EWT多尺度排列熵与GG聚类的轴承故障辨识方法
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摘要
针对滚动轴承故障信号具有非线性、非平稳性特点导致的故障类别难以辨识问题,提出一种基于经验小波变换、多尺度排列熵、GG(Gath-Geva,简称GG)聚类算法相结合的故障诊断方法。首先,采用经验小波变换对滚动轴承的原始信号进行分解、得到若干个固有模态分量,初步提取滚动轴承的状态特征值;其次,通过相关性分析选择最优模态分量,并在多个尺度下计算其排列熵值;最后,运用主成分分析对高维熵值特征向量进行可视化降维、并输入到GG聚类算法中,实现对滚动轴承的故障辨识。与其他模式组合方法进行比较的结果表明,本研究提出的故障辨识方法具有聚类结果的类内紧致性更好的优点。
In the light of the identification of the faults type of rolling bearing,which is hard due to the non-linear and non-stationary characteristics of the fault signals,a method of fault identification is proposed.It consists of the experience wavelet transform(EWT),multi-scale permutation entropy(MPE)and GG(Gath-Geva)clustering algorithm.First of all,the original signals of rolling bearing are decomposed into many intrinsic mode components based on the EWT decomposition.Then,the state features of the rolling bearing are preliminary extracted;the optimal modal component is selected with correlation analysis,and the permutation entropy is calculated in multiple scales.Finally,the principal component analysis(PCA)is used to reduce the dimension of the entropy feature vector for visualization,and low features subset is introduced into the GG clustering algorithm to realize the fault diagnosis of the rolling bearing.Comparisons with other mode combination method show that the proposed fault diagnosis method has certainly advantages,which better fault recognition effect.
In the light of the identification of the faults type of rolling bearing,which is hard due to the non-linear and non-stationary characteristics of the fault signals,a method of fault identification is proposed.It consists of the experience wavelet transform(EWT),multi-scale permutation entropy(MPE)and GG(Gath-Geva)clustering algorithm.First of all,the original signals of rolling bearing are decomposed into many intrinsic mode components based on the EWT decomposition.Then,the state features of the rolling bearing are preliminary extracted;the optimal modal component is selected with correlation analysis,and the permutation entropy is calculated in multiple scales.Finally,the principal component analysis(PCA)is used to reduce the dimension of the entropy feature vector for visualization,and low features subset is introduced into the GG clustering algorithm to realize the fault diagnosis of the rolling bearing.Comparisons with other mode combination method show that the proposed fault diagnosis method has certainly advantages,which better fault recognition effect.
引文
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[10]郑近德,程军圣,杨宇.基于LCD和排列熵的滚动轴承故障诊断[J].振动、测试与诊断,2014,34(5):802-806.Zheng Jinde,Cheng Junsheng,Yang Yu.A rolling bearing fault diagnosis method based on LCD and permutation entropy[J].Journal of Vibration,Measurement&Diagnosis,2014,34(5):802-806.(in Chinese)
