基于HHT的往复压缩机故障诊断研究
|
摘要
往复压缩机广泛应用于石油化工工业等领域,保证其安全稳定运行是设备维护工作的重点,因此开展往复压缩机故障诊断的研究工作有着重要的现实意义与经济价值。虽然近年来机械设备故障诊断技术的研究在国内外得到了较大发展,但是由于往复压缩机具有结构复杂、激励源众多等特点,使测试信号具有较强的非平稳性,而现有的信号分析方法在处理非平稳信号时存在时频分辨率低等缺陷,使得往复压缩机故障诊断技术至今仍未达到实用程度。而Hilbert-Huang变换(HHT)方法的出现,为非平稳信号的分析提供了新方法,也为往复压缩机故障诊断提供了新手段,但其存在的缺陷仍然难以满足实际应用。为了使HHT方法能够有效地应用于往复压缩机故障诊断,本文以2D12型往复压缩机为研究对象,结合HHT方法的优缺点,针对往复压缩机的故障诊断技术开展了一系列研究工作。
首先对往复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,指出了往复压缩机测试信号的强非平稳性,提出了适于驱动机构及气阀振动的分析数据选取原则。
其次,通过HHT方法与小波(包)变换等传统信号处理方法对调幅、调频及调频调幅等仿真信号的大量对比分析,指出HHT方法更适于分析非平稳信号,可以用于往复压缩机的故障诊断。
接着,对HHT方法在仿真分析及往复压缩机实际测试信号分析中出现的端点效应、模态混叠、虚假模态及模态裂解缺陷,在分析了产生原因的基础上,提出了相应的解决方法,主要体现在：(1)根据实际测试数据点较多的特点,提出了在分析数据始末两端设置“污染隔离区”的方法；(2)根据往复压缩机测试信号中由于具有脉冲信息及频率成分接近导致模态混叠的现象,提出了分段三次Hermite插值多项式法替代传统的三次样条插值法构造极值包络线；(3)根据往复压缩机测试信号因高频噪声等因素的影响使固有模态函数出现模态裂解现象,提出了局部信号毛刺消除法；(4)根据经验模态分解算法产生的虚假模态现象及往复压缩机测试信号的非平稳性,提出了去除虚假模态的局部相关分析法。经仿真分析表明,以上四种方法均有效解决了HHT方法的缺陷,促进了其应用于往复压缩机的故障诊断。
最后,根据以上仿真分析成果,将HHT方法应用于往复压缩机驱动机构故障诊断,从时频分析角度有效诊断了连杆大头瓦间隙大、连杆小头瓦间隙大与十字头间隙大三种故障,验证了驱动机构故障诊断分析数据选取原则的正确性,降低了测点过多导致故障诊断的难度；根据最大熵谱分析适于短数据分析的优点及噪声对该方法的影响,结合HHT方法自适应去噪的优势,提出了基于HHT与最大熵谱分析的方法,较好地应用于往复压缩机气阀故障诊断中,结果表明随着气阀状态的不同,气阀的特征频率也具有明显的差别。
Reciprocating compressor is widely used in the area of petroleum and chemical industry and so on. The important thing about equipment maintenance is to guarantee it operate safely, therefore the development of research work on the fault diagnosis of reciprocating compressor will have an important actual significance and economic value. In recent years the research of mechanical equipment failure diagnosis technology has got a great development at home and abroad, but the technology of malfunction diagnosis research of reciprocating compressor is not still practical because of a reciprocating compressor having the feature of complication and multi-driving source, which makes the test signal more nonstationary, while the existing signal processing methods have lowly time-frequency resolution in processing nonstationary signal. While the emergence of Hilbert-Huang transformation (HHT) provides a new method for the analysis of non-stationary signal, also provides a new means for the malfunction diagnosis research of reciprocating compressor, but the existing defects are still unable to meet the actual application. In order to apply HHT to fault diagnosis of reciprocating compressor effectively, this paper will study 2D12 reciprocating compressor, together with the advantages and drawbacks of HHT, carrying out a series of research work aiming at the malfunction diagnosis technology of reciprocating compressor.
