留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

香农熵改进的变分模态分解与故障特征提取

方桂花 杜壮 高旭

方桂花, 杜壮, 高旭. 香农熵改进的变分模态分解与故障特征提取[J]. 机械科学与技术, 2020, 39(7): 1022-1027. doi: 10.13433/j.cnki.1003-8728.20190233
引用本文: 方桂花, 杜壮, 高旭. 香农熵改进的变分模态分解与故障特征提取[J]. 机械科学与技术, 2020, 39(7): 1022-1027. doi: 10.13433/j.cnki.1003-8728.20190233
Fang Guihua, Du Zhuang, Gao Xu. Shannon Entropy Improved Variational Mode Decomposition and Fault Features Extraction[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(7): 1022-1027. doi: 10.13433/j.cnki.1003-8728.20190233
Citation: Fang Guihua, Du Zhuang, Gao Xu. Shannon Entropy Improved Variational Mode Decomposition and Fault Features Extraction[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(7): 1022-1027. doi: 10.13433/j.cnki.1003-8728.20190233

香农熵改进的变分模态分解与故障特征提取

doi: 10.13433/j.cnki.1003-8728.20190233
基金项目: 

内蒙古自治区科技创新引导奖励资金项目 KCBJ2018031

详细信息
    作者简介:

    方桂花(1962-), 教授, 硕士生导师, Fgh60@sohu.com

  • 中图分类号: TH165+.3;TP206+.3

Shannon Entropy Improved Variational Mode Decomposition and Fault Features Extraction

  • 摘要: 为了解决变分模态分解(VMD)的分解层数K选定困难的问题,提出了利用归一化香农熵对变分模态分解进行参数优化,从而可以自适应地确定分解层数K,可以避免信号过分解与欠分解。首先在程序中预先设定分解层数,让程序进行预分解;计算分解后各本征模态函数(IMF)频带的香农熵,再将香农熵归一化处理,以归一化熵值大小作为循环停止条件来进行自适应确定分解层数K;最后对各IMF分量进行包络分析,提取信号中的故障特征频率。将该方法利用仿真信号和实际故障数据进行分析验证,结果表明该方法既能够自适应地确定K值,同时其分解出的各IMF分量均出现规律性故障振动信号或转频的倍频,证明了这种故障特征提取方法是有效的。
  • 图  1  自适应变分模态分解流程图

    图  2  合成仿真信号时域图

    图  3  仿真信号分解后各信号时域与频域图

    图  4  内圈故障时域与频域图

    图  5  K=8时各IMF归一化香农熵

    图  6  各IMF分量包络谱

    表  1  K=3时各IMF归一化香农熵

    IMF1 IMF2 IMF3
    0.329 8 0.330 4 0.339 8
    下载: 导出CSV

    表  2  K=4时各IMF归一化香农熵

    IMF1 IMF2 IMF3 IMF4
    0.109 9 0.123 9 0.140 5 0.625 7
    下载: 导出CSV

    表  3  K=5时各IMF归一化香农熵

    IMF1 IMF1 IMF3 IMF4 IMF5
    0.073 6 0.073 7 0.084 0 0.156 5 0.612 2
    下载: 导出CSV

    表  4  不同K值最后一个IMF归一化香农熵

    K=6 K=7 K=8 K=9
    0.137 7 0.115 9 0.204 6 0.185 0
    下载: 导出CSV
  • [1] 姜万录, 刘思远, 张齐生.液压故障的智能信息诊断与监测[M].北京:机械工业出版社, 2013

    Jiang W L, Liu S Y, Zhang Q S. Intelligent information diagnosis and monitoring of hydraulic faults[M]. Beijing:China Machine Press, 2013(in Chinese)
    [2] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995 doi: 10.1098/rspa.1998.0193
    [3] Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3):531-544 doi: 10.1109/TSP.2013.2288675
    [4] 唐贵基, 王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报, 2015, 49(5):73-81 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xajtdxxb201505012

    Tang G J, Wang X L. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi'an Jiaotong University, 2015, 49(5):73-81(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xajtdxxb201505012
    [5] 王振威.基于变分模态分解的故障诊断方法研究[D].河北秦皇岛: 燕山大学, 2015

