基于非线性多参数的旋转机械故障诊断方法
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摘要
应用关联维数、李亚谱诺夫指数等非线性多参数对旋转机械故障诊断进行研究。对所采集的模拟旋转机械振动故障信号,运用相空间重构理论对其时间序列重构。为使重构相空间能充分地反映系统运动特征,对不同故障信号的时间延迟与嵌入维数确定问题进行研究,计算出不同故障信号的关联维数、李亚谱诺夫指数、复杂度和近似熵四个非线性特征量。在此基础上对四个非线性参数进行融合,并定义为非线性度,用这一特征量对故障信号特征进行提取与识别。由于非线性度是关联维数、李亚谱诺夫指数、复杂度和近似熵多参数的综合,更有利于分析识别故障信号,增强可靠性。研究表明:故障类型不同,非线性度指数不同,验证了这一非线性特征量是表征不同故障信息的有效参数。此研究为复杂旋转机械故障诊断提供一种识别方法。
The nonlinear multi-parameters of the correlation dimension and Lyapunov exponent and so on are applied in the research of the fault diagnosis of rotating machines.By using theory of phase space reconstruction,simulating fault signal of rotating machine is reconstructed.In order to reconstruct the phase space which can be adequately reflect the movement characteristics of the system,the time delay and embedding dimension are discussed emphatically,the four nonlinear values of correlation dimension,Lyapunov exponent,complexity and approximate entropy are calculated.On this basis,four nonlinear parameters are fused,and the characteristic quantities of non-linearity is introduced for extraction and recognition of the fault signal characteristic.Because the nonlinearity is the syntheses of multi parameters of correlation dimension,Lyapunov exponent,complexity and the approximate entropy,it will be more conducive to recognize and analyze fault signal,to enhance the reliability.Studies shows that,the fault type is different,nonlinearity is significantly different,which verifies that the nonlinear feature quantities are effective parameters for fault information,and thus a more effective way is provided for studying the fault diagnosis of complexity rotating machinery.
The nonlinear multi-parameters of the correlation dimension and Lyapunov exponent and so on are applied in the research of the fault diagnosis of rotating machines.By using theory of phase space reconstruction,simulating fault signal of rotating machine is reconstructed.In order to reconstruct the phase space which can be adequately reflect the movement characteristics of the system,the time delay and embedding dimension are discussed emphatically,the four nonlinear values of correlation dimension,Lyapunov exponent,complexity and approximate entropy are calculated.On this basis,four nonlinear parameters are fused,and the characteristic quantities of non-linearity is introduced for extraction and recognition of the fault signal characteristic.Because the nonlinearity is the syntheses of multi parameters of correlation dimension,Lyapunov exponent,complexity and the approximate entropy,it will be more conducive to recognize and analyze fault signal,to enhance the reliability.Studies shows that,the fault type is different,nonlinearity is significantly different,which verifies that the nonlinear feature quantities are effective parameters for fault information,and thus a more effective way is provided for studying the fault diagnosis of complexity rotating machinery.
引文
[1]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2002.LüJinhu,LU Junan,CHEN Shihua.Chaotic time seriesanalysis and its application[M].Wuhan:WuhanUniversity Press,2002.
[2]侯荣涛,闻邦椿,周飙.基于现代非线性理论的汽轮发电机组故障诊断技术研究[J].机械工程学报,2005,41(2):142-147.HOU Rongtao,WEN Bangchun,ZHOU Biao.Study onfault diagnosis tech-nique to turbo unit based on modernnonlinear theories[J].Journal of Mechanical Engineering,2005,41(2):142-147.
[3]陶新民,徐晶,杜宝祥,等.基于小波域广义高斯分布的轴承故障诊断方法[J].机械工程学报,2009,45(10):61-67.TAO Xinmin,XU Jing,DU Baoxiang,et al.Bearing faultdiagnosis based on wavelet-domain generalized gaussiandistribution[J].Journal of Mechanical Engineering,2009,45(10):61-67.
[4]FRASTER A M,WINNEY H.Independent coordinatesfor strange attractors from mutual information[J].Reviews of Modern Physics,1993,65(4):1331-1392.
[5]FRASTER A M.Information and entropy in strangeattractors[J].IEEE Trans.Inf.Theory,1989,35:245-262.
[6]CAO Liangyue.Practical method for determiningembedding dimension of a scalar time series[J].PhysicaD,1977,110(5):43-50.
