基于最大相关峭度解卷积的滚动轴承早期故障诊断
|
摘要
滚动轴承早期故障振动信号微弱,并且受环境噪声影响严重,特征信号提取困难。针对这一问题,提出了最大相关峭度解卷积方法来提取轴承故障的特征信号。通过计算信号的最大相关峭度值,估算出感兴趣的解卷积周期T,选择合适的时延步数M,对故障信号做最大相关峭度解卷积,并对最大相关峭度解卷积滤波后的信号进行包络解调,提取出滚动轴承的故障特征,实现了滚动轴承的早期故障诊断。仿真和实验验证了该方法在滚动轴承故障诊断中的有效性。
The faulty vibration signal of rolling element bearing at initial stage is generally very weak and affected by environment noise seriously. So it is difficult to extract the fault feature. In order to solve this problem,the method of maximum correlated kurtosis deconvolution( MCKD) is proposed. Firstly,the interested deconvolutive period T is estimated by calculating the maximum correlated kurtosis value of signals; Then,the appropriate M-shift is selected to do the MCKD for the fault signal; Finally,the fault feature of rolling-bearing is extracted through envelope demodulation analysis method,and is diagnosed as the incipient fault for this rolling-bearing. Its effectiveness is verified by the simulations and tests.
The faulty vibration signal of rolling element bearing at initial stage is generally very weak and affected by environment noise seriously. So it is difficult to extract the fault feature. In order to solve this problem,the method of maximum correlated kurtosis deconvolution( MCKD) is proposed. Firstly,the interested deconvolutive period T is estimated by calculating the maximum correlated kurtosis value of signals; Then,the appropriate M-shift is selected to do the MCKD for the fault signal; Finally,the fault feature of rolling-bearing is extracted through envelope demodulation analysis method,and is diagnosed as the incipient fault for this rolling-bearing. Its effectiveness is verified by the simulations and tests.
引文
[1]MCFADDEN P D,SMITH J D.Vibration monitioring of rolling element bearings by the high-frequency resonance technique-a review[J].Tribology International,1984,17(1):3-10.
[2]KANKAR P K,SATISH C.SHARMA,HARSHA S P.Rolling element bearing fault diagnosis using wavelet transform[J].Neurocomputing,2011,74(10):1638-1645.
[3]吴强,孔凡让,何清波,等.基于小波变换和ICA的滚动轴承早期故障诊断[J].中国机械工程.2012,23(7):835-840.WU Q,KONG F R,HE Q B,et al.Early fault diagnosis of rolling element bearings based on wavelet transform and independent component analysis[J].Chinese Journal of Mechanical Engineering,2012,23(7):835-840.
[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.CHENG J S,ZHENG J D,YANG Y.A nonstationary signal analysis approach:the local characteristic-scale decomposition method[J].Journal of Mechanical Engineering,2012,25(2):215-220.
[5]YAGUO LEI,JING LIN,ZHENGJIA HE,MING J.ZUO.A review on empirical mode decomposition in fault diagnosis of rotating machinery[J].Mechanical System and Signal Processing,2013,35(1-2):108-126.
[6]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海交通大学学报,2002,36(6):799-802.JIANG M,CHEN J.Demodulation analysis for cyclic auto-correlation function.Journal of Shanghai Jiao Tong University,2002,36(6):799-802.
[7]周宇,陈进,董广明,等.基于循环双谱二次切片分析的滚动轴承故障诊断研究[J].振动与冲击,2012,31(18):24-28.ZHOU Y,CHEN J,DONG G M,et al.Fault diagnosis of a roll-ing element bearing based on twice-slice of a cyclic bispec-trum[J].Journal of Vibration and Shock.2012,31(18):24-28.
[8]郭瑜,郑华文,高艳,等.基于谱峭度的滚动轴承包络分析[J].振动、测试与诊断,2011,31(4):517-521.GUO Y,ZHENG H W,GAO Y,et al.Envelope analysis of rolling bearing based on spectral kurtosis[J].Journal of Vibration,Measurement&Diagnosis,2011,31(4):517-521.
[9]DONOHO D.On minimum entropy deconvolution:Proceedings of the 2nd Applied Times Series Sympsium.Tulsa,Oklahoma:[s.n.],1980.
[10]王宏超,陈进,董广明.基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报.2013,49(1):88-94.WANG H C,CHEN J,DONG G M.Fault diagnosis method for rolling bearing’s weak fault based on minimum entropy deconvolution and sparse decomposition[J].Journal of Mechanical Engineering,2013,49(1):88-94.
