基于SVD优化LMD的电梯导靴振动信号故障特征提取

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (22) : 166-171.

论文

陶然1、2，许有才1、2，邓方华1、2，郭澍2，李新仕2，苟敏1，李琨1，王华1

作者信息 +

Feature extraction of the elevator guide shoe vibration signal based on SVD optimizing LMD

TAO Ran1,2, XU Youcai1,2, DENG Fanghua1, GUO Shu2, LI Xinshi2, GOU Min1, LI Kun1

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文章历史 +

摘要

针对局部均值分解（Local Mean Decomposition, LMD）方法提取电梯导靴振动信号的故障特征分量时存在的模态混淆现象，本文提出了一种基于奇异值分解（Singular Value Decomposition, SVD）优化局部均值分解（Local Mean Decomposition, LMD）的电梯导靴振动信号故障特征提取方法。该方法首先以奇异值贡献率原则构造原始信号的Hankel矩阵，采用SVD对Hankel矩阵进行分解；然后将曲率谱原则与奇异值贡献率原则相结合对奇异值进行选择，将包含主要故障信息的奇异值进行逆重构，得到剔除噪声信号与光滑信号的突变信号；最后利用LMD方法对突变信号进行故障特征提取，得到能够突出原始信号振动特征的故障特征分量。实例结果表明该方法有效改善了LMD的模态混淆现象，更准确地提取了振动信号的故障特征分量。

Abstract

Aimed at the phenomenon of mode mixing of LMD extracting the fault information from the elevator guide shoe, a feature extraction method of the elevator guide shoe vibration signal based on singular value decomposition(SVD) optimizing local mean decomposition(LMD) is proposed. First of all, the method structures the Hankel matrix of original signal with singular value contribution principle. It Uses SVD to decompose the Hankel matrix. Then, in order to obtain the useful singular value which contains the main fault information, the method selects singular values with the principle of curvature spectrum and the principle of singular value contribution. The selected singular values come into being mutation signal through reconstructing. Finally, the fault feature component which represents the original signal vibration characteristics could be extracted from the mutation signal by LMD method. The method was applied to diagnosis the elevator up guide shoe fault, the elevator down guide boots fault. Application examples show that this method is effective to improve the mode mixing of LMD and extract the fault characteristic components from the elevator guide shoe vibration signal more accurately .

导出引用

陶然1、2，许有才1、2，邓方华1、2，郭澍2，李新仕2，苟敏1，李琨1，王华1. 基于SVD优化LMD的电梯导靴振动信号故障特征提取[J]. 振动与冲击, 2017, 36(22): 166-171

TAO Ran1,2, XU Youcai1,2, DENG Fanghua1, GUO Shu2, LI Xinshi2, GOU Min1, LI Kun1. Feature extraction of the elevator guide shoe vibration signal based on SVD optimizing LMD[J]. Journal of Vibration and Shock, 2017, 36(22): 166-171

