性, 将该方法运用到轴承复合故障振动信号中, 分析结果表明, 该方法能够实现欠定情况下源数目的可靠估计。
  • 图  1  仿真信号时域波形及频谱

    图  2  仿真信号VMD-SVD源数目估计图

    图  3  QPZZ-Ⅱ故障模拟实验系统

    图  4  实测信号时域波形及频谱

    图  5  实测信号VMD分量波形及频谱

    图  6  实测信号EEMD-SVD源数目估计图

    图  7  实测信号VMD-SVD源数目估计图

    表  1  第一路信号不同K值所对应的中心频率

    K1 中心频率/Hz
    IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7
    2 463 2 231
    3 365 1 456 2 283
    4 363 1 448 2 235 2 944
    5 356 1 300 1 596 2 247 2 953
    6 356 1 294 1 590 2 239 2 909 3 775
    下载: 导出CSV

    表  2  第二路信号不同K值所对应的中心频率

    K2 中心频率/Hz
    IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7
    2 1 201 1 753
    4 470 1 196 1 716 2 183
    5 469 1 196 1 715 2 180 3 821
    6 459 1 154 1 462 1 754 2 192 3 823
    7 459 1 154 1 461 1 752 2 181 3 002 3 846
    下载: 导出CSV

    表  3  奇异值分解相关结果

    名称 σ1 σ2 σ3 σ4 σ5 σ6 σ7 σ8 σ9
    特征值 0.360 5 0.280 1 0.235 3 0.141 3 0.090 9 0.061 8 0.042 1 0.036 1 0.029 8
    邻近比 1.287 0 1.190 4 1.665 3 1.554 5 1.470 9 1.468 0 1.166 2 1.211 4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-06-22
  • 刊出日期:  2021-12-05

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