往复式压缩机多源冲击振动时频故障特征研究
|
摘要
往复压缩机结构复杂，激励源多，振动以多源冲击信号为主，表现出较强的非平稳、非高斯及非线性等复杂特性，采用传统振动信号分析方法无法有效提取往复压缩机故障信息，现已经成为往复压缩机故障诊断的瓶颈问题。振动信号的时频分析作为设备故障特征提取的先进手段，在旋转机械故障诊断过程中发挥着非常重要的作用。但是，随着故障诊断领域向着往复机械设备的延伸，传统的时频故障特征提取方法面临着挑战。针对往复压缩机复杂的振动信号，以短时傅氏变换、Wigner-Ville分布、小波变换及HHT变换等为基础的时频分析方法，都难以识别故障的有用信息，时频分布变得非常复杂，许多频率成分缺乏明确物理意义。本文针对往复式压缩机多源冲击振动特征，结合现代时频分析方法的优缺点，深入开展了往复式压缩机多源冲击振动时频故障特征研究。
首先，采用小波变换、HHT等现代时频处理方法对调幅、调频及调频调幅等仿真信号进行了时频特征分析，并应用于往复式压缩机的故障诊断，虽然取得一些效果，但仍然存在分辨率低、边界效应干扰等问题，难以满足实际应用。
然后，根据全局频率与瞬时频率两种极端性概念的局限性，提出了更具适用性的局部频率新概念，赋予多源冲击振动信号有明确物理意义的频率内涵，从这个概念出发，通过与自适应峰值分解方法结合，进一步与HHT时频分析方法进行仿真对比，结果表明，基于局部频率的时频分析方法更加有效揭示了多源冲击振动信号的特征，为往复压缩机故障诊断提供了一种新的手段。
最后，对往复式压缩机的基本工作原理、主要部件的运动学及动力学进行分析，并指出了往复压缩机测试信号的强非平稳性，根据上述仿真分析成果，将基于EMD的局部频率时频分析方法及基于自适应峰值分解的广义局部频率时频分析方法应用于往复式压缩机故障诊断，取得较为显著的效果，进一步验证了该方法的准确性和鲁棒性，为大型往复压缩机组故障诊断提供了更加丰富的故障特征信息。
Reciprocating compressor is complicated in structure, incentive, vibration source tomultiple source impact signal is given priority to, show strong nonstationarity andnon-gaussian and nonlinear and complicated characteristics, adopts the traditional vibrationsignal analysis method can not be extracted reciprocating compressor fault information, nowhas become the bottleneck of reciprocating compressor fault diagnosis problem. Vibrationsignal of time-frequency analysis as the equipment fault feature extraction of advanced means,in rotating machinery fault diagnosis process plays a very important role. However, with faultdiagnosis field to reciprocating machinery and equipment is outspread, the traditionaltime-frequency fault feature extraction method challenges. For reciprocating compressorcomplex vibration signals, with short time Fourier transform, Wigner-Ville distribution,wavelet transform and HHT transform as the basis of time-frequency analysis method, it isdifficult to identify fault useful information, time-frequency distribution become verycomplicated, many frequency component a lack of clear physical meaning. Aiming at thereciprocating compressor multi-source impact vibration characteristics, combining moderntime-frequency analysis method, the advantages and disadvantages of in-depth developmentbased on local frequency reciprocating compressor fault time-frequency characteristicsresearch.
First, by wavelet transform, HHT and other modern time-frequency processing method foramplitude modulation, frequency modulation and frequency modulation amplitudemodulation and the simulation signal on the time-frequency characteristic analysis, andapplied to fault diagnosis of reciprocating compressor, although have some effect, but thereare still resolution is low, the boundary effect interference problem, which is difficult to meetthe practical application.
Then, according to the global frequency and instantaneous frequency two extreme sexconcept limitations, this paper puts forward more applicability of local frequency new concept,giving multiple source impact vibration signal with definite physical meaning of frequencycontent, from this definition, through with adaptive peak decomposition method combines,further and HHT time-frequency analysis method, and the results show that the contrast,based on the local frequency of time-frequency analysis method is more effective than revealsthe source impact vibration signal characteristics, for reciprocating compressor fault diagnosisprovides a new means.
Finally, the reciprocating compressor basic working principle, main components of thekinematics and dynamics analysis, and points out that the reciprocating compressor test signalof strong nonstationarity, according to the simulation results, will be based on the localfrequency of time-frequency analysis method is applied to fault diagnosis of reciprocatingcompressor has more significant effect, further verified the method for large reciprocating compressor fault diagnosis to provide more rich the failure characteristics of information,improve the reciprocating compressor fault characteristics of the accuracy and robustness.
首先，采用小波变换、HHT等现代时频处理方法对调幅、调频及调频调幅等仿真信号进行了时频特征分析，并应用于往复式压缩机的故障诊断，虽然取得一些效果，但仍然存在分辨率低、边界效应干扰等问题，难以满足实际应用。
然后，根据全局频率与瞬时频率两种极端性概念的局限性，提出了更具适用性的局部频率新概念，赋予多源冲击振动信号有明确物理意义的频率内涵，从这个概念出发，通过与自适应峰值分解方法结合，进一步与HHT时频分析方法进行仿真对比，结果表明，基于局部频率的时频分析方法更加有效揭示了多源冲击振动信号的特征，为往复压缩机故障诊断提供了一种新的手段。
最后，对往复式压缩机的基本工作原理、主要部件的运动学及动力学进行分析，并指出了往复压缩机测试信号的强非平稳性，根据上述仿真分析成果，将基于EMD的局部频率时频分析方法及基于自适应峰值分解的广义局部频率时频分析方法应用于往复式压缩机故障诊断，取得较为显著的效果，进一步验证了该方法的准确性和鲁棒性，为大型往复压缩机组故障诊断提供了更加丰富的故障特征信息。
Reciprocating compressor is complicated in structure, incentive, vibration source tomultiple source impact signal is given priority to, show strong nonstationarity andnon-gaussian and nonlinear and complicated characteristics, adopts the traditional vibrationsignal analysis method can not be extracted reciprocating compressor fault information, nowhas become the bottleneck of reciprocating compressor fault diagnosis problem. Vibrationsignal of time-frequency analysis as the equipment fault feature extraction of advanced means,in rotating machinery fault diagnosis process plays a very important role. However, with faultdiagnosis field to reciprocating machinery and equipment is outspread, the traditionaltime-frequency fault feature extraction method challenges. For reciprocating compressorcomplex vibration signals, with short time Fourier transform, Wigner-Ville distribution,wavelet transform and HHT transform as the basis of time-frequency analysis method, it isdifficult to identify fault useful information, time-frequency distribution become verycomplicated, many frequency component a lack of clear physical meaning. Aiming at thereciprocating compressor multi-source impact vibration characteristics, combining moderntime-frequency analysis method, the advantages and disadvantages of in-depth developmentbased on local frequency reciprocating compressor fault time-frequency characteristicsresearch.
First, by wavelet transform, HHT and other modern time-frequency processing method foramplitude modulation, frequency modulation and frequency modulation amplitudemodulation and the simulation signal on the time-frequency characteristic analysis, andapplied to fault diagnosis of reciprocating compressor, although have some effect, but thereare still resolution is low, the boundary effect interference problem, which is difficult to meetthe practical application.
