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基于经验小波变换与谱峭度的船舶轴系故障特征提取方法

何洋洋 王馨怡 董晶

何洋洋, 王馨怡, 董晶. 基于经验小波变换与谱峭度的船舶轴系故障特征提取方法[J]. 中国舰船研究, 2020, 15(X): 1–9 doi: 10.19693/j.issn.1673-3185.01771
引用本文: 何洋洋, 王馨怡, 董晶. 基于经验小波变换与谱峭度的船舶轴系故障特征提取方法[J]. 中国舰船研究, 2020, 15(X): 1–9 doi: 10.19693/j.issn.1673-3185.01771
He Y Y, Wang X Y, Dong J. Fault feature extraction method for marine shafting based on empirical wavelet transform-spectral kurtosis[J]. Chinese Journal of Ship Research, 2020, 15(0): 1–9 doi: 10.19693/j.issn.1673-3185.01771
Citation: He Y Y, Wang X Y, Dong J. Fault feature extraction method for marine shafting based on empirical wavelet transform-spectral kurtosis[J]. Chinese Journal of Ship Research, 2020, 15(0): 1–9 doi: 10.19693/j.issn.1673-3185.01771

基于经验小波变换与谱峭度的船舶轴系故障特征提取方法

doi: 10.19693/j.issn.1673-3185.01771
详细信息
    作者简介:

    何洋洋,男,1993年生,硕士,助理工程师。研究方向:旋转机械状态监测和故障诊断。E-mail:heyangyang_0507@163.com

    王馨怡,女,1994年生,硕士,助理工程师。研究方向:电磁兼容。E-mail:wxinyi2014@126.com

    董晶,女,1990年生,硕士,工程师。研究方向:电磁兼容。E-mail:dj19900805@zju.edu.cn

    通信作者:

    何洋洋

  • 中图分类号: U664.21

Fault feature extraction method for marine shafting based on empirical wavelet transform-spectral kurtosis

  • 摘要:   目的  为了解决船舶轴系振动信号的冗余信息过多、故障特征提取率较低等问题,提出一种基于经验小波变换与谱峭度(EWT-SK)的故障特征提取方法。  方法  首先,利用经验小波变换(EWT)对原始信号进行处理,以分离冗余振动成分,从而解决经验模态分解(EMD)固有的端点效应和模态混叠问题;然后,基于谱峭度和相关系数,筛选模态函数并进行重构,以突出故障信息,从而提高信噪比;最后,利用谱峭度获得最优带通滤波器的参数,并对滤波之后的信号进行包络解调,从而完成故障诊断。  结果  根据实例分析验证结果:在信号分解方面,EWT方法的特征提取稳定性和效率更高,可以保证轴系故障信息的完整性;在去噪效果方面,采用EWT-SK方法之后,故障信号的峭度值为4.761 6,相关系数为0.708 8,信噪比为3.762 4,其表现优于EMD和变分模态分解(VMD)方法。  结论  EWT-SK方法具有良好的特征提取能力与噪声抑制能力,适用于船舶轴系的故障诊断。
  • 图  1  傅里叶轴的分割

    Figure  1.  Partitioning of Fourier axis

    图  2  船舶轴系故障的诊断流程图

    Figure  2.  Flow chart of marine shafting fault diagnosis

    图  3  故障轴承模拟及采集装置图

    Figure  3.  Diagram of fault bearing simulation and acquisition device

    图  4  轴承内圈故障信号的时域、频域波形

    Figure  4.  Waveform of fault signal bearing inner ring in time domain and frequency domain

    图  5  EMD的前4阶模态时域图

    Figure  5.  Time-domain graph of the first fourth order IMFs using EMD

    图  6  EMD的前4阶模态频域图

    Figure  6.  Frequency-domain graph of the first fourth order IMFs using EMD

    图  7  VMD的模态函数时域图

    Figure  7.  Time-domain graph of IMFs using VMD

    图  8  VMD的模态函数频域图

    Figure  8.  Frequency-domain graph of IMFs using VMD

    图  9  故障信号的EWT支撑边界

    Figure  9.  EWT support boundary of fault signal

    图  10  EWT构造的带通滤波器幅频特性曲线

    Figure  10.  Amplitude frequency characteristic curve of bandpass filter constructed by EWT

    图  11  EWT的模态函数时域图

    Figure  11.  Time-domain graph of IMFs using EWT

    图  12  EWT的模态函数频域图

    Figure  12.  Frequency-domain graph of IMFs using EWT

    图  13  EMD-SK滤波信号时域图及包络谱

    Figure  13.  Time domain figure and envelope spectrum of signal filtered by EMD-SK

    图  14  VMD-SK滤波信号时域图及包络谱

    Figure  14.  Time domain figure and envelope spectrum of signal filtered by VMD-SK

    图  15  EWT重构信号的谱峭度图

    Figure  15.  Spectral kurtosis figure of EWT reconstructed signal

    图  16  EWT-SK滤波信号时域图及包络谱

    Figure  16.  Time domain figure and envelope spectrum of signal filtered by EWT-SK

    表  1  EWT分解模态函数的参数

    Table  1.   The IMFs' parameters of EWT

    模态峭度相关系数
    IMF13.588 60.333 1
    IMF24.154 90.543 7
    IMF33.669 80.440 4
    IMF45.297 20.351 7
    下载: 导出CSV

    表  2  3种方法的降噪对比结果

    Table  2.   Comparision of noise reduction results by three methods

    特征提取方法评价指标
    峭度相关系数信噪比
    EMD-SK3.325 50.212 22.349 9
    VMD-SK3.299 40.620 32.457 9
    EWT-SK4.761 60.708 83.762 4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-09-11
  • 修回日期:  2019-12-07
  • 网络出版日期:  2020-12-10

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