针对于弱信号在齿轮故障中难以提取问题,提出了一种基于级联双稳随机共振 (Cascaded Bistable Stochastic Resonance,简称CBSR)降噪和局部均值分解(Local Mean Decomposition,简称LMD)齿轮故障的诊断方法。随机共振可有效削弱信号中的噪声,利用噪声增强故障信号的微弱特征;LMD方法可自适应将复杂信号分解为若干个具有一定物理意义上PF分量之和,适合处理多分量调幅调频信号。首先将振动信号进行CBSR消噪处理,然后对消噪信号进行LMD分解,通过PF分量的幅值谱找到齿轮的故障频率。通过齿轮磨损故障诊断的工程应用,表明该方法可以有效提取齿轮故障微弱特征,实现齿轮箱的早期故障诊断。
Abstract
Based on the difficulty of extracting the weak signal in gear fault diagnosis, the method of gear fault diagnosis based on cascaded bistable stochastic resonance(CBSR)denoising and local mean decomposition(LMD)was studied . Stochastic resonance can remove noise in the signals effectively and make use of noise to strengthen the weak fault feature ;The complicated signal can be decomposed into several stationary PF (product function) components with reality meanings by LMD, so it is very suitable to analyze the multi-component amplitude-modulated and frequency-modulated signal. First CBSR was employed as the pretreatment to remove noise in vibration signals and then the denoised signal was decomposed by LMD, the fault frequency of gear was found through the amplitude spectrums of the PF components. Through the engineering application of the fault diagnosis on gear wear demonstrated that this method can extracting the weak feature of gear fault effectively and realize the early gear fault diagnosis.
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参考文献
[1] 毕 果,陈 进. 基于谱相关的齿轮振动监测技术研究[J].振动与冲击, 2009, 28(7): 17-21.
BI Guo,CHEN Jin. The study of gear vibration based on the Spectrum[J]. Journal of Vibration and Shock,2009, 28(7): 17-21.
[2] BENZI R,SUTERA A,VULPIANI A. The mechanism of stochastic resonance[J]. Journal of Physics A :Mathematical and General,1981,14:453-457.
[3] 赵艳菊,王太勇,冷永刚,等. 级联双稳随机共振降噪下的经验模式分解[J]. 天津大学学报, 2009, 42(2): 123-128.
ZHAO Yanju, WANG Taiyong,LENG Yonggang.Empirical mode decomposition based on cascaded bistable stochastic resonance denoising[J]. Journal of Tianjin University, 2009, 42(2): 123-128.
[4] Cohen L. Time-frequency distribution-areview[J].Proceedings of the IEEE, 1989, 77(7): 941—981.
[5] 寿海飞. 基于小波分析的齿轮故障诊断研究[D].杭州.浙江工业大学,2007.
TAO Haifei. Fault Diagnosis for Gear Based on Wavelet Analysis[D].Hang Zhou. Zhejiang University of Technology,2007.
[6] Huang N,Long S R. A new view of nonlinear water wave: the Hilbert spectrum[J]. Ann. Rev. Fluid Mech. 1999,31: 417-57.
[7] 王衍学,何正嘉,訾艳阳,等. 基于LMD的时频分析方法及其机械故障诊断应用研究[J].振动与冲击,2012,31( 9) : 9-12.
WANG Yanxue,HE Zhengjia,ZI Yanyang. Several key issues of local mean decomposition method used in mechanical fault diagnosis[J].Journal of Vibration and Shock,2012,31( 9) : 9-12.
[8] Smith Jonathan S. The localmean decomposition and its ap-plication to EEG perception data[J]. Journal of the Royal Society Interface, 2005, 2(5): 444-450.
[9] 程军圣,张 亢,杨 宇. 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 Junsheng,ZHANG Kang, YANG Yu. The comparison of the local mean decomposition method and the empirical mode decomposition method[J]. Journal of Vibration and Shock,2009,28( 5) : 13-16.
[10] 孙伟,熊邦书,黄建平,等. 小波包降噪与 LMD 相结合的滚动轴承故障诊断方法[J]. 振动与冲击, 2012, 31(18): 153-156.
SUN Wei,XIONG Bangshu,HUANG Jianping. Fault diagnosis of a rolling bearing using Wavelet packet de-noising and LMD[J]. Journal of Vibration and Shock, 2012, 31(18): 153-156.
[11] 冷永刚, 王太勇. 二次采样用于随机共振从强噪声中提取弱信号的数值研究[J]. 物理学报, 2003, 52(10): 2432-2437.
LENG Yonggang,WANG Taiyong. The study of two sampling used to extraction of weak signal from strong noise [J]. Acta Physica Sinica, 2003, 52(10): 2432-2437.
[12] 雷亚国,韩 东,林 京.自适应随机共振新方法及其在故障诊断中的应用[J].机械工程学报,2012,48( 7) : 62-67.
LEI Yaguo, HAN Dong,LIN Jing. New Adaptive Stochastic Resonance Method and Its Application to Fault Diagnosis[J]. Journal of Mechanical Engineering, 2012,48( 7) : 62-67.
[13] 张亢, 程军圣, 杨宇. 基于有理样条函数的局部均值分解方法及其应用[J]. 振动工程学报, 2011, 24(1): 97-103.
ZHANG Kang, CHENG Junsheng,YANG Yu. The local mean decomposition method based on rational spline and its application[J]. Journal of Vibration Engineering, 2009, 2011, 24(1): 97-103.
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