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[13]郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646.Zheng Jinde,Cheng Junsheng,Yang Yu.Multi-scale permutation entropy and its rolling bearing fault diagnosis[J].China Mechanical Engineering,2013,24(19):2641-2646.(in Chinese)
[14]张淑清,包红燕,李盼,等.基于RQA与GG聚类的滚动轴承故障识别[J].中国机械工程,2015,26(10):1385-1390.Zhang Shuqing,Bao Hongyan,Li Pan,et al.Fault diagnosis of rolling bearing based on rqa and gg clustering[J].China Mechanical Engineering,2015,26(10):1385-1390.(in Chinese)
[15]张立国,李盼,李梅梅,等.基于ITD模糊熵和GG聚类的滚动轴承故障诊断[J].仪器仪表学报,2014,35(11):2624-2632.Zhang Guoli,Li Pan,Li Meimei,et al.Fault diagnosis of rolling bearing based on ITD fuzzy entropy and GGclustering[J].Chinese Journal of Scientific Instrument,2014,35(11):2624-2632.(in Chinese)
[16]许凡,方彦军,张荣.基于EEMD模糊熵的PCA-GG滚动轴承聚类故障诊断[J].计算机集成制造系统,2016,22(11):2631-2642.Xu Fan,Fang Yanjun,Zhang Rong.PCA-GG rolling bearing clustering fault diagnosis based on EEMD fuzzy entropy[J].Computer Integrated Manufacturing Systems,2016,22(11):2631-2642.(in Chinese)
[17]蔡艳平,李艾华,王涛,等.基于EMD-Wigner-Ville的内燃机振动时频分析[J].振动工程学报,2010,23(4):430-437.Cai Yanping,Li Aihua,Wang Tao,et al.I.C engine vibration time-frequency analysis based on EMD-Wigner-Ville[J].Journal of Vibration Engineering,2010,23(4):430-437.(in Chinese)
[18]李沁雪,廖晓文,张清华,等.基于EWT和多尺度熵的轴承特征提取及分类[J].轴承,2016(1):48-52.Li Qinxue,Liao Xiaowen,Zhang Qinghua,et al.Feature extraction and classification of bearings based on ewt and multi-scale entropy[J].Bearing,2016(1):48-52.(in Chinese)
[19]Loparo K A.Bearings vibration data set,case western reserve university[EB/OL].(2011-04-12)[2016-12-08].http:∥csegroups.case.edu/bearingdatacenter/home.
[2]张志刚,石晓辉,施全,等.基于改进EMD和谱峭度法滚动轴承故障特征提取[J].振动、测试与诊断,2013,33(3):478-482.Zhang Zhigang,Shi Xiaohui,Shi Quan,et al.Fault feature extraction of rolling element bearing based on improved EMD and spectral kurtosis[J].Journal of Vibration,Measurement&Diagnosis,2013,33(3):478-482.(in Chinese)
[3]李志农,朱明,褚福磊,等.基于经验小波变换的机械故障诊断方法研究[J].仪器仪表学报,2014(11):2423-2432.Li Zhinong,Zhu Ming,Chu Fulei,et al.Mechanical fault diagnosis method based on empirical wavelet transform[J].Chinese Journal of Scientific Instrument,2014(11):2423-2432.(in Chinese)
[4]黄南天,张书鑫,蔡国伟,等.采用EWT和OCSVM的高压断路器机械故障诊断[J].仪器仪表学报,2015,36(12):2773-2781.Huang Nantian,Zhang Shuxin,Cai Guowei,et al.Mechanical fault diagnosis of high voltage circuit breakers utilizing empirical wavelet transform and oneclass support vector machine[J].Chinese Journal of Scientific Instrument,2015,36(12):2773-2781.(in Chinese)
[5]Gilles J.Empirical wavelet transform[J].IEEE Transaction on Signal Processing,2013,61(16):3999-4010.