Then, according to the global frequency and instantaneous frequency two extreme sexconcept limitations, this paper puts forward more applicability of local frequency new concept,giving multiple source impact vibration signal with definite physical meaning of frequencycontent, from this definition, through with adaptive peak decomposition method combines,further and HHT time-frequency analysis method, and the results show that the contrast,based on the local frequency of time-frequency analysis method is more effective than revealsthe source impact vibration signal characteristics, for reciprocating compressor fault diagnosisprovides a new means.
Finally, the reciprocating compressor basic working principle, main components of thekinematics and dynamics analysis, and points out that the reciprocating compressor test signalof strong nonstationarity, according to the simulation results, will be based on the localfrequency of time-frequency analysis method is applied to fault diagnosis of reciprocatingcompressor has more significant effect, further verified the method for large reciprocating compressor fault diagnosis to provide more rich the failure characteristics of information,improve the reciprocating compressor fault characteristics of the accuracy and robustness.
引文
[1]林梅，孙嗣莹.活塞式压缩机原理[M].西安：交通大学出版社，1996，5~8.
[2] Shulin Liu, Youfu Tang, Jicheng Liu, Yinghui Liu. Research of fault feature extractionbased on high order cyclic statistics for reciprocating compressor gas valves[J]. AppliedMechanics and Materials,2011,44-47:2094~2098.
[3]高晶波，徐敏强，张嘉钟.2D12型往复式压缩机气阀故障诊断的时频分析方法[J].化工机械.2003，03：140~142.
[4]何正嘉，訾艳阳，孟庆丰等.机械设备非平稳信号的故障诊断原理与应用[M].北京:高等教育出版社，2001.
[5]夏松波,张嘉钟.旋转机械故障诊断技术的现状与展望[J].振动与冲击,1997,16(2):1-5.
[6]谭继勇，陈雪峰，何正嘉.冲击信号的随机共振自适应检测方法[J].机械工程学报，2010，46(23)：61~66.
[7]王迪生，杨乐之.活塞式压缩机结构[M].北京：机械工业出版社，1990.
[8]金涛.往复式活塞压缩机故障监测与诊断技术[J].流体机械，1999，27(11)：28~31.
[9]沈庆根，郑水英.设备故障诊断[M].北京：化学工业出版社，2006.
[10]高慎琴.化工机器[M].北京：化学工业出版社，1992.
[11]刘卫华.近十年来压缩机技术研究动向[J].压缩机技术，1998，150（4）：33~35.
[12]林京，刘红星，屈梁生.功率谱谱距及其模糊测度在气阀故障诊断中的应用[J].化工机械，1997，24（3）:168~170.
[13]高晶波，王日新，徐敏强等.2D12型往复式压缩机气阀故障诊断的声谱诊断方法[J].化工机械，2003，30（4）:202~205.
[14]高晶波，徐敏强，张嘉钟.2D12型往复式压缩机气阀故障诊断的时频分析方法[J].化工机械，2003，30（3）:140~143.
[15]袁小宏，屈梁生.小波分析及其在压缩机气阀故障检测中的应用研究[J].振动工程学报，1999，12（3）：410~425.
[16]林京，屈梁生.基于连续小波变换的奇异性检测与故障诊断[J].振动工程学报，2000，
[17]刘红星，林京.往复压缩机气阀故障的振动诊断方法[J].压缩机技术，1996，135（1）：32~34.
[18]刘红星，左洪福，姜澄宇等.信号频谱的二维向量及其应用[J].中国机械工程，1999，10（5）：537~539.
[19]刘卫华，郁永章.往复压缩机故障示功图诊断法研究[J].压缩机技术，2001，170（6）：9~12.
[20]何道清.天然气压缩机工况监测及诊断技术[J].西南石油学报，2001，23（4）：64~67
[21]林高平，吴丹青，王毅.利用压缩机缸盖振动信号识别缸内压力[J].压缩机技术，1994，127（5）：35~38.
[22]王金东，张嘉钟，杨敬源.2D12往复压缩机气缸内压力检测技术[J].压缩机技术，2002，175（5）：21~23.
[23]王琳.机械设备故障诊断与监测的常用方法及其发展趋势[J].武汉工业大学学报，2000，22（3）：62~65.
[24]丛蕊,刘树林,杨丽晶.关联维数在往复压缩机气阀故障诊断中的应用[J].压缩机技术,2007,(1):18-20.
[25]王金东,刘岩,赵海洋,等.基于延时嵌入法的往复压缩机关联维数提取技术[J].化工机械,2005,32(4):214-217.
[26]王金东,李新伟,张忠臣,等.往复压缩机多重分形故障特征提取[J].压缩机技术,2008,(1):12-14.
[27]刘彬,王霄,谢平.基于小波多重分形的复杂机械故障诊断方法研究[J].传感技术学报,2006,19(1):232-234.
[28]王金东,赵海洋,李新伟.基于多重分形的往复压缩机分级诊断研究[J].压缩机技术,2009,(1):1-4.
[29]刘树林,杨丽晶,马锐,等.近似熵在往复压缩机气阀故障诊断中的应用[J].压缩机技术,2005,(6):20-22.
[30] R.M.S.S. Abeysekera and B.Boashash. Time frequency domain feature of ECG signals:their application in P wave detection using the cross Wigner-Ville distribution[J]. Proc.IEEE ICASSP-89,1989:1524~1527.
[31][美]S.M.Kay, S.L.Marple.现代谱分析技术综述[J].强度与环境.1987，2：60~119.
[32]黄建国.现代谱分析的发展及应用(一)[J].数据采集与处理.1990，5(4)：47~55.
[33]黄建国.现代谱分析的发展及应用(二)[J].数据采集与处理.1991，6(1)：33~45.
[34][美]L科恩.时频分析：理论与应用[M].西安.西安交通大学出版社，2000.
[35] Childer D.G. Modern Spectrum Analysis. IEEE Press.1978.
[36]袁保宗.信号处理技术的若干进展[J].电信科学.1993，9(4)：2~8
[37] J.R. Fonolosa and C.L. Nikias. Wigner higher order moment spectra: definition,properities, computation and application to transient signal analysis. IEEE Trans SignalProcessing,1993,41:245~266.
[38] C.L.Nikias, M.R.Raghuveer. Bispectrum estimation: a digital signal processingframework. Proc. IEEE,1987,75：869~891.
[39] G.B.Gianaakis. Cumulants：a powerful tool in signal processing. Proc.IEEE,1987,75:1333~1334.
[40]张贤达.现代信号处理[M].北京：清华大学出版社，1995.
[41]刘庆云，李志舜，刘朝晖.时频分析技术及研究现状[J].计算机工程，2004，30(1)：171~173.
[42]陈卫，吕贵洲，梁四洋.参数化时频分析综述[J].现代雷达，2008，30(2)：61~64
[43] John M. O, Mostefa M and Boualem B. A New Discrete Analytic Signal for ReducingAliasing in the Discrete Wigner-Ville Distribution[J]. IEEE Transactions on SignalProcessing,2008,56(11):5427~5434.