Firstly, the elementary working principle of reciprocating compressor, the kinematics and dynamics analysis of main moving parts are analyzed, the testing signals for reciprocating compressor have strong nonstationarity are given, and choosing principle for vibration data of driving mechanism and gas valve are proposed.
Secondly, through the comparisons for lots of simulated signals included the common amplitude modulation, frequency modulation signal of reciprocating compressor testing signal between HHT and wavelets analysis, this paper points out that HHT method is more suitable for processing non-steady signal and could be used in malfunction diagnosis of reciprocating compressor.
Then, according to the further analysis of the existing defect of HHT method and the features of reciprocating compressor actual signal, this paper points out the cause of end effect, modality aliasing, modality disintegrate and false modality, and brings the corresponding solve methods, which mainly represent that:(1) bringing the method of setting pollution isolation region on both ends of analysis data according to the feature of practical test data point being too many; (2) according to one phenomenon that reciprocating compressor testing data cause modality aliasing with the closeness of frequency component, this paper brings a method that we use a method of piecewise cubic Hermite interpolation polynomial to structure extreme value enveloping line to seek for local means; (3) according to one phenomenon that reciprocating compressor testing signal makes inherent modality function arise modality disintegrate because of the influence of high-frequency factor, this paper brings a way of local signal glitch elimination; (4) according to false modality phenomenon which is caused by experiential modality decomposition algorithm and non-stationary of the testing signal of reciprocating compressor, it puts in this paper false modality elimination way of local correlation analysis. The simulation analysis shows that the four methods above all solve efficiently the defects of HHT, which promotes its practical application in fault diagnosis reciprocating compressor.
Finally, from above research results, we apply HHT to the fault diagnosis for drive mechanism of reciprocating compressor, and diagnose efficiently three kinds of fault from the view of time-frequency analysis, and prove the accuracy of analysis data taking crosshead measured data as the malfunction diagnosis of drive mechanism, and reduce the difficulty of fault diagnosis because the measurement station being too many; according to the advantage of maximum entropy spectrum analysis being suitable for short data analysis and the influence of noise to the method, together with the advantage of self-adapting denoising for HHT, a method based HHT and maximum entropy spectrum analysis is pointed out, which acquires good application in fault diagnosis of gas valves in reciprocating compressor. The result shows that the feature frequency of gas valve varies clearly with valve's conditions.
首先对往复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,指出了往复压缩机测试信号的强非平稳性,提出了适于驱动机构及气阀振动的分析数据选取原则。
其次,通过HHT方法与小波(包)变换等传统信号处理方法对调幅、调频及调频调幅等仿真信号的大量对比分析,指出HHT方法更适于分析非平稳信号,可以用于往复压缩机的故障诊断。
接着,对HHT方法在仿真分析及往复压缩机实际测试信号分析中出现的端点效应、模态混叠、虚假模态及模态裂解缺陷,在分析了产生原因的基础上,提出了相应的解决方法,主要体现在：(1)根据实际测试数据点较多的特点,提出了在分析数据始末两端设置“污染隔离区”的方法；(2)根据往复压缩机测试信号中由于具有脉冲信息及频率成分接近导致模态混叠的现象,提出了分段三次Hermite插值多项式法替代传统的三次样条插值法构造极值包络线；(3)根据往复压缩机测试信号因高频噪声等因素的影响使固有模态函数出现模态裂解现象,提出了局部信号毛刺消除法；(4)根据经验模态分解算法产生的虚假模态现象及往复压缩机测试信号的非平稳性,提出了去除虚假模态的局部相关分析法。经仿真分析表明,以上四种方法均有效解决了HHT方法的缺陷,促进了其应用于往复压缩机的故障诊断。
最后,根据以上仿真分析成果,将HHT方法应用于往复压缩机驱动机构故障诊断,从时频分析角度有效诊断了连杆大头瓦间隙大、连杆小头瓦间隙大与十字头间隙大三种故障,验证了驱动机构故障诊断分析数据选取原则的正确性,降低了测点过多导致故障诊断的难度；根据最大熵谱分析适于短数据分析的优点及噪声对该方法的影响,结合HHT方法自适应去噪的优势,提出了基于HHT与最大熵谱分析的方法,较好地应用于往复压缩机气阀故障诊断中,结果表明随着气阀状态的不同,气阀的特征频率也具有明显的差别。
Reciprocating compressor is widely used in the area of petroleum and chemical industry and so on. The important thing about equipment maintenance is to guarantee it operate safely, therefore the development of research work on the fault diagnosis of reciprocating compressor will have an important actual significance and economic value. In recent years the research of mechanical equipment failure diagnosis technology has got a great development at home and abroad, but the technology of malfunction diagnosis research of reciprocating compressor is not still practical because of a reciprocating compressor having the feature of complication and multi-driving source, which makes the test signal more nonstationary, while the existing signal processing methods have lowly time-frequency resolution in processing nonstationary signal. While the emergence of Hilbert-Huang transformation (HHT) provides a new method for the analysis of non-stationary signal, also provides a new means for the malfunction diagnosis research of reciprocating compressor, but the existing defects are still unable to meet the actual application. In order to apply HHT to fault diagnosis of reciprocating compressor effectively, this paper will study 2D12 reciprocating compressor, together with the advantages and drawbacks of HHT, carrying out a series of research work aiming at the malfunction diagnosis technology of reciprocating compressor.
