基于VMD的自适应形态学在轴承故障诊断中的应用

摘要
图/表
参考文献(14)
相关文章 (15)

全文: PDF (1539 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为有效提取滚动轴承信号的特征频率，提出了基于变分模态分解（VMD）的自适应形态学的特征提取方法。首先利用VMD将目标信号分解为有限个模态信号，依据互信息法提取与原始信号相关的模态信号，将其进行求和重构；然后利用形态学对重构信号进行降噪处理，提取出滚动轴承的特征频率。针对形态学固有统计偏移和结构元素的选择问题，利用粒子群算法来优化改进的广义形态学滤波器，实现自适应滤波。通过数字仿真实验与滚动轴承故障试验分析，将其与基于经验模式分解（EMD）的自适应形态学、包络解调方法进行比较，结果表明该方法可以有效提取故障信号的特征频率。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	钱林1
	康敏1
	2
	傅秀清1
	王兴盛1
	费秀国3

关键词 ：轴承, 变分模态分解, 数学形态学, 粒子群算法, 互信息法

Abstract：To effectively extract characteristic frequencies of rolling bearing vibration signals, the adaptive morphology was proposed based on variational mode decomposition (VMD). VMD was used to decompose a target signal into finite modal signals firstly. Then the modal signals related to the original signal were extracted based on the mutual information method, and they were summed to reconstruct a signal. The morphologic filter was used to reduce noise from the reconstructed signal, and the rolling bearing fault feature frequencies were extracted. Aiming at problems of morphologic structural element selection and the inherent statistical deviation, the particle swarm optimization was used to adoptively optimize the improved generalized morphological filter to realize adaptive filtering. Through digital simulation test and rolling bearing fault tests, the method was compared with the adaptive morphology based on EMD and the envelope demodulation method. The results showed that this method can extract fault characteristic frequencies of rolling bearings effectively.

Key words： bearing variational mode decomposition (VMD) mathematical morphology particle swarm optimization mutual information method

收稿日期: 2015-10-09 出版日期: 2017-01-25

引用本文:

钱林1，康敏1,2，傅秀清1，王兴盛1，费秀国3. 基于VMD的自适应形态学在轴承故障诊断中的应用[J]. 振动与冲击, 2017, 36(3): 227-233.
QIAN Lin1,KANG Min1,2,FU Xiuqing1,WANG Xingsheng1,FEI Xiuguo3. Application of adaptivemorphology in bearing fault diagnosis based on VMD. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(3): 227-233.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2017/V36/I3/227

[1]钟秉林，黄仁.机械故障诊断学[M].北京：机械工业出版社，2007.
[2]胥永刚，孟志鹏，陆明. 基于双树复小波包变换的滚动轴承故障诊断[J]. 农业工程学报，2013，29(10)：49-56.
XU Yong-gang， MENG Zhi-peng，LU Ming. Fault diagnosis of rolling bearing based on dual-tree complex wavelet packet transform [J]. Transactions of the Chinese Society of Agricultural Engineering，2013，29(10)：49-56.
[3] 王建国，吴林峰，秦绪华. 基于自相关分析和LMD的滚动轴承振动信号故障特征提取[J].中国机械工程，2014，25(2)：186-191.
WANG Jian-guo，Wu Lin-feng，Qin Xu-hua. Rolling Bearing Vibration Signal Fault Feature Extraction Based on Autocorrelation Analysis and LMD [J]. China Mechanical Engineering，2014，25(2)：186-191.
[4]唐贵基，王晓龙. 基于EEMD降噪和1.5维能量谱的滚动轴承故障诊断研究[J]. 振动与冲击，2014，33(1)：6-10.
TANG Gui-ji，WANG Xiao-long. Fault diagnosis for roller bearings based on EEMD de-noising and 1.5 -dimensional energy spectrum [J]. Journal of Vibration and Shock，2014，33(1)：6-10.
[5]郝如江，卢文秀，褚福磊.形态滤波器用于滚动轴承故障信号的特征提取[J].中国机械工程，2009 ，20 (2)：197-201.
HAO Ru-jiang，LU Wen-xiu，CHU Fu-lei. Morphological filters in feature extraction for rolling bearing defect signals [J]. China Mechanical Engineering，2009，20(2)： 197-201.
[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] Proceedings of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences, 1998，454：903-995.
[7] Wu Z， Huang N E. A study of the characteristic of white noise using the empirical mode decomposition method [J] .Proceedings of the Royal Society of London， Series A： Mathematical, Physical and Engineering Sciences， 2004，460(2046)：1597-1611.
[8]Serra J. Morphological filtering: an overview [J]. Signal Process ，1994，38(1)：3-11.
[9]章立军，杨德斌，徐金梧，等.基于数学形态滤波的齿轮故障特征提取方法[J].机械工程学报，2007，43 (2): 71-75.
ZHANG Li-jun， YANG De-bin， XU Jin-wu，et al. Approach to extracting gear fault feature based on mathematical morphological filtering [J]. Chinese Journal of Mechanical Engineering, 2007，43 (2) ：71-75.
[10]张文斌，周晓军，林勇. 广义形态滤波器在振动信号处理中的应用研究[J]. 农业工程学报，2008，24(6)：203-205.
ZHANG Wen-bin, ZHOU Xiao-jun, Lin Yong. Application research of generalized morphological filter in vibration signal processing[J]. Transactions of the CSAE, 2008, 24 (6):203-205.
[11]沈长青，朱忠奎，孔凡让，等. 形态学滤波方法改进及其滚动轴承故障特征提取[J]. 振动工程学报，2012，25(4)：468-473.
SHEN Chang-qing，ZHU Zhong-kui， KONG Fan-rang，et al. An improved morphological filtering method and bearing fault feature extraction [J]. Journal of Vibration Engineering，2012，25(4)：468-473.
[12]赵昭，刘利林，张承学，等.形态学滤波器结构元素选取原则研究与分析[J].电力系统保护与控制，2009 ，37 (14 )：21-25.
ZHAO Zhao， LIU Li-lin， ZHANG Cheng-xue， et al. Research and analysis of morphological filter's structure element selection principle[J].Power System Protection and Control ，2009，37(14) ：21-25.
[13]Dragomiretskiy K， Zosso D. Variational Mode Decomposition[J]. TEEE Transactions on Signal Processing， 2014， 62 (3)：531-544.
[14]Wang Y， MarKert R， Xiang J， et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system [J]. Mechanical Systems and Signal Processing，2015，60： 243-251.