[44] Jagmal S, Matteo S and Mihai D. Parametric Versus Non-Parametric Complex ImageAnalysis [C]. IEEE International Conference Proceedings on Geoscience and RemoteSensing,2009:9~12.
[45] Norden E. H, Zheng S and Steven R. L. The Empirical Mode Decomposition and theHilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis [J]. Proc. R.Soc. Lond. A,1998,454:903~995.
[46]程军圣，杨宇，于德介.局部均值分解方法及其在齿轮故障诊断中的应用[J].振动工程学报，2009，22(1)：76~83.
[47]杨建文，贾民平.希尔伯特-黄谱的端点效应分析及处理方法研究[J].振动工程学报，2006，19(2)：283~288.
[48]王秋生，段丹辉，袁梅.基于AR模型包络延拓的经验模态分解[J].数据采集与处理，2008，23(2)：205~210.
[49]程军圣，于德介，杨宇.Hilbert-Huang变换端点效应问题的探讨[J].振动与冲击，2005，24(6)：40~42.
[50]刘慧婷，张旻，程家兴.基于多项式拟合算法的EMD端点问题的处理[J].计算机工程与应用，2004，40(16)：84~86.
[51]邓拥军，王伟，钱成春等.EMD方法及Hilbert变换中边界问题的处理[J].科学通报，2001，46(3)：114~118.
[52]胡爱军，安连锁，唐贵基.Hilbert-Huang变换端点效应处理新方法[J].机械工程学报，2008，44(4)：154~158.
[53]王婷，杨莘元，李冰冰.一种改善EMD端点效应的新方法[J].哈尔滨理工大学学报，2009，14(5)：23~26.
[54]程军圣，于德介，杨宇.基于支持矢量回归机的Hilbert-Huang变换端点效应问题的处理方法[J].机械工程学报，2006，42(4)：23~31.
[55]杨永锋，吴亚锋，任兴民等.基于最大Lyapunov指数预测的EMD端点延拓[J].物理学报，2009，58(6)：3762~3746.
[56] Qi Keyu, He Zhengjia, Zi Yanyang. Cosine window-based boundary processingmethod for EMD and its application in rubbing fault diagnosis[J]. Mechanical Systemsand Signal Processing,2007,21(7):2750~2760.
[57] Zhao Jinping, Huang Daji. Mirror extending and circular spline function for empiricalmode decomposition Method[J].浙江大学学报（英文版）,2001,2(3):247~252.
[58]余磊，刘泉，经验模态分解中端点效应的抑制[J].武汉理工大学学报，2010，32(10)：151~154.
[59]杜陈艳，张榆锋，杨平等.经验模态分解边缘效应抑制方法综述[J].仪器仪表学报，2009，30(1)：54~59.
[60] Q. Chen, N.E. Huang, S. Riemen schneider, etc. A B-spline approach for empiricalmode decomposition[J]. Advances in Computational Mathematics,2006,24(1-4):171~195.
[61] G. G. S Pegram, M. C Peel, T. A. McMahon. Empirical mode decomposition usingrational splines: an application to rainfall time series[J]. Proc. R. Soc. A,2008,464(2094):1483~1501.
[62] Hawley Stephen D., Atlas Les E., Chizeck Howard J.. Some properties of an empiricalmode type signal decomposition algorithm[J]. IEEE Signal Process Lett,2010,17(1):24~27.
[63] Gabriel Rilling, Patrick Flandrin, Paulo Goncalves. On empirical mode decompositionand its algorithms[C]. IEEE-EURASIP Workshop on Nonlinear Signal and ImageProcessing NSIP-03, Grado (I),2003.
[64] J. Cheng, D. Yu, Y. Yang. Research on the intrinsic mode function (IMF) criterion inEMD method[J]. Mechanical Systems and Signal Processing,2006,20(4):817~824.
[65]胡劲松，杨世锡.基于能量的振动信号经验模态分解终止条件[J].振动、测试与诊断，2009，29(1)：19~22.
[66] G. Rilling, P. Flandrin. One or two frequencies? The empirical mode decompositionanswers[J]. IEEE Trans. Signal Processing,2008,56(1):85~95.
[67] Z. K. Peng, Peter W. Tse, F. L. Chu. A comparison study of improved Hilbert-Huangtransform and wavelet transform: Application to fault diagnosis for rolling bearing[J].Mechnical Systems and Signal Processing,2005,19(5):974~988.
[68] Z. K. Peng, Peter W. Tseb, F. L. Chu. An improved Hilbert-Huang transform and itsapplication in vibration signal analysis[J]. Journal of Sound and Vibration,2005,286(1-2):187~205.
[69] J.C. Echeverria, J. A. Crowe, M.S. Woolfson, etc. Application of empirical modedecomposition to heart rate variability analysis[J]. Medical&Biological Engineering&Computing,2001(39):471~479.
[70] John I. S., Ying S.. Assessment of chaotic parameters in nonstationaryelectrocardiograms by use of empirical mode decompostion[J]. Annals of BiomedicalEngineering,2004,32(10):1348~1354．
[71] Andrade A. O., Kyberd P. J., Taffler S. D.. A novel spectral representation ofelectromyographic signals[C]. Proceedings of the25th Annual International Conferenceof the IEEE EMBS Cancun,2003,3:2598~2601．
[72] H. Liang. Empirical mode decomposition: a method for analyzing neural data[J].Neurocomputing,2005,65-66:801~807.
[73] M. Bennett. Empirical mode decomposition and tissue harmonic imaging[J].Ultrasound in Med.&Biol.,2005,301(8):1051~1061.
[74] Hualou Liang, Qiu-Hua Lin, J. D. Chen. Application of the empirical modedecomposition to the analysis of esophageal manometric data in gastroesophagealreflux disease[C]. Proceeding of the26th Annual International Conference of the IEEEEMBS, San Francisco, CA, USA, September1-5,2004.
[75] J. C. Nunes, Y. Bouaoune, E. Delechelle, et al. Image analysis by bidimensionalempirical mode decomposition[J]. Image and Vision Computing,2003,21:1019~1026.
[76]徐文良，孟明，马玉良.HHT方法在人体下肢表面肌电信号分析中的应用[J].传感技术学报，2010，23(3)：297~302.
[77]韩民，彭玉华，张顺利等.基于经验模态分解的虹膜识别[J].光学学报，2010，30(2)：364~368.
[78] Zhang Y., Gao Y., Wang L., etc. The removal of wall components in doppler ultrasoundsignals by using the empirical mode decomposition algorithm[J]. IEEE Trans Biomed.Eng.,2007,54(9):1631~1642.
[79] L. Hadjileontiadis. Emprical mode decomposition and fractal dimension filter[J]. IEEEEngineering in Medicine and Biology Magazine,2007,26(1):30~39.
[80] H. Liang．Empirical mode decomposition of field potentials from macaque V4in visualspatial attention[J]. Biological Cybernetics,2005,92:380~392.
[81] Martin Arundell. Hilbert transform of voltammetric data[J]. ElectrochemistryCommunication,2004,6:366~372.
[82]王卫强.经验模态法在中国沿岸海平面变化中的应用研究[J].海洋学报，1999，21(6)：102~109.
