基于变分模态分解奇异值熵的滚动轴承微弱故障辨识方法
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摘要
针对滚动轴承微弱故障难以识别的问题,提出一种基于变分模态分解(Variational Mode Decomposition,VMD)与奇异值熵融合的滚动轴承微弱故障辨识方法。该方法对滚动轴承的振动信号进行VMD分解获得4个本征模态函数(Intrinsic Mode Function,IMF),并根据一种均方差-欧氏距离指标选择出含丰富故障信息的IMF分量进行信号重构;对重构信号进行奇异值分解获得奇异值对角阵,进而结合信息熵理论求取对角阵的奇异值熵;利用奇异值熵的大小区分滚动轴承的工作状态和故障类型。用美国西储大学的滚动轴承振动信号对所述方法进行验证的结果表明:相比传统EMD奇异值熵故障诊断方法,该方法能够更清晰地划分出滚动轴承微弱故障的类别区间,有助于实现微弱故障类型的准确辨识,为滚动轴承微弱故障诊断提供了一种可靠的评估依据。
Aiming at problems of rolling bearings' weak failures being difficult to identify,a rolling bearing weak fault identification method based on fusion of the variational mode decomposition( VMD) and the singular value entropy was proposed. Firstly,VMD was done for vibration signal of a rolling bearing to obtain 4 intrinsic mode functions( IMFs). According to a mean square deviation-Euclidean distance index,IMF components containing rich fault information were chosen to perform signal reconstruction. Then,the singular value decomposition( SVD) was done for the reconstructed signal to obtain the diagonal matrix of singular values,and the diagonal matrix's singular value entropy was obtained using the information entropy theory. Finally,the singular value entropy was used to distinguish working state and fault type of the bearing. The new method was verified with rolling bearing vibration signals of American West Storage University. The results showed that compared with the traditional EMD singular value entropy fault diagnosis method,this method can more clearly delineate rolling bearing weak fault classes to correctly identify a bearing's weak fault. This study provided a reliable basis for weak fault diagnosis of rolling bearings.
Aiming at problems of rolling bearings' weak failures being difficult to identify,a rolling bearing weak fault identification method based on fusion of the variational mode decomposition( VMD) and the singular value entropy was proposed. Firstly,VMD was done for vibration signal of a rolling bearing to obtain 4 intrinsic mode functions( IMFs). According to a mean square deviation-Euclidean distance index,IMF components containing rich fault information were chosen to perform signal reconstruction. Then,the singular value decomposition( SVD) was done for the reconstructed signal to obtain the diagonal matrix of singular values,and the diagonal matrix's singular value entropy was obtained using the information entropy theory. Finally,the singular value entropy was used to distinguish working state and fault type of the bearing. The new method was verified with rolling bearing vibration signals of American West Storage University. The results showed that compared with the traditional EMD singular value entropy fault diagnosis method,this method can more clearly delineate rolling bearing weak fault classes to correctly identify a bearing's weak fault. This study provided a reliable basis for weak fault diagnosis of rolling bearings.
引文
[1]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 Hongchao, CHEN Jin, DONG Guangming. 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.
[2]冷永刚,郑安总,范胜波. SVD分量包络检测方法及其在滚动轴承早期故障诊断中的研究[J].振动工程学报,2014,27(5):794-800.LENG Yonggang, ZHENG Anzong, FAN Shengbo. SVD component-envelope detection method and its application in the incipient fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering,2014,27(5):794-800.
[3]何正嘉,訾艳阳,张西宁.现代信号处理及工程应用[M].西安:西安交通大学出版社,2007.
[4]WU Z H, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2011,1(1):1-41.
[5]RILLING G,FLANDRIN P. On the influence of sampling on the empirical mode decomposition[C]∥IEEE International Conference on Acoustics, Speech and Signal Processing.Toulouse:IEEE,2006.
[6]DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531-544.
[7]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358-3365.LIU Changliang,WU Yingjie,ZHEN Chenggang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J]. Proceedings of the CSEE,2015,35(13):3358-3365.
[8]唐贵基,王晓龙.变分模态分解方法及其在滚动轴承早期故障诊断中的应用[J].振动工程学报,2016,29(4):638-648.TANG Guiji, WANG Xiaolong. Variational mode decomposition method and its application on incipient fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering,2016,29(4):638-648.
[9]KIM S H,SOEDEL W,LEE J M. Analysis of the beating response of bell type structures[J]. Journal of Sound&Vibration,1994,173(4):517-536.
[10]FGEANT O. Structural mobilities for the edge-excited,semi-infinite cylindrical shell using a perturbation method[J]. Journal of Sound&Vibration,2001,248(3):499-519.
[11]于德介,陈淼峰,程军圣,等.基于EMD的奇异值熵在转子系统故障诊断中的应用[J].振动与冲击,2006,25(2):24-26.YU Dejie,CHEN Miaofeng,CHENG Junsheng,et al. Fault diagnosis approach for rotor system based on EMD method and sigular value entropy[J]. Journal of Vibration and Shock,2006,25(2):24-26.