[7]王福来,达庆利.复杂性度量方法的改进及其在证券市场的应用[J].系统工程学报,2007,36(5):455-460.WANG Fulai,DA Qingli.Improvement of complexitymeasure method and its application to the stock exchangemarket[J].Journal of Systems Engineering,2007,36(5):455-460.
[8]解幸幸,李舒,张春利,等.Lempel-Ziv复杂度在非线性检测中的应用研究[J].复杂系统与复杂性科学,2005,31(3):61-66.XIE Xingxing,LI Shu,ZHANG Chunli,et al.Study onthe application of Lempel-Ziv complexity in the nonlineardetecting[J].Complex Systems and Complexity Science,2005,31(3):61-66.
[9]LEMPEL A,ZIY J.On complexity of finite sequence[J].IEEE Transactions on Information Theory,1976,22(1):75-81.
[10]吕悦军,陆远忠.用算法复杂性分析时间序列[J].中国地震,1993,9(3):229-234.LüYuejun,LU Yuanzhong.Characterization of timesequence by algorithmic complexity[J].EarthquakeResearch in China,1993,9(3):229-234.
[11]WOLF A,SWIFT J B,SWINNEV L,et al.DeterminingLyapunov exponents from a time series[J].Physica D,1985,16:285-317.
[12]SATO S,SANO M,SAWADA Y.Resonance-likephenomena in Lyapunov calculations from datareconstructed by the time delay method[J].Prog.Theor.Phys,1987,77:1-5.
[13]STEVEN M,PINCUS A.Approximate entropy as ameasure of system complexity[J].Proc.Natl.Acad.Sci.USA,1996,93:20883-20889.
[2]侯荣涛,闻邦椿,周飙.基于现代非线性理论的汽轮发电机组故障诊断技术研究[J].机械工程学报,2005,41(2):142-147.HOU Rongtao,WEN Bangchun,ZHOU Biao.Study onfault diagnosis tech-nique to turbo unit based on modernnonlinear theories[J].Journal of Mechanical Engineering,2005,41(2):142-147.
[3]陶新民,徐晶,杜宝祥,等.基于小波域广义高斯分布的轴承故障诊断方法[J].机械工程学报,2009,45(10):61-67.TAO Xinmin,XU Jing,DU Baoxiang,et al.Bearing faultdiagnosis based on wavelet-domain generalized gaussiandistribution[J].Journal of Mechanical Engineering,2009,45(10):61-67.
[4]FRASTER A M,WINNEY H.Independent coordinatesfor strange attractors from mutual information[J].Reviews of Modern Physics,1993,65(4):1331-1392.
[5]FRASTER A M.Information and entropy in strangeattractors[J].IEEE Trans.Inf.Theory,1989,35:245-262.
[6]CAO Liangyue.Practical method for determiningembedding dimension of a scalar time series[J].PhysicaD,1977,110(5):43-50.
[7]王福来,达庆利.复杂性度量方法的改进及其在证券市场的应用[J].系统工程学报,2007,36(5):455-460.WANG Fulai,DA Qingli.Improvement of complexitymeasure method and its application to the stock exchangemarket[J].Journal of Systems Engineering,2007,36(5):455-460.
[8]解幸幸,李舒,张春利,等.Lempel-Ziv复杂度在非线性检测中的应用研究[J].复杂系统与复杂性科学,2005,31(3):61-66.XIE Xingxing,LI Shu,ZHANG Chunli,et al.Study onthe application of Lempel-Ziv complexity in the nonlineardetecting[J].Complex Systems and Complexity Science,2005,31(3):61-66.
[9]LEMPEL A,ZIY J.On complexity of finite sequence[J].IEEE Transactions on Information Theory,1976,22(1):75-81.
[10]吕悦军,陆远忠.用算法复杂性分析时间序列[J].中国地震,1993,9(3):229-234.LüYuejun,LU Yuanzhong.Characterization of timesequence by algorithmic complexity[J].EarthquakeResearch in China,1993,9(3):229-234.
[11]WOLF A,SWIFT J B,SWINNEV L,et al.DeterminingLyapunov exponents from a time series[J].Physica D,1985,16:285-317.
[12]SATO S,SANO M,SAWADA Y.Resonance-likephenomena in Lyapunov calculations from datareconstructed by the time delay method[J].Prog.Theor.Phys,1987,77:1-5.
[13]STEVEN M,PINCUS A.Approximate entropy as ameasure of system complexity[J].Proc.Natl.Acad.Sci.USA,1996,93:20883-20889.
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