[83] Per Gloersen, Norden Huang. Comparison of interannual intrinsic modes inhemispheric sea ice covers and other geophysical parameters[C]. IEEE Transactions onGeoscience and Remote Sensing，MAY2003,41(5):1062~1074.
[84] Paul A. Hwang, Norden E. Huang, David W. Wang. A note on analyzing nonlinear andnonstationary ocean wave data[J]. Applied Ocean Research,2003,25:187~193.
[85] A. D. Veltcheva, C. Guedes Soares. Identification of the components of wave spectraby the Hilbert Huang transform method[J]. Applied Ocean Research,2004,26:1~12.
[86]王国龙，吴中鼎，袁延茂等.经验模态分解方法在潮余流分离中的应用[J].海洋测绘，2010，30(2)：42~45.
[87]公茂盛，谢礼立.HHT方法在地震工程中的应用之初步探讨[J].世界地震工程,2003，19(3)：39~43.
[88]汪权，王建国，张鸣祥.基于Hilbert-Huang变换理论的结构振动主动变刚度频率控制[J]，应用力学学报，2010，27(2)：301~315.
[89]傅光耀，段晨东，刘健新.经验模态分解剔除结构地震响应位移漂移[J].振动工程学报，2009，22(4)：438~442.
[90] K. T. Coughlin，K. K. Tung.11-Year solar cycle in the stratosphere extracted by theempirical mode decomositiion method[J]. Advances in Space Research,2004,34:323~329.
[91]沈国际，陶利民，陈仲生.减速器振动信号的经验模态分解[J].航空学报，2005，26(1)：62~65.
[92] Loh C. H., Wu T. C., Huang N. E.. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics andstructual response[J]. Bulletin of the Seismological Society of America,2001,(91):1339~1357.
[93] R. Balocchi. Deriving the respiratory sinus arrhy thmia from the heartbeat time seriesusing empirical mode decomposition[J]. Chaos, Solitons and Fractas，2004，20：171~177．
[94]王学敏，黄方林.EMD方法在消除桥梁振动信号局部强干扰中的应用[J].机械强度，2005，27(1)：33~37．
[95] S. Loutridis. Forced vibration behaviour and crack detection of cracked beams usinginstantaneous frequency[J]. NDT&E International,2005,38:411~419．
[96] G. Kerschen, A. F. Vakakis, Y. S. Lee, etc. Toward a fundamental understanding of theHilbert-Huang transform in nonlinear structural dynamics[J]. Journal of Vibration andControl,2008,14(1-2):77~105.
[97] B. Ning, S. Qiyu, Y. Zhihua, etc. Robust image watermarking based on multibandwavelets and empirical mode decomposition[J]. IEEE Transactions on ImageProcessing,2007,16(8):1956~1966.
[98] E. Delechelle. Empirical mode decomposition synthesis of fractional processes in1D-and2D-space[J]. Image and Vision Computing,2005,23(9):799~806.
[99] J. C. Nunes, S. Guyout, E. Delechelle. Texture analysis based on local analysis of thebidimensional empirical mode decomposition[J]. Machine Vision and Applications,2005,16(3):177~188.
[100] Shaohui Chen, Hongbo Su, Renhua Zhang, etc. Improving empirical modedecomposit-ion using support vector machines for multifocus image fusion[J]. Sensors,2008,8(4):2500~2508.
[101] M. E. Montesinos． Hilbert-Huang analysis of BWR neutron detector signals:application to DR calculation and to corrupted signal analysis[J]. Annals of NuclearEnergy,2003,30(6):715~727.
[102] Chengjie Cai, Weixian Liu, Fu J. S., etc. Empirical mode decomposition ofmicro-doppler signature[C]．Radar Conference, IEEE International，2005,895~899.
[103]赵犁丰，王振芬等.基于经验模式分解的希尔伯特变换包络提取在机械故障诊断中的应用[J].青岛海洋大学学报，2002,32(6)：965-970.
[104] B. Liu, S. Riemenschneider, Y. Xu. Gearbox fault diagnosis using empirical modedecomposition and Hilbert spectrum[J]. Mechanical Systems and Signal Processing,2006,20(3):718~734.
[105]陈忠，郑时雄.基于经验模式分解（EMD）的齿轮箱齿轮故障诊断技术研究[J].振动工程学报，2003，16(2)：229-232.
[106] S. J. Loutridis. Damage detection in gear systems using empirical mode dedomposit-ion[J]. Engineering Structures,2004,26(2):1833~1841.
[107]胡劲松，杨世锡.基于HHT的旋转机械故障诊断方法研究[J].动力工程，2004，24(6)：845~851.
[108] Z.K. Peng, Peter W. Tse, F.L. Chu. A comparison study of improved Hilbert-Huangtransform and wavelet transform: Application to fault diagnosis for rolling bearing[J].Mechanical Systems and Signal Processing,2005,19(5):974~988.
[109] Ruqiang Yan, Robert X. Gao. Rotary machine health diagnosis based on empiricalmode decomposition[J]. Journal of Vibration and Acoustics,2008,130(2):021007-1~12.
[110] V.K. Rai, A.R. Mohanty. Bearing fault diagnosis using FFT of intrinsic modefunctions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007,21(6):2607~2615.
[111]杨宇，于德介，程军圣.基于经验模态分解包络谱的滚动轴承故障诊断方法[J].中国机械工程，2004，15(16)：1469~1471.
[112] Dejie Yu, Junsheng Chen, Yu Yang. Application of EMD method and Hilbert spectrumto the fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing,2005,19(2):259~270.
[113] Liu Yibing, Wu Qi, Ma Zhiyong, etc. An improved Hilbert-Huang transform and itsapplication in fault signal analysis[C]. Proceeding of the2006IEEE InternationalConferecne on Mechatronics and Automation,2006,2426~2431.
[114] Q. Gao, C. Duan, H. Fan, etc. Rotating machine fault diagnosis using empirical modedecomposition[J]. Mechanical Systems and Signal Processing,2008,22(5):1072~1081.
[115] Jianmin Zeng, Zhengyi Jiang, Taosen Li, etc. Gear local fault diagnosis with empiricalmode decomposition and Hilbert-Huang transform[J]. Advanced Materials Research,2011,199-200:899~904.
[116]李琳，张永祥，明延涛.EMD降噪的关联维数在齿轮故障诊断中的应用研究[J].振动与冲击，2009，28(4):145~148.
[117]汤宝平，蒋永华，张祥春.基于形态奇异值分解和经验模态分解的滚动轴承故障特征提取方法[J].机械工程学报，2010，46(5):37~42.
[118] Patrick Flandrin, Gabriel Rilling. Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2004,11(2):112~114.
[119]孔国杰，张培林，徐龙堂等.基于经验模态分解的自适应滤波算法及其应用[J].信号处理，2009，25(6):958~962.
[120]侯王宾，刘天琪，李兴源.基于经验模态分解滤波的低频振荡Prony分析[J].物理学报，2010，59(5):3531~3537.
[121]曹寅文，宋慎义，肖井华.运动后人体心肺节律同步关系及信号的耦合方向[J].物理学报，2010，59(7):5163-5168.
[122]杨光亮，朱元清，于海英.基于HHT的地震信号自动去噪算法[J].大地测量与地球动力学，2010，30(3):39~42.