Firstly, the elementary working principle of reciprocating compressor, the kinematics and dynamics analysis of main moving parts are analyzed, the testing signals for reciprocating compressor have strong nonstationarity are given, and choosing principle for vibration data of driving mechanism and gas valve are proposed.
Secondly, through the comparisons for lots of simulated signals included the common amplitude modulation, frequency modulation signal of reciprocating compressor testing signal between HHT and wavelets analysis, this paper points out that HHT method is more suitable for processing non-steady signal and could be used in malfunction diagnosis of reciprocating compressor.
Then, according to the further analysis of the existing defect of HHT method and the features of reciprocating compressor actual signal, this paper points out the cause of end effect, modality aliasing, modality disintegrate and false modality, and brings the corresponding solve methods, which mainly represent that:(1) bringing the method of setting pollution isolation region on both ends of analysis data according to the feature of practical test data point being too many; (2) according to one phenomenon that reciprocating compressor testing data cause modality aliasing with the closeness of frequency component, this paper brings a method that we use a method of piecewise cubic Hermite interpolation polynomial to structure extreme value enveloping line to seek for local means; (3) according to one phenomenon that reciprocating compressor testing signal makes inherent modality function arise modality disintegrate because of the influence of high-frequency factor, this paper brings a way of local signal glitch elimination; (4) according to false modality phenomenon which is caused by experiential modality decomposition algorithm and non-stationary of the testing signal of reciprocating compressor, it puts in this paper false modality elimination way of local correlation analysis. The simulation analysis shows that the four methods above all solve efficiently the defects of HHT, which promotes its practical application in fault diagnosis reciprocating compressor.
Finally, from above research results, we apply HHT to the fault diagnosis for drive mechanism of reciprocating compressor, and diagnose efficiently three kinds of fault from the view of time-frequency analysis, and prove the accuracy of analysis data taking crosshead measured data as the malfunction diagnosis of drive mechanism, and reduce the difficulty of fault diagnosis because the measurement station being too many; according to the advantage of maximum entropy spectrum analysis being suitable for short data analysis and the influence of noise to the method, together with the advantage of self-adapting denoising for HHT, a method based HHT and maximum entropy spectrum analysis is pointed out, which acquires good application in fault diagnosis of gas valves in reciprocating compressor. The result shows that the feature frequency of gas valve varies clearly with valve's conditions.
引文
[1]冯世良.“十一五”规划为能源建设及石油和化工领域带来的机遇[J].中国石油和化工经济分析,2006,(12)：16-21.
[2]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.
[3]何正嘉,訾艳阳,孟庆丰.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.
[4]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,(5)：35-38.
[5]赵俊龙,张志新,郭正刚,等.往复式压缩机气缸压力模拟曲线提取[J].振动、测试与诊断,2009,29(1)：79-82.
[6]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,(5)：21-23.
[7]刘卫华,昂海松.测取往复压缩机气缸压力的新方法[J].中国机械工程,2002,13(16)：1368-1371.
[8]高晶波,王日新,徐敏强.基于示功图修正的往复压缩机气阀故障诊断方法[J].压缩机技术,2009,(3)：4-8.
