LCD和SWT在滚动轴承故障诊断中的应用
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摘要
针对强噪声背景下滚动轴承故障诊断中存在的非平稳非线性信号特征提取这一难题,提出一种基于局部特征尺度分解(Local Characteristic-scale Decomposition,LCD)和同步压缩小波变换的方法(Synchrosqueezing Wavelet Transform, SWT),该方法首先对信号进行LCD分解,将其分解成多个內禀尺度函数(Intrinsic Scale Component,ISC),选取包含有效频率成分的ISC作为SWT的输入信号,使用SWT对其作进一步分析,从而提取有效频率特征。对强噪声背景下提取的滚动轴承外圈故障信号、内圈故障信号以及滚动体故障信号进行分析的结果表明,相比局部特征尺度分解、同步压缩小波变换等方法,该方法能够有效抑制噪声,从强噪声背景下提取出有效信号频率特征,从而能够有效判断滚动轴承的运转状况。同时该方法能够有效重构信号。
In order to overcome the difficulty of feature extraction of non-stationary faulty signals in rolling bearing fault diagnosis under strong noise background, a method based on local characteristic-scale decomposition and synchrosqueezing wavelet transform is proposed. Firstly, the measured vibration signals are processed with LCD and decomposes into a series of intrinsic scale component(ISC). Then a number of ISCs containing valid information components are selected for SWT and processing them by SWT so that we can extract the effective frequency characteristics. The analysis results from rolling bearing signals with out ring, inner ring and rolling body faults which in a strong noise background shows that comparing with LCD and SWT, the approach of synchrosqueezing wavelet transform based on LCD can effectively suppress the noise and extract the effective signal frequency characteristics. It also has a high time-frequency resolution for accurately determining the operation of rolling bearings. While the method can also effectively reconstruct the signal.
In order to overcome the difficulty of feature extraction of non-stationary faulty signals in rolling bearing fault diagnosis under strong noise background, a method based on local characteristic-scale decomposition and synchrosqueezing wavelet transform is proposed. Firstly, the measured vibration signals are processed with LCD and decomposes into a series of intrinsic scale component(ISC). Then a number of ISCs containing valid information components are selected for SWT and processing them by SWT so that we can extract the effective frequency characteristics. The analysis results from rolling bearing signals with out ring, inner ring and rolling body faults which in a strong noise background shows that comparing with LCD and SWT, the approach of synchrosqueezing wavelet transform based on LCD can effectively suppress the noise and extract the effective signal frequency characteristics. It also has a high time-frequency resolution for accurately determining the operation of rolling bearings. While the method can also effectively reconstruct the signal.
引文
[1] 曾海平.基于经验模态分解法的滚动轴承故障诊断系统研究[D].杭州:浙江大学,2005:1-3.ZENG HaiPing.The study of the rolling bearing fault diagnosis system based on empirical mode decomposition method [D].Hangzhou:Zhejiang University,2005:1-3 (In Chinese).
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[7] Ingrid Daubechies,Jianfeng Lu,Hau-Tieng Wu.Synchrosqueezed wavelet transforms:An empirical mode decomposition-like tool [J].Applied and Computational Harmonic Analysis,2011,30(1):243-261.
[8] Gaurav Thakur,Eugene Brevdo,Neven S.Fuc ˇkar,et al.The Synchrosqueezing algorithm for time-varying spectral analysis:Robustness properties and new paleoclimate applications [J].Signal Processing,2013,93(5):1079-1094.
[9] Roberto H.Herrera1,Jiajun Han1,Mirko van der Baan.Applications of the synchrosqueezing transform in seismic time-frequency analysis [J].Geophysics,2014,79(3):V55-V64.
[10] Chuan Li,Ming Liang.Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform [J].Mechanical Systems and Signal Processing,2012,26(1):205-217.
[11] Gaurav Thakur,Eugene Brevdo,Neven S.Fu,et al.The Synchrosqueezing algorithm for time-varying spectral analysis:Robustness properties and new paleoclimate applications [J].Signal Processing,2013,93(5):1079-1094.
[2] 张来斌,崔厚玺,王朝晖,等.基于信息熵神经网络的风力发电机故障诊断方法研究[J].机械强度,2009,31(1):132-135.ZHANG LaiBin,CUI HouXi,WANG ChaoHui,et al.Research on fault diagnosis method of wind turbine based on information entropy neural network [J].Journal of Mechanical Strength,2009,31(1):132-135.
[3] 李嶷,熊国良,张龙.经验模式分解法(LCD)在滚动轴承故障诊断中的应用[J].华东交通大学学报,2005,22(1):135-138.LI Wei,XIONG GuoLiang,ZHANG Long.Experience mode decomposition (LCD) is used in rolling bearing fault diagnosis [J].Journal of East China Jiaotong University;2005,22(1):135-138 (In Chinese)
[4] 程军圣,郑近德,杨宇.一种新的非平稳信号分析方法-局部特征尺度分解法[J].振动工程学报,2012,25(2):215-220.CHEN JunSheng,ZHENG JinDe,YANG Yu.A new nonstationary signal analysis method - local feature scale decomposition method [J].Journal of Vibration Engineering.2012,25(2):215-220 (In Chinese)
[5] 张美玲,胡晓.基于LCD和改进SVM的轴承故障诊断方法[J].测控技术与仪器仪表,2016,42(6):81-86.ZHANG MeiLing,HU Xiao.Bearing fault diagnosis method based on LCD and improved SVM [J].Measurement and Control Technology and Instrument,2016,42(6):81-86 (In Chinese)
[6] 王余奎,李洪儒,魏晓斌,等.基于局部特征尺度分解谱熵和 VPMCD 的液压泵退化状态识别[J].振动与冲击,2016,32(12):188-195.WANG YuKui,LI HongRu,WEI XiaoBin,et al.Based on local feature scale decomposition spectrum entropy and VPMCD hydraulic pump degradation status recognition [J].Journal of Vibration and Shock,2016,32(12):188-195 (In Chinese).
[7] Ingrid Daubechies,Jianfeng Lu,Hau-Tieng Wu.Synchrosqueezed wavelet transforms:An empirical mode decomposition-like tool [J].Applied and Computational Harmonic Analysis,2011,30(1):243-261.
[8] Gaurav Thakur,Eugene Brevdo,Neven S.Fuc ˇkar,et al.The Synchrosqueezing algorithm for time-varying spectral analysis:Robustness properties and new paleoclimate applications [J].Signal Processing,2013,93(5):1079-1094.
[9] Roberto H.Herrera1,Jiajun Han1,Mirko van der Baan.Applications of the synchrosqueezing transform in seismic time-frequency analysis [J].Geophysics,2014,79(3):V55-V64.
[10] Chuan Li,Ming Liang.Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform [J].Mechanical Systems and Signal Processing,2012,26(1):205-217.
[11] Gaurav Thakur,Eugene Brevdo,Neven S.Fu,et al.The Synchrosqueezing algorithm for time-varying spectral analysis:Robustness properties and new paleoclimate applications [J].Signal Processing,2013,93(5):1079-1094.