参考文献

[1] 张进，冯志鹏，褚福磊. 滚动轴承故障特征的时间-小波能量谱提取方法[J]. 机械工程学报，2011,47（17）：44-49.
    ZHANG Jin, FENG Zhi-peng, Qu Fu-lei. Rolling bearing fault feature with time - the wavelet energy spectrum extraction method[J]. Journal of Mechanical Engineering, 2011, 47(17): 44-49.
[2] 杨江天，周培钰. 经验模态分解和Laplace小波在机车柴油机齿轮系故障诊断中的应用[J]. 机械工程学报,2011,47（7）：109-115.
    YANG Jiang-tian, ZHOU Pei-yu. Empirical mode decomposition and Laplace wavelet in the application of locomotive diesel engine gear fault diagnosis[J]. Journal of Mechanical Engineering, 2011, 47(7): 109-115.
[3] 何田,林意洲,郝普刚,等.局部均值分解在齿轮故障诊断中的应用研究[J]. 振动与冲击,2011,30(6):196-201.
    HE Tian, LIN Yi-zhou, HAO Pu-gang, et al. Local mean decomposition in the application of gear fault diagnosis research[J]. Vibration and Shock, 2011, 30(6): 196-201.
[4] Jonathan S Smith. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface, 2005, 2(5):12.
[5] KIM Y A, PHALAK R. Atrust prediction framework in rating-based experience sharing social networks without a Web of trust[J]. Information Sciences,2012,191(1):128-145.
[6] FOULIRAS P. A novel reputation-based model for e-commerce[J]. Operational Research, 2014, 13(1):113-138.
[7] 甘早斌,曾灿,李开,等.电子商务下的信任网络构造与优化[J].计算机学报，2012,35（1）：27-37.
    GAN Zao-bin, ZENG Can, LI Kai, et al. Construction and optimization of trust network in e-commerce environment[J]. Chinese Journal of Computers, 2012, 35(1):27-37.
[8] 张少中,方朝曦,陈军敢,等.基于社会网络的电子商务信任社区聚类模型[J].浙江大学学报：工学版,2013,47(4):654-661.
    ZHANG Shao-zhong, FANG Chao-xi, CHEN Jun-gan, et al. Community clustering model for e-commerce trust based on social network[J]. Journal of Zhejiang University:Engineering Science, 2013,47(4):654-661.
[9] YAHYAOUI H, ZHIOUA S. Bootstrapping trust of Web services through behavior observation[J]. Knowledge and Information Systems,2013, 37(2): 389-416.
[10] 赵学智,叶邦彦,林颖. 奇异值分解对轴承振动信号中调幅特征信息的提取[J].北京理工大学学报,2011,31(5):572-577.
    ZHAO Xue-zhi, YE Bang-yan, Lin Ying. Amplitude modulation feature extraction of bearing vibration signal using singular value decomposition[J]. Transactions of Beijing Institude of Technology,2011,31(5):572-577.
[11] 李葵,范玉刚,吴建德.基于二次奇异值分解与VPMCD的故障诊断方法[J].计算机工程,2015,41(4):181-186.
    LI Kui, FAN Yu-gang, Wu Jian-de. Fault diagnosis method based on quadratic singular value decomposition and VPMCD[J]. Computer Engineering,2015,41(4):181- 186.
[12] 赵学智,叶邦彦,陈统坚. 矩阵构造对奇异值分解信号处理效果的影响[J]. 华南理工大学学报:自然科学版,2008,36(9):86-93.
    ZHAO Xue-zhi, YE Bang-yan, CHEN Tong-jian. Influence of matrix creation way on signal processing[J]. Journal of South China University: Natural Science Edition,2008,36(9):86-93.
[13] 赵学智，叶邦彦，陈统坚.基于奇异值曲率普的有效奇异值选择[J].华南理工大学学报：自然科学版,2010,38(6):11-18,23.
    ZHAO Xue-zhi, YE Bang-yan, CHEN Tong-jian. Selection of effective singular values based on curvature spectrum of singular values[J]. Journal of South China University:Natural Science Edition,2010, 38(6): 11-18,23.
[14] 李葵,范玉刚,吴建德.基于SVD和TKEO的轴承振动信号特征提取[J].计算机工程与应用,2014,50(17): 195-199.
    LI Kui, FAN Yu-gang, WU Jian-de. Feature extraction of bearing vibration signal based on SVD and TKEO. Computer Engineering and Applications, 2014,50(17):195-199.
[15] 程军圣,张亢,杨宇. 基于噪声辅助分析的总体局部均值分解方法[J]. 机械工程学报, 2011,47(3):55-62.
    CHENG Jun-sheng, ZHANG Kang, YANG Yu. Ensemble local mean decomposition method based on noise-assisted analysis[J]. Journal of Mechanical Engineering, 2011, 47(3): 55-62.
[16] 王天金,冯志鹏,郝如江,等.基于Teager能量算子的滚动轴承故障诊断研究[J].振动与冲击,2012,31(2):1-5.
    WANG Tian-jin, FENG Zhi-peng, HAO Ru-jiang, et al. Fault diagnosis of rolling element bearing bsed on Teager energy operator[J]. JJournal of vibration and shock,2012,31(2): 1-5.
[17] 程军圣，张亢，杨宇，等. 局部均值分解与经验模式分解的对比研究[J]. 振动与冲击,2009,28(5)13-16.
    CHENG Jun-sheng, ZHANG Kang, YANG Yu, et al. The comparative study of local mean decomposition and empirical model decomposition[J]. Journal of vibration and shock, 2009, 28(5)13-16.