[123]王强,周云龙,崔玉峰等.EMD与神经网络在气液两相流流型识别中的应用[J].
[124]林瑞霖，周平，基于EMD和神经网络的气阀机构故障诊断研究[J].海军工程大学学报，2008，20(2):48~51.
[125]程凌,陈前.基于经验模态分解与神经网络的结构故障诊断[J].振动、测试与诊断,2010,189~192.
[126]沈志熙,黄席樾,马笑潇.基于EMD和支持向量机的柴油机故障诊断[J].振动、测试与诊断,2010,30(1):19~22.
[127] Chang Che, Dan Hu. An approach based on Hilbert-Huang transform and supportvector machine for intelligent fault diagnosis[J]. Lecture Notes in ElectricalEngineering,2010,67(8):811~817.
[128]崔玲丽，康晨晖，胥永刚等.滚动轴承早期冲击性故障特征提取的综合算法研究[J].仪器仪表学报，2010，31(11)：2422~2427.
[129]邵毅敏，周晓君，欧家福等.增强型滤波及冲击性机械故障特征的提取[J].机械工程学报，2009，45(4)：166~171.
[130]章立军，阳建宏，徐金梧等.形态非抽样小波及其在冲击信号特征提取中的应用[J].振动与冲击，2007，26(10)：56~59.
[131]胡晓依，何庆复，王华胜.基于滤波器组分解的周期性振动冲击信号解调方法及其应用[J].振动与冲击，2008，27(8)：133~137.
[132]徐珍华，张来斌，段礼祥.时频域分析在往复压缩机活塞磨损故障诊断中的应用[J].压缩机技术，2010，(3)：1~3.
[133] M. Elhaj, F. Gu, A.D. Ball, etc. Numerical simulation and experimental study of atwo-stage reciprocating compressor for condition monitoring[J]. Mechanical Systemsand Signal Processing,2008,22(2):374~389.
[134]王江萍，鲍泽富.往复式压缩机振动信号频谱分析与故障诊断[J].石油机械，2008，36(8)：63~66.
[135]别锋锋，郭正刚，张志新.基于局域波时频谱的系统级故障诊断方法研究[J].仪器仪表学报，2008，29(5)：1092~1095.
[136] Shulin Liu, Xianming Wang, Hui Wang, eta.. Extension of Traditional Frequency andResearch on Time-Frequency Distribution[J]. Applied Mechanics and Materials,2011,44-47:2089~2093.
[137]刘树林，赵海峰，王辉等.局部频率概念及适用性分析[J].振动与冲击(S)，2008：202~204.
[138] Liu Shulin, Wang Hui, Ma Rui, eta.. Time-Frequency Distribution Based on LocalFrequency and EMD[C]. Proceedings of ICALIP2008International Conference,2008:112~117.
[139]胡昌华,张军波,夏军等.基于MATLAB的系统分析与设计－小波分析[M].西安:西安电子科技大学出版社,1999.
[140] Schlurmann T.. Spectral frequency analysis of nonlinear water waves derived from theHilbert-Huang transformation[J]. Journal of Offshore Mechanics and ArcticEngineering (JOMAE), American Society of Mechanical Engineers (ASME),2002,124(1):22-27.
[141] L. Cohen. Time frequency analysis[M]. Englewood Cliffs, NJ: Prentice-Hall,1995.
[142] Loughlin P. J., Tacer B.. Comments on the interpretation of instantaneousfrequency[C]. IEEE Singal Processing Letters,1997,4(5):123-125.
[143] L. Mandel. Interpretation of instrantaneous frequencies[J]. Amer. J. Phys., Oct.1974,42:840-846.
[144] Feldmann, M.. Nonlinear free vibration identification via the Hilbert transform[J].Journal of Sound and Vibration,1997,208(3):475-489.
[145] Roberts Michael J． Signals and Systems：Analysis Using Transform Methods andMATLAB[M]．NewYork：McGraw-Hill，2004．
[146] B．Boashash．Estimating and interpreting the instantaneous frequency of a signal-Part1：fundamentals[J]． Proceedings of the IEEE，1992，80(4)：520-538．
[147]李俊.往复压缩机状态监测系统的研究[D].武汉：华中科技大学，2004.
[2] Shulin Liu, Youfu Tang, Jicheng Liu, Yinghui Liu. Research of fault feature extractionbased on high order cyclic statistics for reciprocating compressor gas valves[J]. AppliedMechanics and Materials,2011,44-47:2094~2098.
[3]高晶波，徐敏强，张嘉钟.2D12型往复式压缩机气阀故障诊断的时频分析方法[J].化工机械.2003，03：140~142.
[4]何正嘉，訾艳阳，孟庆丰等.机械设备非平稳信号的故障诊断原理与应用[M].北京:高等教育出版社，2001.
[5]夏松波,张嘉钟.旋转机械故障诊断技术的现状与展望[J].振动与冲击,1997,16(2):1-5.
[6]谭继勇，陈雪峰，何正嘉.冲击信号的随机共振自适应检测方法[J].机械工程学报，2010，46(23)：61~66.
[7]王迪生，杨乐之.活塞式压缩机结构[M].北京：机械工业出版社，1990.
[8]金涛.往复式活塞压缩机故障监测与诊断技术[J].流体机械，1999，27(11)：28~31.
[9]沈庆根，郑水英.设备故障诊断[M].北京：化学工业出版社，2006.
[10]高慎琴.化工机器[M].北京：化学工业出版社，1992.
[11]刘卫华.近十年来压缩机技术研究动向[J].压缩机技术，1998，150（4）：33~35.
[12]林京，刘红星，屈梁生.功率谱谱距及其模糊测度在气阀故障诊断中的应用[J].化工机械，1997，24（3）:168~170.
[13]高晶波，王日新，徐敏强等.2D12型往复式压缩机气阀故障诊断的声谱诊断方法[J].化工机械，2003，30（4）:202~205.
[14]高晶波，徐敏强，张嘉钟.2D12型往复式压缩机气阀故障诊断的时频分析方法[J].化工机械，2003，30（3）:140~143.
[15]袁小宏，屈梁生.小波分析及其在压缩机气阀故障检测中的应用研究[J].振动工程学报，1999，12（3）：410~425.
[16]林京，屈梁生.基于连续小波变换的奇异性检测与故障诊断[J].振动工程学报，2000，
[17]刘红星，林京.往复压缩机气阀故障的振动诊断方法[J].压缩机技术，1996，135（1）：32~34.
[18]刘红星，左洪福，姜澄宇等.信号频谱的二维向量及其应用[J].中国机械工程，1999，10（5）：537~539.
[19]刘卫华，郁永章.往复压缩机故障示功图诊断法研究[J].压缩机技术，2001，170（6）：9~12.
[20]何道清.天然气压缩机工况监测及诊断技术[J].西南石油学报，2001，23（4）：64~67
[21]林高平，吴丹青，王毅.利用压缩机缸盖振动信号识别缸内压力[J].压缩机技术，1994，127（5）：35~38.
[22]王金东，张嘉钟，杨敬源.2D12往复压缩机气缸内压力检测技术[J].压缩机技术，2002，175（5）：21~23.