    Wang Z W. Research on fault diagnosis method based on variational mode decomposition[D]. Hebei Qinhuangdao: Yanshan University, 2015(in Chinese)
    [6] 刘尚坤, 唐贵基, 王晓龙.基于改进变分模态分解的旋转机械故障时频分析方法[J].振动工程学报, 2016, 29(6):1119-1126 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdgcxb201606022

    Liu S K, Tang G J, Wang X L. Time frequency analysis method for rotary mechanical fault based on improved variational mode decomposition[J]. Journal of Vibration Engineering, 2016, 29(6):1119-1126(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdgcxb201606022
    [7] 王志坚, 常雪, 王俊元, 等.排列熵优化改进变模态分解算法诊断齿轮箱故障[J].农业工程学报, 2018, 34(23):59-66 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb201823007

    Wang Z J, Chang X, Wang J Y, et al. Gearbox fault diagnosis based on permutation entropy optimized variational mode decomposition[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(23):59-66(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb201823007
    [8] 吴文轩, 王志坚, 张纪平, 等.基于峭度的VMD分解中k值的确定方法研究[J].机械传动, 2018, 42(8):153-157 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxcd201808030

    Wu W X, Wang Z J, Zhang J P, et al. RRsearch of the method of determining k value in VMD based on Kurtosis[J]. Journal of Mechanical Transmission, 2018, 42(8):153-157(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxcd201808030
    [9] 王奉涛, 柳晨曦, 张涛, 等.基于k值优化VMD的滚动轴承故障诊断方法[J].振动、测试与诊断, 2018, 38(3):540-547 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdcsyzd201803016

    Wang F T, Liu C X, Zhang T, et al. Fault diagnosis method of rolling bearing based on k value optimized VMD[J]. Journal of Vibration, Measurement & Diagnosi, 2018, 38(3):540-547(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdcsyzd201803016
    [10] 丁雷, 曾锐利, 沈虹, 等.基于香农熵特征的发动机故障诊断方法[J].振动与冲击, 2018, 37(21):233-239 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdycj201821034

    Ding L, Zeng S L, Shen H, et al. An engine fault diagnosis method based on Shannon entropy features[J]. Journal of Vibration and Shock, 2018, 37(21):233-239(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdycj201821034
    [11] 李真.熵选择IMF分量的滚动轴承故障诊断方法[D].北京: 北京交通大学, 2014

    Li Z. Fault diagnosis method of rolfing bearing based on entropy selecting the IMF component[D]. Beijing: Beijing Jiaotong University, 2014(in Chinese)
    [12] Tang G J, Wang X L, He Y L, et al. Rolling bearing fault diagnosis based on variational mode decomposition and permutation entropy[C]//2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). Xi'an: IEEE, 2016
    [13] 郑小霞, 周国旺, 任浩翰, 等.基于变分模态分解和排列熵的滚动轴承故障诊断[J].振动与冲击2017, 36(22):22-28 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdycj201722004

    Zheng X X, Zhou G W, Ren H H, et al. A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy[J]. Journal of Vibration and Shock, 2017, 36(22):22-28(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdycj201722004
    [14] 傅雷.面向状态监测和故障诊断的风力发电模拟技术及其应用研究[D].杭州: 浙江大学, 2018

    Fu L. Research on wind generation recurring technology dedicated to condition monitoring and fault diagnosis[D]. Hangzhou: Zhejiang University, 2018(in Chinese)
    [15] 王奉涛, 陈守海, 闫达文, 等.基于流形-奇异值熵的滚动轴承故障特征提取[J].振动、测试与诊断, 2016, 36(2):288-294 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdcsyzd201602013

    Wang F T, Chen S H, Yan D W, et al. Fault feature extraction method for rolling bearing based on manifold and singular values entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2016, 36(2):288-294(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdcsyzd201602013
  • 加载中
图(6) / 表(4)
计量
  • 文章访问数:  263
  • HTML全文浏览量:  34
  • PDF下载量:  24
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-05-16
  • 刊出日期:  2020-07-05

目录

    /

    返回文章
    返回