[9]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,(6)：9-12.
[10]M. Elhaj, F. Gu, A.D. Ball, etc. Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring[J]. Mechanical Systems and Signal Processing,2008,22(2):374-389.
[11]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.
[12]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.
[13]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.
[14]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.
[15]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.
[16]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.
[17]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.
[18]高晶波,王日新,徐敏强.2D12型往复式压缩机气阀故障的声谱诊断方法[J].化工机械,2003,30(4)：202-205.
[19]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.
[20]刘红星,左洪福,姜澄宇,等.往复机械特征频段信号的解调分解及其应用[J].振动工程学报,2000,13(2)：283-289.
[21]林京,刘红星,屈梁生.信号包络特征识别在故障诊断中的应用[J].振动、测试与诊断,1998,18(1)：34-38.
[22]王朝晖,张来斌,郭存杰,等.包络解调法在气阀弹簧失效故障诊断中的应用[J].石油大学学报(自然科学版),2005,29(2)：86-88.
[23]姚利斌,曹斌,苑宇.基于AR模型的往复压缩机故障诊断[J].压缩机技术,2005,(6)：22-24.
[24]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)：410-415.
[25]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)：140-142.
[26]周燕峰,马孝江,苑宇.基于信号时频熵的往复式压缩机故障诊断[J].中国设备工程,2006,(9)：40-42.
[27]姚利斌,曹斌,张志新,等.基于小波包分析的往复压缩机故障诊断[J].中国设备工程,2006,(2)：48-50.
[28]程香平,丁雪兴,刘海亮,等.小波分析在往复压缩机故障诊断中的应用[J].压缩机技术,2007,(6)：19-21.
[29]董宁娟,赵洪金,高晶波.基于参数识别和小波包分析的故障特征提取[J].噪声与振动控制,2008,(5)：91-93.
[30]聂士忠,王玉泰.最大熵谱分析方法和MATLAB中对短记录资料的谱分析[J].山东师范大学学报,2005,20(3)：40-42.
[31]Huang N E. Computer implicated empirical mode decomposition method, apparatus and articale of manufacture [P]. U. S. Patent Pending,1996.
[32]Norden E. H., Zheng Shen, Steven R. Long, etc. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc. R. Soc. Lond. A,1998,454:903-995.
[33]Norden E. Huang, Zheng Shen, Steven R. Long. A new view of nonlinear water waves: the Hilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31:417-457.
[34]丛蕊,高学良,刘树林,等.EMD和关联维数在往复压缩机故障诊断中的应用[J].大庆石油学院学报,2008,32(2)：86-89.
[35]张国新,蔡改贫,朱家俊.基于EMD与自回归谱的往复压缩机气阀故障诊断[J].煤矿机械,2007,28(10)：192-193.
[36]苑宇,马孝江.局域波时频域多重分形在故障诊断中的应用[J].振动与冲击,2007,26(5)：60-63.
[37]杜海峰,王孙安,丁国锋.基于粗糙集与模糊神经网络的多级压缩机诊断[J].西安交通大学学报,2001,35(9)：940-944.
[38]王金东,张嘉钟,刘树林.应用神经网络识别往复式压缩机指示图[J].振动、测试与诊断,2003,23(3)：217-219.
[39]高晶波,唐立强,王日新.基于神经网络的往复压缩机气阀故障诊断方法[J].压缩机技术,2005,(1)：25-27.
[40]呼滨,张来斌,段礼祥,等.基于模糊聚类和人工神经网络的往复压缩机气阀剩余寿命预测研究[J].计算机科学,2009,36(4B)：320-322.
[41]Quanmin Ren, Xiaojiang Ma, Gang Miao. Application of support vector machines in reciprocating compressor valve fault diagnosis[J]. Lecture Notes in Computer Science, 2005, (3611):81-84.
[42]王金东,赵海洋,于庆江.基于支持向量机的往复压缩机气缸活塞系统故障诊断技术[J].流体机械,2006,34(7)：43-46.
[43]Houxi Cui, Laibin Zhang, Rongyu Kang, etc. Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method[J]. Journal of Loss Prevention in the Process Industries,2009,22(6):864-867.