[6]冯博,李辉,郑海起.基于经验小波变换的轴承故障诊断研究[J].轴承,2015(12):53-58.Feng Bo,Li Hui,Zheng Haiqi.Study on fault diagnosis for bearings based on empirical wavelet transform[J].Bearing,2015(12):53-58.(in Chinese)
[7]向玲,李媛媛.经验小波变换在旋转机械故障诊断中的应用[J].动力工程学报,2015,35(12):975-981.Xiang Lin,Li Yuanyuan.Application of empirical wavelet transform in fault diagnosis of rotary mechanisms[J].Journal of Chinese Society of Power Engineering,2015,35(12):975-981.(in Chinese)
[8]郑近德,潘海洋,潘紫微,等.自适应无参经验小波变换及其在转子故障诊断中的应用[J].中国机械工程,2016,27(16):2218-2224.Zheng Jinde,Pan Haiyang,Pan Ziwei,et al.Adaptive parameterless empirical wavelet transform(ewt)and its applications to fault diagnosis of rotor system[J].China Mechanical Engineering,2016,27(16):2218-2224.(in Chinese)
[9]祝文颖,冯志鹏.基于改进经验小波变换的行星齿轮箱故障诊断[J].仪器仪表学报,2016,37(10):2193-2201.Zhu Wenying,Feng Zhipeng.Fault diagnosis of planetary gearbox based on improved empirical wavelet transform[J].Chinese Journal of Scientific Instrument,2016,37(10):2193-2201.(in Chinese)
[10]郑近德,程军圣,杨宇.基于LCD和排列熵的滚动轴承故障诊断[J].振动、测试与诊断,2014,34(5):802-806.Zheng Jinde,Cheng Junsheng,Yang Yu.A rolling bearing fault diagnosis method based on LCD and permutation entropy[J].Journal of Vibration,Measurement&Diagnosis,2014,34(5):802-806.(in Chinese)
[11]Yan Ruqiang,Liu Yongbin,Gao R X.Permutation entropy:a nonlinear statistical measure for status characterization of rotary machines[J].Mechanical Systems and Signal Processing,2012,29:474-484.
[12]Aziz W,Arif M.Multiscale Permutation Entropy of Physiological Time Series[C]∥Proceeding of IEEE International Multitopic Conference.Karachi,Pakistan:IEEE,2005:368-373.
[13]郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646.Zheng Jinde,Cheng Junsheng,Yang Yu.Multi-scale permutation entropy and its rolling bearing fault diagnosis[J].China Mechanical Engineering,2013,24(19):2641-2646.(in Chinese)
[14]张淑清,包红燕,李盼,等.基于RQA与GG聚类的滚动轴承故障识别[J].中国机械工程,2015,26(10):1385-1390.Zhang Shuqing,Bao Hongyan,Li Pan,et al.Fault diagnosis of rolling bearing based on rqa and gg clustering[J].China Mechanical Engineering,2015,26(10):1385-1390.(in Chinese)
[15]张立国,李盼,李梅梅,等.基于ITD模糊熵和GG聚类的滚动轴承故障诊断[J].仪器仪表学报,2014,35(11):2624-2632.Zhang Guoli,Li Pan,Li Meimei,et al.Fault diagnosis of rolling bearing based on ITD fuzzy entropy and GGclustering[J].Chinese Journal of Scientific Instrument,2014,35(11):2624-2632.(in Chinese)
[16]许凡,方彦军,张荣.基于EEMD模糊熵的PCA-GG滚动轴承聚类故障诊断[J].计算机集成制造系统,2016,22(11):2631-2642.Xu Fan,Fang Yanjun,Zhang Rong.PCA-GG rolling bearing clustering fault diagnosis based on EEMD fuzzy entropy[J].Computer Integrated Manufacturing Systems,2016,22(11):2631-2642.(in Chinese)
[17]蔡艳平,李艾华,王涛,等.基于EMD-Wigner-Ville的内燃机振动时频分析[J].振动工程学报,2010,23(4):430-437.Cai Yanping,Li Aihua,Wang Tao,et al.I.C engine vibration time-frequency analysis based on EMD-Wigner-Ville[J].Journal of Vibration Engineering,2010,23(4):430-437.(in Chinese)
[18]李沁雪,廖晓文,张清华,等.基于EWT和多尺度熵的轴承特征提取及分类[J].轴承,2016(1):48-52.Li Qinxue,Liao Xiaowen,Zhang Qinghua,et al.Feature extraction and classification of bearings based on ewt and multi-scale entropy[J].Bearing,2016(1):48-52.(in Chinese)
[19]Loparo K A.Bearings vibration data set,case western reserve university[EB/OL].(2011-04-12)[2016-12-08].http:∥csegroups.case.edu/bearingdatacenter/home.