[23]王琳.机械设备故障诊断与监测的常用方法及其发展趋势[J].武汉工业大学学报，2000，22（3）：62~65.
[24]丛蕊,刘树林,杨丽晶.关联维数在往复压缩机气阀故障诊断中的应用[J].压缩机技术,2007,(1):18-20.
[25]王金东,刘岩,赵海洋,等.基于延时嵌入法的往复压缩机关联维数提取技术[J].化工机械,2005,32(4):214-217.
[26]王金东,李新伟,张忠臣,等.往复压缩机多重分形故障特征提取[J].压缩机技术,2008,(1):12-14.
[27]刘彬,王霄,谢平.基于小波多重分形的复杂机械故障诊断方法研究[J].传感技术学报,2006,19(1):232-234.
[28]王金东,赵海洋,李新伟.基于多重分形的往复压缩机分级诊断研究[J].压缩机技术,2009,(1):1-4.
[29]刘树林,杨丽晶,马锐,等.近似熵在往复压缩机气阀故障诊断中的应用[J].压缩机技术,2005,(6):20-22.
[30] R.M.S.S. Abeysekera and B.Boashash. Time frequency domain feature of ECG signals:their application in P wave detection using the cross Wigner-Ville distribution[J]. Proc.IEEE ICASSP-89,1989:1524~1527.
[31][美]S.M.Kay, S.L.Marple.现代谱分析技术综述[J].强度与环境.1987，2：60~119.
[32]黄建国.现代谱分析的发展及应用(一)[J].数据采集与处理.1990，5(4)：47~55.
[33]黄建国.现代谱分析的发展及应用(二)[J].数据采集与处理.1991，6(1)：33~45.
[34][美]L科恩.时频分析：理论与应用[M].西安.西安交通大学出版社，2000.
[35] Childer D.G. Modern Spectrum Analysis. IEEE Press.1978.
[36]袁保宗.信号处理技术的若干进展[J].电信科学.1993，9(4)：2~8
[37] J.R. Fonolosa and C.L. Nikias. Wigner higher order moment spectra: definition,properities, computation and application to transient signal analysis. IEEE Trans SignalProcessing,1993,41:245~266.
[38] C.L.Nikias, M.R.Raghuveer. Bispectrum estimation: a digital signal processingframework. Proc. IEEE,1987,75：869~891.
[39] G.B.Gianaakis. Cumulants：a powerful tool in signal processing. Proc.IEEE,1987,75:1333~1334.
[40]张贤达.现代信号处理[M].北京：清华大学出版社，1995.
[41]刘庆云，李志舜，刘朝晖.时频分析技术及研究现状[J].计算机工程，2004，30(1)：171~173.
[42]陈卫，吕贵洲，梁四洋.参数化时频分析综述[J].现代雷达，2008，30(2)：61~64
[43] John M. O, Mostefa M and Boualem B. A New Discrete Analytic Signal for ReducingAliasing in the Discrete Wigner-Ville Distribution[J]. IEEE Transactions on SignalProcessing,2008,56(11):5427~5434.
[44] Jagmal S, Matteo S and Mihai D. Parametric Versus Non-Parametric Complex ImageAnalysis [C]. IEEE International Conference Proceedings on Geoscience and RemoteSensing,2009:9~12.
[45] Norden E. H, Zheng S and Steven R. L. The Empirical Mode Decomposition and theHilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis [J]. Proc. R.Soc. Lond. A,1998,454:903~995.
[46]程军圣，杨宇，于德介.局部均值分解方法及其在齿轮故障诊断中的应用[J].振动工程学报，2009，22(1)：76~83.
[47]杨建文，贾民平.希尔伯特-黄谱的端点效应分析及处理方法研究[J].振动工程学报，2006，19(2)：283~288.
[48]王秋生，段丹辉，袁梅.基于AR模型包络延拓的经验模态分解[J].数据采集与处理，2008，23(2)：205~210.
[49]程军圣，于德介，杨宇.Hilbert-Huang变换端点效应问题的探讨[J].振动与冲击，2005，24(6)：40~42.
[50]刘慧婷，张旻，程家兴.基于多项式拟合算法的EMD端点问题的处理[J].计算机工程与应用，2004，40(16)：84~86.
[51]邓拥军，王伟，钱成春等.EMD方法及Hilbert变换中边界问题的处理[J].科学通报，2001，46(3)：114~118.
[52]胡爱军，安连锁，唐贵基.Hilbert-Huang变换端点效应处理新方法[J].机械工程学报，2008，44(4)：154~158.
[53]王婷，杨莘元，李冰冰.一种改善EMD端点效应的新方法[J].哈尔滨理工大学学报，2009，14(5)：23~26.
[54]程军圣，于德介，杨宇.基于支持矢量回归机的Hilbert-Huang变换端点效应问题的处理方法[J].机械工程学报，2006，42(4)：23~31.
[55]杨永锋，吴亚锋，任兴民等.基于最大Lyapunov指数预测的EMD端点延拓[J].物理学报，2009，58(6)：3762~3746.
[56] Qi Keyu, He Zhengjia, Zi Yanyang. Cosine window-based boundary processingmethod for EMD and its application in rubbing fault diagnosis[J]. Mechanical Systemsand Signal Processing,2007,21(7):2750~2760.
[57] Zhao Jinping, Huang Daji. Mirror extending and circular spline function for empiricalmode decomposition Method[J].浙江大学学报（英文版）,2001,2(3):247~252.
[58]余磊，刘泉，经验模态分解中端点效应的抑制[J].武汉理工大学学报，2010，32(10)：151~154.
[59]杜陈艳，张榆锋，杨平等.经验模态分解边缘效应抑制方法综述[J].仪器仪表学报，2009，30(1)：54~59.
[60] Q. Chen, N.E. Huang, S. Riemen schneider, etc. A B-spline approach for empiricalmode decomposition[J]. Advances in Computational Mathematics,2006,24(1-4):171~195.
[61] G. G. S Pegram, M. C Peel, T. A. McMahon. Empirical mode decomposition usingrational splines: an application to rainfall time series[J]. Proc. R. Soc. A,2008,464(2094):1483~1501.
[62] Hawley Stephen D., Atlas Les E., Chizeck Howard J.. Some properties of an empiricalmode type signal decomposition algorithm[J]. IEEE Signal Process Lett,2010,17(1):24~27.
[63] Gabriel Rilling, Patrick Flandrin, Paulo Goncalves. On empirical mode decompositionand its algorithms[C]. IEEE-EURASIP Workshop on Nonlinear Signal and ImageProcessing NSIP-03, Grado (I),2003.
[64] J. Cheng, D. Yu, Y. Yang. Research on the intrinsic mode function (IMF) criterion inEMD method[J]. Mechanical Systems and Signal Processing,2006,20(4):817~824.
[65]胡劲松，杨世锡.基于能量的振动信号经验模态分解终止条件[J].振动、测试与诊断，2009，29(1)：19~22.
[66] G. Rilling, P. Flandrin. One or two frequencies? The empirical mode decompositionanswers[J]. IEEE Trans. Signal Processing,2008,56(1):85~95.