[44]刘树林.基于免疫机理的往复压缩机气阀故障检测方法[J].机械工程学报,2004,40(7)：156-160.
[45]刘树林,张嘉钟,徐敏强,等.基于小波包与神经网络的往复压缩机故障诊断方法[J].石油矿场机械,2002,31(5)：1-3.
[46]孙盛宇.基于小波神经网络的往复压缩机故障诊断方法研究[J].压缩机技术,2009,(5)：28-30.
[47]杜海峰,王孙安.基于ART-人工免疫网络的多级压缩机故障诊断[J].机械工程学报,2002,38(4)：88-90.
[48]赵海洋,王金东,刘树林,等.基于神经网络和支持向量机的复合故障诊断技术[J].流体机械,2008,36(1)：39-42.
[49]Bo-Suk Yang, Won-Woo Hwang, Dong-Jo Kim, etc. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines[J]. Mechanical Systems and Signal Processing,2005,19(2):371-390.
[50]刘树林,张嘉钟,黄文虎,等.基于小波包与粗集的往复压缩机故障诊断方法[J].压缩机技术,2002,(2)：1-3.
[51]窦唯,刘树林,孙明,等.往复压缩机气阀故障诊断的信息融合方法研究[J].压缩机技术,2004,(1)：11-13.
[52]王朝晖,姚德群,陈朝晖,等.基于数据挖掘的往复压缩机气阀故障诊断方法研究[J].科学技术与工程,2006,6(2)：136-138.
[53]闫凯,李峰,段礼祥.基于信息融合的往复压缩机气缸性能状态模糊评定方法[J]. 压缩机技术,2010,(1)：10-12.
[54]Francis E. H. Tay, Lixiang Shen. Fault diagnosis based on rough set theory[J]. Engineering Applications of Artificial Intelligence,2003,16(1):39-43.
[55]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.
[56]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.
[57]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.
[58]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.
[59]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.
[60]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.
[61]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.
[62]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.
[63]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.
[64]Qi Keyu, He Zhengjia, Zi Yanyang. Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis[J]. Mechanical Systems and Signal Processing,2007,21(7):2750-2760.
[65]Zhao Jinping, Huang Daji. Mirror extending and circular spline function for empirical mode decomposition Method[J].浙江大学学报(英文版),2001,2(3)：247-252.
[66]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.
[67]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.
[68]Q. Chen, N.E. Huang, S. Riemen schneider, etc. A B-spline approach for empirical mode decomposition[J]. Advances in Computational Mathematics,2006,24(1-4):171-195.
[69]G. G. S Pegram, M. C Peel, T. A. McMahon. Empirical mode decomposition using rational splines:an application to rainfall time series[J]. Proc. R. Soc. A,2008, 464(2094):1483-1501.
[70]Hawley Stephen D., Atlas Les E., Chizeck Howard J.. Some properties of an empirical mode type signal decomposition algorithm[J]. IEEE Signal Process Lett,2010,17(1): 24-27.
[71]Gabriel Rilling, Patrick Flandrin, Paulo Goncalves. On empirical mode decomposition and its algorithms[C]. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado (I),2003.
[72]J. Cheng, D. Yu, Y. Yang. Research on the intrinsic mode function (IMF) criterion in EMD method[J]. Mechanical Systems and Signal Processing,2006,20(4):817-824.
[73]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.
[74]G. Rilling, P. Flandrin. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Trans. Signal Processing,2008,56(1):85-95.
[75]Z. K. Peng, Peter W. Tse, F. L. Chu. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J]. Mechnical Systems and Signal Processing,2005,19(5):974-988.
[76]Z. K. Peng, Peter W. Tseb, F. L. Chu. An improved Hilbert-Huang transform and its application in vibration signal analysis[J]. Journal of Sound and Vibration,2005, 286(1-2):187-205.
[77]J.C. Echeverria, J. A. Crowe, M.S. Woolfson, etc. Application of empirical mode decomposition to heart rate variability analysis[J]. Medical & Biological Engineering & Computing,2001 (39):471-479.
[78]John I. S., Ying S.. Assessment of chaotic parameters in nonstationary electrocardiograms by use of empirical mode decompostion[J]. Annals of Biomedical Engineering,2004, 32(10):1348-1354.