[11]GEOFF L,MCDONALD,QING ZHAO,et al.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems and Signal Processing,2012,33:237-255.
[12]冷军发.基于盲源分离的齿轮箱低频故障特征提取方法研究[D].焦作:河南理工大学,2016.LENG J F.Research on low-frequency fault feature ex-traction for gearbox based on blind source separation[D].Jiaozuo:Henan Polytechnic University,2016.
[13]冷军发,杨鑫,荆双喜.最小熵解卷积在滚动轴承早期故障诊断中的应用[J].机械传动.2015,39(8):189-192.LENG J F,YANG X,JING S X.Application of minimum en-tropy deconvolution in the rolling element bearing fault diagnosis[J].Mechanical Drive.2015,39(8):189-192.
[14]CASE WESTERN RESERVE UNIVERSITY.Bearing Data Center Website[DB/OL](2016-04-06)[2017-08-20].http://www.eecs.wru.edu/laboratory/bearing.
[2]KANKAR P K,SATISH C.SHARMA,HARSHA S P.Rolling element bearing fault diagnosis using wavelet transform[J].Neurocomputing,2011,74(10):1638-1645.
[3]吴强,孔凡让,何清波,等.基于小波变换和ICA的滚动轴承早期故障诊断[J].中国机械工程.2012,23(7):835-840.WU Q,KONG F R,HE Q B,et al.Early fault diagnosis of rolling element bearings based on wavelet transform and independent component analysis[J].Chinese Journal of Mechanical Engineering,2012,23(7):835-840.
[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.CHENG J S,ZHENG J D,YANG Y.A nonstationary signal analysis approach:the local characteristic-scale decomposition method[J].Journal of Mechanical Engineering,2012,25(2):215-220.
[5]YAGUO LEI,JING LIN,ZHENGJIA HE,MING J.ZUO.A review on empirical mode decomposition in fault diagnosis of rotating machinery[J].Mechanical System and Signal Processing,2013,35(1-2):108-126.
[6]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海交通大学学报,2002,36(6):799-802.JIANG M,CHEN J.Demodulation analysis for cyclic auto-correlation function.Journal of Shanghai Jiao Tong University,2002,36(6):799-802.
[7]周宇,陈进,董广明,等.基于循环双谱二次切片分析的滚动轴承故障诊断研究[J].振动与冲击,2012,31(18):24-28.ZHOU Y,CHEN J,DONG G M,et al.Fault diagnosis of a roll-ing element bearing based on twice-slice of a cyclic bispec-trum[J].Journal of Vibration and Shock.2012,31(18):24-28.
[8]郭瑜,郑华文,高艳,等.基于谱峭度的滚动轴承包络分析[J].振动、测试与诊断,2011,31(4):517-521.GUO Y,ZHENG H W,GAO Y,et al.Envelope analysis of rolling bearing based on spectral kurtosis[J].Journal of Vibration,Measurement&Diagnosis,2011,31(4):517-521.
[9]DONOHO D.On minimum entropy deconvolution:Proceedings of the 2nd Applied Times Series Sympsium.Tulsa,Oklahoma:[s.n.],1980.
[10]王宏超,陈进,董广明.基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报.2013,49(1):88-94.WANG H C,CHEN J,DONG G M.Fault diagnosis method for rolling bearing’s weak fault based on minimum entropy deconvolution and sparse decomposition[J].Journal of Mechanical Engineering,2013,49(1):88-94.
[11]GEOFF L,MCDONALD,QING ZHAO,et al.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems and Signal Processing,2012,33:237-255.
[12]冷军发.基于盲源分离的齿轮箱低频故障特征提取方法研究[D].焦作:河南理工大学,2016.LENG J F.Research on low-frequency fault feature ex-traction for gearbox based on blind source separation[D].Jiaozuo:Henan Polytechnic University,2016.
[13]冷军发,杨鑫,荆双喜.最小熵解卷积在滚动轴承早期故障诊断中的应用[J].机械传动.2015,39(8):189-192.LENG J F,YANG X,JING S X.Application of minimum en-tropy deconvolution in the rolling element bearing fault diagnosis[J].Mechanical Drive.2015,39(8):189-192.
[14]CASE WESTERN RESERVE UNIVERSITY.Bearing Data Center Website[DB/OL](2016-04-06)[2017-08-20].http://www.eecs.wru.edu/laboratory/bearing.