[67] Z. K. Peng, Peter W. Tse, F. L. Chu. A comparison study of improved Hilbert-Huangtransform and wavelet transform: Application to fault diagnosis for rolling bearing[J].Mechnical Systems and Signal Processing,2005,19(5):974~988.
[68] Z. K. Peng, Peter W. Tseb, F. L. Chu. An improved Hilbert-Huang transform and itsapplication in vibration signal analysis[J]. Journal of Sound and Vibration,2005,286(1-2):187~205.
[69] J.C. Echeverria, J. A. Crowe, M.S. Woolfson, etc. Application of empirical modedecomposition to heart rate variability analysis[J]. Medical&Biological Engineering&Computing,2001(39):471~479.
[70] John I. S., Ying S.. Assessment of chaotic parameters in nonstationaryelectrocardiograms by use of empirical mode decompostion[J]. Annals of BiomedicalEngineering,2004,32(10):1348~1354．
[71] Andrade A. O., Kyberd P. J., Taffler S. D.. A novel spectral representation ofelectromyographic signals[C]. Proceedings of the25th Annual International Conferenceof the IEEE EMBS Cancun,2003,3:2598~2601．
[72] H. Liang. Empirical mode decomposition: a method for analyzing neural data[J].Neurocomputing,2005,65-66:801~807.
[73] M. Bennett. Empirical mode decomposition and tissue harmonic imaging[J].Ultrasound in Med.&Biol.,2005,301(8):1051~1061.
[74] Hualou Liang, Qiu-Hua Lin, J. D. Chen. Application of the empirical modedecomposition to the analysis of esophageal manometric data in gastroesophagealreflux disease[C]. Proceeding of the26th Annual International Conference of the IEEEEMBS, San Francisco, CA, USA, September1-5,2004.
[75] J. C. Nunes, Y. Bouaoune, E. Delechelle, et al. Image analysis by bidimensionalempirical mode decomposition[J]. Image and Vision Computing,2003,21:1019~1026.
[76]徐文良，孟明，马玉良.HHT方法在人体下肢表面肌电信号分析中的应用[J].传感技术学报，2010，23(3)：297~302.
[77]韩民，彭玉华，张顺利等.基于经验模态分解的虹膜识别[J].光学学报，2010，30(2)：364~368.
[78] Zhang Y., Gao Y., Wang L., etc. The removal of wall components in doppler ultrasoundsignals by using the empirical mode decomposition algorithm[J]. IEEE Trans Biomed.Eng.,2007,54(9):1631~1642.
[79] L. Hadjileontiadis. Emprical mode decomposition and fractal dimension filter[J]. IEEEEngineering in Medicine and Biology Magazine,2007,26(1):30~39.
[80] H. Liang．Empirical mode decomposition of field potentials from macaque V4in visualspatial attention[J]. Biological Cybernetics,2005,92:380~392.
[81] Martin Arundell. Hilbert transform of voltammetric data[J]. ElectrochemistryCommunication,2004,6:366~372.
[82]王卫强.经验模态法在中国沿岸海平面变化中的应用研究[J].海洋学报，1999，21(6)：102~109.
[83] Per Gloersen, Norden Huang. Comparison of interannual intrinsic modes inhemispheric sea ice covers and other geophysical parameters[C]. IEEE Transactions onGeoscience and Remote Sensing，MAY2003,41(5):1062~1074.
[84] Paul A. Hwang, Norden E. Huang, David W. Wang. A note on analyzing nonlinear andnonstationary ocean wave data[J]. Applied Ocean Research,2003,25:187~193.
[85] A. D. Veltcheva, C. Guedes Soares. Identification of the components of wave spectraby the Hilbert Huang transform method[J]. Applied Ocean Research,2004,26:1~12.
[86]王国龙，吴中鼎，袁延茂等.经验模态分解方法在潮余流分离中的应用[J].海洋测绘，2010，30(2)：42~45.
[87]公茂盛，谢礼立.HHT方法在地震工程中的应用之初步探讨[J].世界地震工程,2003，19(3)：39~43.
[88]汪权，王建国，张鸣祥.基于Hilbert-Huang变换理论的结构振动主动变刚度频率控制[J]，应用力学学报，2010，27(2)：301~315.
[89]傅光耀，段晨东，刘健新.经验模态分解剔除结构地震响应位移漂移[J].振动工程学报，2009，22(4)：438~442.
[90] K. T. Coughlin，K. K. Tung.11-Year solar cycle in the stratosphere extracted by theempirical mode decomositiion method[J]. Advances in Space Research,2004,34:323~329.
[91]沈国际，陶利民，陈仲生.减速器振动信号的经验模态分解[J].航空学报，2005，26(1)：62~65.
[92] Loh C. H., Wu T. C., Huang N. E.. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics andstructual response[J]. Bulletin of the Seismological Society of America,2001,(91):1339~1357.
[93] R. Balocchi. Deriving the respiratory sinus arrhy thmia from the heartbeat time seriesusing empirical mode decomposition[J]. Chaos, Solitons and Fractas，2004，20：171~177．
[94]王学敏，黄方林.EMD方法在消除桥梁振动信号局部强干扰中的应用[J].机械强度，2005，27(1)：33~37．
[95] S. Loutridis. Forced vibration behaviour and crack detection of cracked beams usinginstantaneous frequency[J]. NDT&E International,2005,38:411~419．
[96] G. Kerschen, A. F. Vakakis, Y. S. Lee, etc. Toward a fundamental understanding of theHilbert-Huang transform in nonlinear structural dynamics[J]. Journal of Vibration andControl,2008,14(1-2):77~105.
[97] B. Ning, S. Qiyu, Y. Zhihua, etc. Robust image watermarking based on multibandwavelets and empirical mode decomposition[J]. IEEE Transactions on ImageProcessing,2007,16(8):1956~1966.
[98] E. Delechelle. Empirical mode decomposition synthesis of fractional processes in1D-and2D-space[J]. Image and Vision Computing,2005,23(9):799~806.
[99] J. C. Nunes, S. Guyout, E. Delechelle. Texture analysis based on local analysis of thebidimensional empirical mode decomposition[J]. Machine Vision and Applications,2005,16(3):177~188.
[100] Shaohui Chen, Hongbo Su, Renhua Zhang, etc. Improving empirical modedecomposit-ion using support vector machines for multifocus image fusion[J]. Sensors,2008,8(4):2500~2508.
[101] M. E. Montesinos． Hilbert-Huang analysis of BWR neutron detector signals:application to DR calculation and to corrupted signal analysis[J]. Annals of NuclearEnergy,2003,30(6):715~727.
[102] Chengjie Cai, Weixian Liu, Fu J. S., etc. Empirical mode decomposition ofmicro-doppler signature[C]．Radar Conference, IEEE International，2005,895~899.
[103]赵犁丰，王振芬等.基于经验模式分解的希尔伯特变换包络提取在机械故障诊断中的应用[J].青岛海洋大学学报，2002,32(6)：965-970.
[104] B. Liu, S. Riemenschneider, Y. Xu. Gearbox fault diagnosis using empirical modedecomposition and Hilbert spectrum[J]. Mechanical Systems and Signal Processing,2006,20(3):718~734.
[105]陈忠，郑时雄.基于经验模式分解（EMD）的齿轮箱齿轮故障诊断技术研究[J].振动工程学报，2003，16(2)：229-232.