[79]Andrade A. O., Kyberd P. J., Taffler S. D.. A novel spectral representation of electromyographic signals[C]. Proceedings of the 25th Annual International Conference of the IEEE EMBS Cancun,2003,3:2598-2601.
[80]H. Liang. Empirical mode decomposition:a method for analyzing neural data[J]. Neurocomputing,2005,65-66:801-807.
[81]M. Bennett. Empirical mode decomposition and tissue harmonic imaging[J]. Ultrasound in Med.& Biol.,2005,301(8):1051-1061.
[82]Hualou Liang, Qiu-Hua Lin, J. D. Chen. Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease[C]. Proceeding of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA, September 1-5,2004.
[83]J. C. Nunes, Y. Bouaoune, E. Delechelle, et al. Image analysis by bidimensional empirical mode decomposition[J]. Image and Vision Computing,2003,21:1019-1026.
[84]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.
[85]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.
[86]Zhang Y., Gao Y., Wang L., etc. The removal of wall components in doppler ultrasound signals by using the empirical mode decomposition algorithm[J]. IEEE Trans Biomed. Eng.,2007,54(9):1631-1642.
[87]L. Hadjileontiadis. Emprical mode decomposition and fractal dimension filter[J]. IEEE Engineering in Medicine and Biology Magazine,2007,26(l):30-39.
[88]H. Liang. Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention[J]. Biological Cybernetics,2005,92:380-392.
[89]Martin Arundell. Hilbert transform of voltammetric data[J]. Electrochemistry Communication,2004,6:366-372.
[90]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.
[91]Per Gloersen, Norden Huang. Comparison of interannual intrinsic modes in hemispheric sea ice covers and other geophysical parameters[C]. IEEE Transactions on Geoscience and Remote Sensing, MAY 2003,41(5):1062-1074.
[92]Paul A. Hwang, Norden E. Huang, David W. Wang. A note on analyzing nonlinear and nonstationary ocean wave data[J]. Applied Ocean Research,2003,25:187-193.
[93]A. D. Veltcheva, C. Guedes Soares. Identification of the components of wave spectra by the Hilbert Huang transform method[J]. Applied Ocean Research,2004,26:1-12.
[94]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.
[95]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.
[96]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.
[97]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.
[98]K. T. Coughlin, K. K. Tung.11-Year solar cycle in the stratosphere extracted by the empirical mode decomositiion method[J]. Advances in Space Research,2004,34: 323-329.
[99]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.
[100]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 and structual response[J]. Bulletin of the Seismological Society of America,2001, (91):1339-1357.
[101]R. Balocchi. Deriving the respiratory sinus arrhy thmia from the heartbeat time series using empirical mode decomposition[J]. Chaos, Solitons and Fractas,2004,20:171-177.
[102]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.
[103]S. Loutridis. Forced vibration behaviour and crack detection of cracked beams using instantaneous frequency[J]. NDT&E International,2005,38:411-419.
[104]G. Kerschen, A. F. Vakakis, Y. S. Lee, etc. Toward a fundamental understanding of the Hilbert-Huang transform in nonlinear structural dynamics[J]. Journal of Vibration and Control,2008,14(1-2):77-105.
[105]B. Ning, S. Qiyu, Y. Zhihua, etc. Robust image watermarking based on multiband wavelets and empirical mode decomposition[J]. IEEE Transactions on Image Processing, 2007,16(8):1956-1966.
[106]E. Delechelle. Empirical mode decomposition synthesis of fractional processes in 1D- and 2D-space[J]. Image and Vision Computing,2005,23(9):799-806.
[107]J. C. Nunes, S. Guyout, E. Delechelle. Texture analysis based on local analysis of the bidimensional empirical mode decomposition[J]. Machine Vision and Applications,2005, 16(3):177-188.
[108]Shaohui Chen, Hongbo Su, Renhua Zhang, etc. Improving empirical mode decomposit- ion using support vector machines for multifocus image fusion[J]. Sensors, 2008,8(4):2500-2508.
[109]M. E. Montesinos. Hilbert-Huang analysis of BWR neutron detector signals: application to DR calculation and to corrupted signal analysis[J]. Annals of Nuclear Energy,2003,30(6):715-727.