[12]张超,陈建军,杨立东,等.奇异值熵和支持向量机的齿轮故障诊断[J].振动、测试与诊断,2011,31(5):600-604.ZHANG Chao,CHEN Jianjun,YANG Lidong,et al. Gear fault diagnosis based on singular value entropy and support vector machines[J]. Journal of Vibration Measurement&Diagnosis,2011,31(5):600-604.
[13]王奉涛,陈守海,闫达文,等.基于流形-奇异值熵的滚动轴承故障特征提取[J].振动、测试与诊断,2016,36(2):288-294.WANG Fengtao,CHEN Shouhai,YAN Dawen,et al. Fault feature extraction of rolling bearings based on manifold singular value entropy[J]. Journal of Vibration Measurement&Diagnosis,2016,36(2):288-294.
[14]李天云,陈昌雷,周博,等.奇异值分解和最小二乘支持向量机在电能质量扰动识别中的应用[J].中国电机工程学报,2008,28(34):124-128.LI Tianyun,CHEN Changlei,ZHOU Bo,et al. Application of SVD and LS-SVD in power quality disturbances classification[J]. Proceedings of the CSEE,2008,28(34):124-128.
[15]KONSSTANTINIDES K, YAO K. Statistical analysis of effective singular values in matrix rank determination[J].Acoustics Speech&Signal Processing IEEE Transactions on,1988,36(5):757-763.
[16]HOU Z. Adaptive singular value decomposition in wavelet domain for image deoising[J]. Pattern Recognition,2003,36(8):1747-1763.
[2]冷永刚,郑安总,范胜波. SVD分量包络检测方法及其在滚动轴承早期故障诊断中的研究[J].振动工程学报,2014,27(5):794-800.LENG Yonggang, ZHENG Anzong, FAN Shengbo. SVD component-envelope detection method and its application in the incipient fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering,2014,27(5):794-800.
[3]何正嘉,訾艳阳,张西宁.现代信号处理及工程应用[M].西安:西安交通大学出版社,2007.
[4]WU Z H, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2011,1(1):1-41.
[5]RILLING G,FLANDRIN P. On the influence of sampling on the empirical mode decomposition[C]∥IEEE International Conference on Acoustics, Speech and Signal Processing.Toulouse:IEEE,2006.
[6]DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531-544.
[7]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358-3365.LIU Changliang,WU Yingjie,ZHEN Chenggang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J]. Proceedings of the CSEE,2015,35(13):3358-3365.
[8]唐贵基,王晓龙.变分模态分解方法及其在滚动轴承早期故障诊断中的应用[J].振动工程学报,2016,29(4):638-648.TANG Guiji, WANG Xiaolong. Variational mode decomposition method and its application on incipient fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering,2016,29(4):638-648.
[9]KIM S H,SOEDEL W,LEE J M. Analysis of the beating response of bell type structures[J]. Journal of Sound&Vibration,1994,173(4):517-536.
[10]FGEANT O. Structural mobilities for the edge-excited,semi-infinite cylindrical shell using a perturbation method[J]. Journal of Sound&Vibration,2001,248(3):499-519.
[11]于德介,陈淼峰,程军圣,等.基于EMD的奇异值熵在转子系统故障诊断中的应用[J].振动与冲击,2006,25(2):24-26.YU Dejie,CHEN Miaofeng,CHENG Junsheng,et al. Fault diagnosis approach for rotor system based on EMD method and sigular value entropy[J]. Journal of Vibration and Shock,2006,25(2):24-26.
[12]张超,陈建军,杨立东,等.奇异值熵和支持向量机的齿轮故障诊断[J].振动、测试与诊断,2011,31(5):600-604.ZHANG Chao,CHEN Jianjun,YANG Lidong,et al. Gear fault diagnosis based on singular value entropy and support vector machines[J]. Journal of Vibration Measurement&Diagnosis,2011,31(5):600-604.
[13]王奉涛,陈守海,闫达文,等.基于流形-奇异值熵的滚动轴承故障特征提取[J].振动、测试与诊断,2016,36(2):288-294.WANG Fengtao,CHEN Shouhai,YAN Dawen,et al. Fault feature extraction of rolling bearings based on manifold singular value entropy[J]. Journal of Vibration Measurement&Diagnosis,2016,36(2):288-294.
[14]李天云,陈昌雷,周博,等.奇异值分解和最小二乘支持向量机在电能质量扰动识别中的应用[J].中国电机工程学报,2008,28(34):124-128.LI Tianyun,CHEN Changlei,ZHOU Bo,et al. Application of SVD and LS-SVD in power quality disturbances classification[J]. Proceedings of the CSEE,2008,28(34):124-128.
[15]KONSSTANTINIDES K, YAO K. Statistical analysis of effective singular values in matrix rank determination[J].Acoustics Speech&Signal Processing IEEE Transactions on,1988,36(5):757-763.
[16]HOU Z. Adaptive singular value decomposition in wavelet domain for image deoising[J]. Pattern Recognition,2003,36(8):1747-1763.
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