[106] S. J. Loutridis. Damage detection in gear systems using empirical mode dedomposit-ion[J]. Engineering Structures,2004,26(2):1833~1841.
[107]胡劲松，杨世锡.基于HHT的旋转机械故障诊断方法研究[J].动力工程，2004，24(6)：845~851.
[108] Z.K. Peng, Peter W. Tse, F.L. Chu. A comparison study of improved Hilbert-Huangtransform and wavelet transform: Application to fault diagnosis for rolling bearing[J].Mechanical Systems and Signal Processing,2005,19(5):974~988.
[109] Ruqiang Yan, Robert X. Gao. Rotary machine health diagnosis based on empiricalmode decomposition[J]. Journal of Vibration and Acoustics,2008,130(2):021007-1~12.
[110] V.K. Rai, A.R. Mohanty. Bearing fault diagnosis using FFT of intrinsic modefunctions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007,21(6):2607~2615.
[111]杨宇，于德介，程军圣.基于经验模态分解包络谱的滚动轴承故障诊断方法[J].中国机械工程，2004，15(16)：1469~1471.
[112] Dejie Yu, Junsheng Chen, Yu Yang. Application of EMD method and Hilbert spectrumto the fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing,2005,19(2):259~270.
[113] Liu Yibing, Wu Qi, Ma Zhiyong, etc. An improved Hilbert-Huang transform and itsapplication in fault signal analysis[C]. Proceeding of the2006IEEE InternationalConferecne on Mechatronics and Automation,2006,2426~2431.
[114] Q. Gao, C. Duan, H. Fan, etc. Rotating machine fault diagnosis using empirical modedecomposition[J]. Mechanical Systems and Signal Processing,2008,22(5):1072~1081.
[115] Jianmin Zeng, Zhengyi Jiang, Taosen Li, etc. Gear local fault diagnosis with empiricalmode decomposition and Hilbert-Huang transform[J]. Advanced Materials Research,2011,199-200:899~904.
[116]李琳，张永祥，明延涛.EMD降噪的关联维数在齿轮故障诊断中的应用研究[J].振动与冲击，2009，28(4):145~148.
[117]汤宝平，蒋永华，张祥春.基于形态奇异值分解和经验模态分解的滚动轴承故障特征提取方法[J].机械工程学报，2010，46(5):37~42.
[118] Patrick Flandrin, Gabriel Rilling. Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2004,11(2):112~114.
[119]孔国杰，张培林，徐龙堂等.基于经验模态分解的自适应滤波算法及其应用[J].信号处理，2009，25(6):958~962.
[120]侯王宾，刘天琪，李兴源.基于经验模态分解滤波的低频振荡Prony分析[J].物理学报，2010，59(5):3531~3537.
[121]曹寅文，宋慎义，肖井华.运动后人体心肺节律同步关系及信号的耦合方向[J].物理学报，2010，59(7):5163-5168.
[122]杨光亮，朱元清，于海英.基于HHT的地震信号自动去噪算法[J].大地测量与地球动力学，2010，30(3):39~42.
[123]王强,周云龙,崔玉峰等.EMD与神经网络在气液两相流流型识别中的应用[J].
[124]林瑞霖，周平，基于EMD和神经网络的气阀机构故障诊断研究[J].海军工程大学学报，2008，20(2):48~51.
[125]程凌,陈前.基于经验模态分解与神经网络的结构故障诊断[J].振动、测试与诊断,2010,189~192.
[126]沈志熙,黄席樾,马笑潇.基于EMD和支持向量机的柴油机故障诊断[J].振动、测试与诊断,2010,30(1):19~22.
[127] Chang Che, Dan Hu. An approach based on Hilbert-Huang transform and supportvector machine for intelligent fault diagnosis[J]. Lecture Notes in ElectricalEngineering,2010,67(8):811~817.
[128]崔玲丽，康晨晖，胥永刚等.滚动轴承早期冲击性故障特征提取的综合算法研究[J].仪器仪表学报，2010，31(11)：2422~2427.
[129]邵毅敏，周晓君，欧家福等.增强型滤波及冲击性机械故障特征的提取[J].机械工程学报，2009，45(4)：166~171.
[130]章立军，阳建宏，徐金梧等.形态非抽样小波及其在冲击信号特征提取中的应用[J].振动与冲击，2007，26(10)：56~59.
[131]胡晓依，何庆复，王华胜.基于滤波器组分解的周期性振动冲击信号解调方法及其应用[J].振动与冲击，2008，27(8)：133~137.
[132]徐珍华，张来斌，段礼祥.时频域分析在往复压缩机活塞磨损故障诊断中的应用[J].压缩机技术，2010，(3)：1~3.
[133] M. Elhaj, F. Gu, A.D. Ball, etc. Numerical simulation and experimental study of atwo-stage reciprocating compressor for condition monitoring[J]. Mechanical Systemsand Signal Processing,2008,22(2):374~389.
[134]王江萍，鲍泽富.往复式压缩机振动信号频谱分析与故障诊断[J].石油机械，2008，36(8)：63~66.
[135]别锋锋，郭正刚，张志新.基于局域波时频谱的系统级故障诊断方法研究[J].仪器仪表学报，2008，29(5)：1092~1095.
[136] Shulin Liu, Xianming Wang, Hui Wang, eta.. Extension of Traditional Frequency andResearch on Time-Frequency Distribution[J]. Applied Mechanics and Materials,2011,44-47:2089~2093.
[137]刘树林，赵海峰，王辉等.局部频率概念及适用性分析[J].振动与冲击(S)，2008：202~204.
[138] Liu Shulin, Wang Hui, Ma Rui, eta.. Time-Frequency Distribution Based on LocalFrequency and EMD[C]. Proceedings of ICALIP2008International Conference,2008:112~117.
[139]胡昌华,张军波,夏军等.基于MATLAB的系统分析与设计－小波分析[M].西安:西安电子科技大学出版社,1999.
[140] Schlurmann T.. Spectral frequency analysis of nonlinear water waves derived from theHilbert-Huang transformation[J]. Journal of Offshore Mechanics and ArcticEngineering (JOMAE), American Society of Mechanical Engineers (ASME),2002,124(1):22-27.
[141] L. Cohen. Time frequency analysis[M]. Englewood Cliffs, NJ: Prentice-Hall,1995.
[142] Loughlin P. J., Tacer B.. Comments on the interpretation of instantaneousfrequency[C]. IEEE Singal Processing Letters,1997,4(5):123-125.
[143] L. Mandel. Interpretation of instrantaneous frequencies[J]. Amer. J. Phys., Oct.1974,42:840-846.
[144] Feldmann, M.. Nonlinear free vibration identification via the Hilbert transform[J].Journal of Sound and Vibration,1997,208(3):475-489.
[145] Roberts Michael J． Signals and Systems：Analysis Using Transform Methods andMATLAB[M]．NewYork：McGraw-Hill，2004．
[146] B．Boashash．Estimating and interpreting the instantaneous frequency of a signal-Part1：fundamentals[J]． Proceedings of the IEEE，1992，80(4)：520-538．
[147]李俊.往复压缩机状态监测系统的研究[D].武汉：华中科技大学，2004.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |