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基于NGWarblet-WVD的高质量时频分析方法

郝国成 冯思权 王巍 凌斯奇 谭淞元

郝国成, 冯思权, 王巍, 凌斯奇, 谭淞元. 基于NGWarblet-WVD的高质量时频分析方法. 自动化学报, 2022, 48(10): 2526−2536 doi: 10.16383/j.aas.c190566
引用本文: 郝国成, 冯思权, 王巍, 凌斯奇, 谭淞元. 基于NGWarblet-WVD的高质量时频分析方法. 自动化学报, 2022, 48(10): 2526−2536 doi: 10.16383/j.aas.c190566
Hao Guo-Cheng, Feng Si-Quan, Wang Wei, Ling Si-Qi, Tan Song-Yuan. High quality time-frequency analysis via normalized generalized Warblet-WVD. Acta Automatica Sinica, 2022, 48(10): 2526−2536 doi: 10.16383/j.aas.c190566
Citation: Hao Guo-Cheng, Feng Si-Quan, Wang Wei, Ling Si-Qi, Tan Song-Yuan. High quality time-frequency analysis via normalized generalized Warblet-WVD. Acta Automatica Sinica, 2022, 48(10): 2526−2536 doi: 10.16383/j.aas.c190566

基于NGWarblet-WVD的高质量时频分析方法

doi: 10.16383/j.aas.c190566
基金项目: 国家自然科学基金(61333002), 111项目(B17040), 资助
详细信息
    作者简介:

    郝国成:中国地质大学(武汉)机械与电子信息学院教授. 主要研究方向为信号处理, 时频分析, 电磁传感器设计. 本文通信作者.E-mail: haogch@cug.edu.cn

    冯思权:中国地质大学(武汉)机械与电子信息学院硕士研究生. 主要研究方向为图像处理, 机械故障信号处理, 时频分析算法.E-mail: fengsq@cug.edu.cn

    王巍:中国地质大学(武汉)机械与电子信息学院讲师. 主要研究方向为FPGA开发, 信号检测.E-mail: geo_wangwei@126.com

    凌斯奇:中国地质大学(武汉)机械与电子信息学院硕士研究生. 主要研究方向为机械故障信号处理, 时频分析算法. E-mail: ling047@icloud.com

    谭淞元:中国地质大学(武汉)机械与电子信息学院硕士研究生. 主要研究方向为电磁信号处理, 时频分析算法.E-mail: tansongyuan@cug.edu.cn

High Quality Time-frequency Analysis via Normalized Generalized Warblet-WVD

Funds: Supported by National Natural Science Foundation of China (61333002) and 111 Project (B17040)
More Information
    Author Bio:

    HAO Guo-Cheng Professor at the School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan). His research interest covers signal processing, time-frequency analysis, and electromagnetic sensor design. Corresponding author of this paper

    FENG Si-Quan Master student at the School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan). His research interest covers image processing, mechanical fault signal processing, and time-frequency analysis algorithm

    WANG Wei Lecturer at the School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan). His research interest covers FPGA development and signal detection

    LING Si-Qi Master student at the School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan). His research interest covers mechanical fault signal processing and timefrequency analysis algorithm

    TAN Song-Yuan Master student at the School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan). His research interest covers electromagnetic signal processing and time-frequency analysis algorithm

  • 摘要: 针对高聚集度Wigner-Ville distribution (WVD)时频分析方法存在严重的交叉项干扰问题, 利用广义Warblet变换(Generalized Warblet transform, GWT)不产生虚假频率分量的特点, 提出了WVD与GWT相结合的归一化广义Warblet-WVD (Normalized generalized Warblet-WVD, NGWT-WVD)算法. 该算法将GWT与WVD进行矩阵运算, 实现滤波效应, 抑制WVD产生的新交叉项以及混入自项的交叉项, 提高WVD的时频分析质量. 实验结果表明, NGWT-WVD方法有效地去除了多分量信号的交叉项干扰, 提高信号分析结果的时频聚集度, 还原多分量信号的真实时频分布. 采用NGWT-WVD方法处理金属疑似破裂样本信号, 获取破裂发生区间的时间和频率标志段, 为监测传感器设置有效门限值提供判据, 取得了良好效果.
  • 图  1  三分量信号的WVD时频图

    Fig.  1  Time-frequency diagram of WVD of three-components signal

    图  2  三分量信号的GWT时频图

    Fig.  2  Time-frequency diagram of GWT of three-components signal

    图  3  3种GWT-WVD时频图

    Fig.  3  Time-frequency diagram of three types of GWT-WVD

    图  5  三分量信号三维时频图比较

    Fig.  5  Three-dimensional time-frequency diagrams comparison of three-component signals

    图  4  NGWT-WVD算法流程图

    Fig.  4  Algorithm flowchart of NGWT-WVD

    图  6  阈值敏感性测试图

    Fig.  6  The test chart of threshold sensitivity

    图  7  分段信号时频图

    Fig.  7  Time-frequency diagram of segmented signal

    图  8  交叉型信号时频图

    Fig.  8  Time-frequency diagram of cross-type signal

    图  9  两分量调频信号时频图

    Fig.  9  Time-frequency diagram of two-component frequency modulated signal

    图  10  各算法的时变功率谱误差柱形图

    Fig.  10  Time-varying power spectrum error column chart of each algorithm

    图  11  各算法的时变功率谱误差折线图

    Fig.  11  Time-varying power spectrum error line chart of each algorithm

    图  12  各算法的CM值柱形图

    Fig.  12  CM value column chart of each algorithm

    图  13  各算法的CM值折线图$(\times{10^{ - 3}})$

    Fig.  13  CM value line chart of each algorithm $(\times{10^{ - 3}})$

    图  14  六面顶压机和硬质合金顶锤

    Fig.  14  Cubic press and carbide anvil

    图  15  疑似金属破裂样本时频分析

    Fig.  15  Time-frequency analysis of suspected metal rupture samples

    表  1  各算法的时变功率谱误差比较

    Table  1  Time-varying power spectrum error comparison of each algorithm

    算法类型${ {z_1}( t )}$${{z_2}( t )}$${{z_3}( t )}$${{z_4}( t )}$
    WVD0.60610.31530.51390.5603
    Gabor-WVD0.30950.08540.05990.0736
    GWT-WVD0.20720.10840.12870.1394
    VMD-WVD0.07200.02740.01050.2375
    NGWT-WVD0.02100.05870.00990.0136
    下载: 导出CSV

    表  2  各算法的CM值比较$(\times{10^{ - 3}})$

    Table  2  CM value comparison of each algorithm $(\times{10^{ - 3}})$

    算法类型${{z_1}( t )}$${{z_2}( t)}$${{z_3}( t )}$${{z_4}( t )}$
    GWT0.00790.02820.01670.0194
    Gabor-WVD0.03030.08520.05760.0554
    GWT-WVD0.03860.09210.05960.1164
    WVD0.06870.18210.07760.1008
    VMD-WVD0.06490.21430.12040.1526
    NGWT-WVD0.07220.22540.13360.1625
    下载: 导出CSV

    表  3  六种算法的CM值比较$(\times{10^{ - 5}})$

    Table  3  CM value comparison of six algorithms $(\times{10^{ - 5}})$

    算法类型CM
    GWT4.3669
    Gabor-WVD5.6375
    GWT-WVD7.5044
    WVD7.5046
    VMD-WVD17.6381
    NGWT-WVD20.8527
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-08-06
  • 录用日期:  2020-04-10
  • 网络出版日期:  2022-09-15
  • 刊出日期:  2022-10-14

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