基于EEMD奇异值熵的滚动轴承故障诊断方法
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摘要
为充分利用振动信号进行故障辨识,提出一种基于集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)奇异值熵判据的滚动轴承故障诊断方法。首先,对滚动轴承的振动信号进行EEMD分解获得若干个本征模态函数(intrinsic mode function,简称IMF),并根据一种IMF分量故障信息含量的评价指标(即峭度、均方差和欧氏距离)选出能够表征原始信号状态的分量进行信号重构;其次,利用奇异值分解技术对重构信号进行处理,结合信息熵算法求取其奇异值熵;最后,利用奇异值熵的大小判断滚动轴承的故障类别。用美国西储大学滚动轴承振动信号对所述方法进行验证的结果表明,相比传统的EMD奇异值熵故障诊断方法,本方法能够清晰的划分出滚动轴承不同工作状态的类别特征区间,而且具有更高的故障诊断精度。
In the light of fault identification,a rolling bearing fault diagnosis method is proposed to make full use of the vibration signal.The improvement is based on ensemble empirical mode decomposition(EEMD)singular value entropy criterion.First,the intrinsic mode functions(IMFs)are created based on the EEMD decomposition of the vibration signal of a rolling bearing.The representative fault information is selected from the IMF to reconstruct the original signal in terms of evaluation index,such as kurtosis,mean square error,and Euclidean distance.Then the singular value entropy is obtained by combining the information entropy method to determine the fault category of the rolling.The results show that the proposed method can distinguish the different characteristics of a rolling bearing under different types of work characteristics of the interval with a higher fault diagnosis accuracy than the traditional method.
In the light of fault identification,a rolling bearing fault diagnosis method is proposed to make full use of the vibration signal.The improvement is based on ensemble empirical mode decomposition(EEMD)singular value entropy criterion.First,the intrinsic mode functions(IMFs)are created based on the EEMD decomposition of the vibration signal of a rolling bearing.The representative fault information is selected from the IMF to reconstruct the original signal in terms of evaluation index,such as kurtosis,mean square error,and Euclidean distance.Then the singular value entropy is obtained by combining the information entropy method to determine the fault category of the rolling.The results show that the proposed method can distinguish the different characteristics of a rolling bearing under different types of work characteristics of the interval with a higher fault diagnosis accuracy than the traditional method.
引文
[1]刘中磊,于德介,刘坚.基于故障特征频率的阶比双谱方法及其在滚动轴承故障诊断中的应用[J].中国电机工程学报,2013,33(33):123-129.Liu Zhonglei,Yu Dejie,Liu Jian.Order bispectrum analysis based on fault characteristic frequency and its application to the fault diagnosis of rolling bearings[J].Proceedings of the CSEE,2013,33(33):123-129.(in Chinese)
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[9]郑直,姜万录,胡浩松,等.基于EEMD形态谱和KFCM聚类集成的滚动轴承故障诊断方法研究[J].振动工程学报,2015,28(2):324-330.Zheng Zhi,Jiang Wanlu,Hu Haosong,et al.Research on fault diagnosis method of rolling bearing based on EEMD shape spectrum and KFCM clustering ensemble[J].Journal of Vibration Engineering,2015,28(2):324-330.(in Chinese)
[10]郑近德,陈敏均,程军圣,等.多尺度模糊熵及其在滚动轴承故障诊断中的应用[J].振动工程学报,2014,27(1):145-151.Zheng Jinde,Chen Minjun,Cheng Junsheng,et al.Multi scale fuzzy entropy and its application in fault diagnosis of rolling bearing[J].Journal of Vibration Engineering,2014,27(1):145-151.(in Chinese)
[11]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.(in Chinese)
[12]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.
[13]Hou Zujun.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.SVDcomponent-envelope detection method and its application in the incipient fault diagnosis of rolling bearing[J].Journal of Vibration Engineering,2014,27(5):794-800.(in Chinese)
[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,2007:230-231.
[4]Wu Zhaohua,Huang N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[5]胡爱军,马万里,唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J].中国电机工程学报,2012,32(11):106-111.Hu Aijun,Ma Wanli,Tang Guiji.Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and kurtosis criterion[J].Proceedings of the CSEE,2012,32(11):106-111.(in Chinese)
[6]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.
[7]Fégeant 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.
[8]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.(in Chinese)
[9]郑直,姜万录,胡浩松,等.基于EEMD形态谱和KFCM聚类集成的滚动轴承故障诊断方法研究[J].振动工程学报,2015,28(2):324-330.Zheng Zhi,Jiang Wanlu,Hu Haosong,et al.Research on fault diagnosis method of rolling bearing based on EEMD shape spectrum and KFCM clustering ensemble[J].Journal of Vibration Engineering,2015,28(2):324-330.(in Chinese)
[10]郑近德,陈敏均,程军圣,等.多尺度模糊熵及其在滚动轴承故障诊断中的应用[J].振动工程学报,2014,27(1):145-151.Zheng Jinde,Chen Minjun,Cheng Junsheng,et al.Multi scale fuzzy entropy and its application in fault diagnosis of rolling bearing[J].Journal of Vibration Engineering,2014,27(1):145-151.(in Chinese)
[11]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.(in Chinese)
[12]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.
[13]Hou Zujun.Adaptive singular value decomposition in wavelet domain for image deoising[J].Pattern Recognition,2003,36(8):1747-1763.
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