自适应变分模态分解的齿轮箱故障诊断研究
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摘要
强噪环境下,复合故障特征提取难度更大,VMD(Variational Mode Decomposition)被大量应用于齿轮箱故障诊断中;但是它属于参数型分解方法,K过大或过小都会导致过分解或欠分解现象,因此分解的层数需要自适应的确定。提出了一种多点峭度和VMD的复合故障特征提取方法。考虑到多点峭度可以提取多故障的冲击性周期的个数;周期性冲击个数决定VMD的分解层数K,通过VMD处理后,进一步通过FFT确定故障特征。所提出的自适应复合故障特征提取方法和EEMD(En?semble Empirical Mode Decomposition)对比分析,验证了它可以克服模态混叠的特征,通过对实测性信号处理进一步确定了此方法的有效性。最终确定了齿轮剥落和轴承滚珠等复合故障特征。
In the noisy environment,the composite fault feature extraction is more difficult. The VMD is widely used in gearbox fault diagnosis,but it is a parametric decomposition method. If K is too large or too small,it will lead to over-decomposition or under-decomposition. The number of layers needs to be determined adaptively,a multi-point kurtosis-VMD(Variational Mode Decomposition)composite fault feature extraction method is proposed. Considering the multi-point kurtosis,the number of impact cycles of multiple faults can be extracted,the number of periodic impacts determines the number K of decomposition layers of the VMD,and after VMD processing,the fault features are further determined by FFT. The proposed adaptive composite fault feature extraction method and Ensemble Empirical Mode Decomposition(EEMD)comparison analysis verify that it can overcome the characteristics of modal aliasing. The effectiveness of this method is further determined by the measured signal processing. The composite fault characteristics such as gear spalling and bearing balls are finally determined.
In the noisy environment,the composite fault feature extraction is more difficult. The VMD is widely used in gearbox fault diagnosis,but it is a parametric decomposition method. If K is too large or too small,it will lead to over-decomposition or under-decomposition. The number of layers needs to be determined adaptively,a multi-point kurtosis-VMD(Variational Mode Decomposition)composite fault feature extraction method is proposed. Considering the multi-point kurtosis,the number of impact cycles of multiple faults can be extracted,the number of periodic impacts determines the number K of decomposition layers of the VMD,and after VMD processing,the fault features are further determined by FFT. The proposed adaptive composite fault feature extraction method and Ensemble Empirical Mode Decomposition(EEMD)comparison analysis verify that it can overcome the characteristics of modal aliasing. The effectiveness of this method is further determined by the measured signal processing. The composite fault characteristics such as gear spalling and bearing balls are finally determined.
引文
[1]WANG Zhijian,HAN Zhennan.A novel procedure for diagnosing multiple faults in rotating machinery[J].ISA Transactions,2015,55:208-218.
[2]WANG Zhijian,WANG Junyuan,KOU Yanfei,et al.Weak fault diagnosis of wind turbine gearboxes based on MED-LMD[J].Entropy,2017,19(6):1-11.
[3]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.
[4]王志坚,王俊元,赵志芳,等.基于MKurt-MOMEDA的齿轮箱复合故障特征提取[J].振动.测试与诊断,2017,37(4):830-834.
[5]王志坚,韩振南,刘邱祖,等.基于MED-EEMD的滚动轴承微弱故障特征提取[J].农业工程学报,2014,30(23):70-78.
[6]DRAGOMIRETSKIY K,ZOSSO D.Variational mode decom position[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.
[7]WANG Jianguo,CHEN Shuai,ZHANG Chao.Fault diagnosis method of gear based on vmd and multi-feature fusion[J].Journal of Mechanical Transmission,2017(3):32.
[8]XIANG Ling,ZHANG Lijia.Rolling bearing fault feature extraction based on the VMD and 1.5-dimensional Teager energy spectrum[J].Journal of Vibration and Shock,2017,35(18):98-104.
[9]MCDONALD Geoff L,ZHAO Qing.Multipoint optimal minimum entropy deconvolution and convolution fix:application to vibration fault detection[J].Mechanical Systems&Signal Processing,2016,82:461-477.
[2]WANG Zhijian,WANG Junyuan,KOU Yanfei,et al.Weak fault diagnosis of wind turbine gearboxes based on MED-LMD[J].Entropy,2017,19(6):1-11.
[3]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.
[4]王志坚,王俊元,赵志芳,等.基于MKurt-MOMEDA的齿轮箱复合故障特征提取[J].振动.测试与诊断,2017,37(4):830-834.
[5]王志坚,韩振南,刘邱祖,等.基于MED-EEMD的滚动轴承微弱故障特征提取[J].农业工程学报,2014,30(23):70-78.
[6]DRAGOMIRETSKIY K,ZOSSO D.Variational mode decom position[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.
[7]WANG Jianguo,CHEN Shuai,ZHANG Chao.Fault diagnosis method of gear based on vmd and multi-feature fusion[J].Journal of Mechanical Transmission,2017(3):32.
[8]XIANG Ling,ZHANG Lijia.Rolling bearing fault feature extraction based on the VMD and 1.5-dimensional Teager energy spectrum[J].Journal of Vibration and Shock,2017,35(18):98-104.
[9]MCDONALD Geoff L,ZHAO Qing.Multipoint optimal minimum entropy deconvolution and convolution fix:application to vibration fault detection[J].Mechanical Systems&Signal Processing,2016,82:461-477.