[110]Chengjie Cai, Weixian Liu, Fu J. S., etc. Empirical mode decomposition of micro-doppler signature[C]. Radar Conference, IEEE International,2005,895-899.
[111]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.
[112]B. Liu, S. Riemenschneider, Y. Xu. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum[J]. Mechanical Systems and Signal Processing, 2006,20(3):718-734.
[113]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.
[114]S. J. Loutridis. Damage detection in gear systems using empirical mode dedomposit-ion[J]. Engineering Structures,2004,26(2):1833-1841.
[115]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.
[116]Z.K. Peng, Peter W. Tse, F.L. Chu. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J]. Mechanical Systems and Signal Processing,2005,19(5):974-988.
[117]Ruqiang Yan, Robert X. Gao. Rotary machine health diagnosis based on empirical mode decomposition[J]. Journal of Vibration and Acoustics,2008,130(2):021007-1-12.
[118]V.K. Rai, A.R. Mohanty. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing, 2007,21(6):2607-2615.
[119]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.
[120]Dejie Yu, Junsheng Chen, Yu Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing,2005,19(2):259-270.
[121]Liu Yibing, Wu Qi, Ma Zhiyong, etc. An improved Hilbert-Huang transform and its application in fault signal analysis[C]. Proceeding of the 2006 IEEE International Conferecne on Mechatronics and Automation,2006,2426-2431.
[122]Q. Gao, C. Duan, H. Fan, etc. Rotating machine fault diagnosis using empirical mode decomposition[J]. Mechanical Systems and Signal Processing,2008,22(5):1072-1081.
[123]Jianmin Zeng, Zhengyi Jiang, Taosen Li, etc. Gear local fault diagnosis with empirical mode decomposition and Hilbert-Huang transform[J]. Advanced Materials Research,2011,199-200:899-904.
[124]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.
[125]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.
[126]Patrick Flandrin, Gabriel Rilling. Empirical mode decomposition as a filter bank[J]. IEEE Signal Processing Letters,2004,11(2):112-114.
[127]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.
[128]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.
[129]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.
[130]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.
[131]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].工程热物理学报,2007,28(3)：442-444.
[132]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.
[133]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.
[134]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.
[135]Chang Che, Dan Hu. An approach based on Hilbert-Huang transform and support vector machine for intelligent fault diagnosis[J]. Lecture Notes in Electrical Engineering, 2010,67(8):811-817.
[136]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.
[137]李云,姜培正.过程流体机械[M].化学工业出版社,北京,2008.
[138]刘卫华,郁永章.往复压缩机故障分析及智能故障诊断系统[J].压缩机技术,2004,(4)：27-30.
[139]科恩.时频分析：理论与应用[M].西安：西安交通大学出版社,1998.
[140]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.
[141]Schlurmann T.. Spectral frequency analysis of nonlinear water waves derived from the Hilbert-Huang transformation[J]. Journal of Offshore Mechanics and Arctic Engineering (JOMAE), American Society of Mechanical Engineers (ASME),2002,124 (1):22-27.
[142]L. Cohen. Time frequency analysis[M]. Englewood Cliffs, NJ:Prentice-Hall,1995.
[143]Loughlin P. J., Tacer B.. Comments on the interpretation of instantaneous frequency[C]. IEEE Singal Processing Letters,1997,4(5):123-125.
[144]L. Mandel. Interpretation of instrantaneous frequencies[J]. Amer. J. Phys., Oct.1974, 42:840-846.
[145]Feldmann, M.. Nonlinear free vibration identification via the Hilbert transform[J]. Journal of Sound and Vibration,1997,208(3):475-489.
[146]C. Moler. Numerical Computing with Matlab[M]. Society for Industrial and Applied Mathematics,2004.
[147]Liu Shulin, Zhao Haifeng, Wang Hui, et al. Application of improved EMD algorithm for the fault diagnosis of reciprocating pump valves with spring failure[C].9th International Symposium on Signal Processing and Its Application,2007,1-4.
[148]Deering R, Kaiser J F. The use of masking signal to improve empirical mode decomposition [C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'05), Philadelphia, Pennsylvania, USA,2005,4:485-488.
