针对希尔伯特-黄变换(Hilbert-Huang Transform, HHT)方法中存在的模态混叠和虚假固有模态函数(Intrinsic Mode Function, IMF)问题,提出一种基于总体包络均值经验模态分解(Ensemble Envelop Mean Empirical Mode Decomposition,EEMEMD)和虚假模态函数剔除算法相结合的改进HHT方法。该方法利用EEMEMD可准确反映加噪后信号的自身变化,一定程度上中和残留在各模态分量间的噪声,获得无模式混淆的较纯净的IMF分量。同时,通过基于归一化能量熵值的虚假模态函数剔除算法可有效剔除噪声干扰成分和迭代误差分量,从而提高信号特征提取的准确性。通过仿真分析和转子不对中故障诊断的工程实例表明,改进HHT方法能够较好地抑制模态混叠问题并有效剔除同故障无相关的虚假IMF,实现对旋转机械故障的有效诊断。
Abstract
In order to solve the problem of Hilbert-Huang transform (HHT), which has mode mixing and false intrinsic mode function (IMF), an improved HHT method combining ensemble envelop mean empirical mode decomposition(EEMEMD) and spurious mode function elimination algorithm was proposed. The method uses EEMEMD to accurately reflect the self-change of the signal after adding noise, neutralizes the residual noise contained in modal components partly, and obtains a purer IMF component without mode mixing. At the same time, the false mode function elimination algorithm based on normalized energy entropy can effectively eliminate noise interference components and iterative error components, so as to improve the accuracy of signal feature extraction. The simulation analysis and engineering examples of rotor misalignment fault diagnosis were compared.The result show that the improved HHT method can suppress the mode mixing problem better and effectively eliminate the false IMF, which is not related to the fault,to achieve effective fault diagnosis of rotating machinery.
关键词
总体包络均值经验模态分解 /
希尔伯特-黄变换 /
模态混叠 /
虚假模态 /
故障诊断
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Key words
ensemble envelop mean empirical mode decomposition /
Hilbert-Huang transform /
mode mixing /
false mode /
fault diagnosis
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参考文献
[1] 廖伯瑜. 机械故障诊断基础 [M]. 北京: 冶金工业出版社, 1995.
[2] 苏乃权, 熊建斌, 张清华, 等. 旋转机械故障诊断研究方法综述 [J]. 机床与液压, 2018, 46(7): 140-146.
SU Naiquan, XIONG Jianbin, ZHANG Qinghua, et al. Research methods of the rotating machinery fault diagnosis [J]. Machine Tool & Hydraulics, 2018, 46(7): 140-146.
[3] 时陪明, 丁雪娟, 李庚, 等. 一种EMD改进方法及其在旋转机械故障诊断中的应用 [J]. 振动与冲击, 2013, 32(4): 185-190.
SHI Peiming, DING Xuejuan, LI Geng, et al. An improved method of EMD and its applications in rotating machinery fault diagnosis [J]. Journal of Vibration and Shock, 2013, 32(4): 185-190.
[4] 周奇才, 刘星辰, 赵炯, 等. 旋转机械一维深度卷积神经网络故障诊断研究 [J]. 振动与冲击, 2018, 37(23): 31-37.
ZHOU Qicai, LIU Xingchen, ZHAO Jiong, et al. Fault diagnosis for rotating mechinery based on 1D depth convolutional neural network [J]. Journal of Vibration and Shock, 2018, 37(23): 31-37.
[5] 张进明. 基于EMD和HHT的旋转机械故障诊断方法研究[D]. 北京: 北京化工大学, 2006.
[6] 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]. Proceeding of the Royal Society of London-Series A: Mathematical, Physical and Engineering Sciences, 1998, 454: 903-995.
[7] Xu B, Yuan S, Wang M, et al. Determining impact induced damage by lamb wave mode extracted by EMD method [J]. Measurement, 2015, 65: 120-128.
[8] 周小龙, 刘薇娜, 姜振海, 等. 基于改进HHT和马氏距离的齿轮故障诊断 [J]. 振动与冲击, 2017, 36(22): 224-230.
ZHOU Xiaolong, LIU Weina, JIANG Zhenhai, et al. Gear fault diagnosis based on improved HHT and Mahalanobis distance [J]. Journal of Vibration and Shock, 2017, 36(22): 224-230.
[9] Amarnath M, Praveen Krishna I R. Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis [J]. Measurement, 2014, 58: 154-164.
[10] Wu Z, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[11] Yeh J R, Shieh J S, Huang N E. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method [J]. Advances in Adaptive Data Analysis, 2010, 2(2): 135-156.
[12] 郑近德, 程军圣, 杨宇. 改进的EEMD算法及其应用研究[J]. 振动与冲击, 2013, 32(21): 21-26.
ZHENG Jinde, CHENG Junsheng, YANG Yu. Modified EEMD algorithm and its applications [J]. Journal of Vibration and Shock, 2013, 32(21): 21-26.
[13] Colominas M A, Schlotthauer G, Torres M E. Improved complete ensemble EMD: A suitable tool for biomedical signal processing [J]. Biomedical Signal Processing & Control, 2014, 14: 19-29.
[14] 荣锋, 陈宁, 郭翠娟, 等. 一种EEMD-HHT与时频重排结合的转速曲线估计新方法 [J]. 振动与冲击, 2018, 37(22): 81-87.
RONG Feng, CHEN Ning, GUO Cuijuan, et al. New method for speed curve estimation by using the EEMD-HHT combined with time-frequency reassignment [J]. Journal of Vibration and Shock, 2018, 37(22): 81-87.
[15] Peng Z K, Tse P W, Chu F L. An improved Hilbert-Huang transform and its application in vibration signal analysis [J]. Journal of Shound and Vibration, 2005, 286(1-2): 187-205.
[16] 张梅军, 唐建, 何晓晖. EEMD方法及其在机械故障诊断中的应用 [M]. 北京: 国防工业出版社, 2015.
[17] 姜万录,王浩楠,朱勇,等. 变分模态分解消噪和核模糊C均值聚类相结合的滚动轴承故障识别方法 [J]. 中国机械工程, 2017, 28(10): 1215-1220.
JIANG Wanlu, WANG Haonan, ZHU Yong, et al. Integrated VMD denoising KFCM clustering fault identification method of rolling bearings [J]. China Mechanical Engineering, 2017, 28(10): 1215-1220.
[18] 韩清凯, 于小光. 基于振动分析的现代机械故障诊断原理及应用[M]. 北京: 科学技术出版社, 2010.
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