[149]李宏坤,周帅,黄文宗.基于时频图像特征提取的状态识别方法研究与应用[J].振动与冲击,2010,29(7)：184-188.
[150]李宏坤,周帅,孙志辉.Hilbert谱特征提取与支持向量机的状态识别方法研究[J].振动与冲击,2009,28(6)：131-134.
[151]柏林,刘小峰,秦树人.小波-形态-EMD综合分析法及其应用[J].振动与冲击,2008,27(5)：1-4.
[152]林瑞霖,孙云岭,孟祥东.基于小波降噪的经验模式分解方法研究[J].船海工程,2010,39(2)：30-32.
[153]曲巍葳,高峰.基于噪声方差估计的小波阈值降噪研究[J].机械工程学报,2010, 46(2)：28-32.
[154]Leon C., Lorenzo G., Robert H., et al. Time frequency analysis of a variable stiffness model for fault development. Digital Signal Processing,2002,12:429-440.
[155]Neild S. A., Mcfadden P. D., Williams M. S. A review of time frequency methods for structural vibration analysis[J]. Engineering Structures,2003,25:713-728.
[156]陈光雄,周仲荣.摩擦振动的时频特性[J].机械工程学报,2006,42(2)：1-5.
[157]刘卫华,郁永章,昂海松.气阀故障诊断的实验研究[J].压缩机技术,2001,(2)：3-5.
[158]张贤达.现代信号处理[M].2版.北京：清华大学出版社,2002：102-115.
[159]Wear K. A., Wagner R. F., Garra B. S.. A comparison of autoregressive spectral estimation algorithms and order determination methods in ultrasonic tissue characterization[J]. IEEE Trans on Ultrasonic,1995,42(4):709-716.
[160]吴道虎.基于最大熵谱估计的工程非平稳信号的频谱分析研究与实践[J].继电器,2006,34(13)：38-41.
[161]卢亚玲.最大熵谱估计在昆虫鸣声特征提取中的应用[J].计算机与数字工程,2007,35(11)：117-121.
[162]聂士忠,王玉泰.最大熵谱分析方法和MATLAB中对短记录资料的谱分析[J].山东师范大学学报,2005,20(3)：40-42.
[2]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.
[3]何正嘉,訾艳阳,孟庆丰.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.
[4]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,(5)：35-38.
[5]赵俊龙,张志新,郭正刚,等.往复式压缩机气缸压力模拟曲线提取[J].振动、测试与诊断,2009,29(1)：79-82.
[6]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,(5)：21-23.
[7]刘卫华,昂海松.测取往复压缩机气缸压力的新方法[J].中国机械工程,2002,13(16)：1368-1371.
[8]高晶波,王日新,徐敏强.基于示功图修正的往复压缩机气阀故障诊断方法[J].压缩机技术,2009,(3)：4-8.
[9]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,(6)：9-12.
[10]M. Elhaj, F. Gu, A.D. Ball, etc. Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring[J]. Mechanical Systems and Signal Processing,2008,22(2):374-389.
[11]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.
[12]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.
[13]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.
[14]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.
[15]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.
[16]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.
[17]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.
[18]高晶波,王日新,徐敏强.2D12型往复式压缩机气阀故障的声谱诊断方法[J].化工机械,2003,30(4)：202-205.
[19]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.
[20]刘红星,左洪福,姜澄宇,等.往复机械特征频段信号的解调分解及其应用[J].振动工程学报,2000,13(2)：283-289.
[21]林京,刘红星,屈梁生.信号包络特征识别在故障诊断中的应用[J].振动、测试与诊断,1998,18(1)：34-38.
[22]王朝晖,张来斌,郭存杰,等.包络解调法在气阀弹簧失效故障诊断中的应用[J].石油大学学报(自然科学版),2005,29(2)：86-88.
[23]姚利斌,曹斌,苑宇.基于AR模型的往复压缩机故障诊断[J].压缩机技术,2005,(6)：22-24.
[24]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)：410-415.
[25]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)：140-142.
[26]周燕峰,马孝江,苑宇.基于信号时频熵的往复式压缩机故障诊断[J].中国设备工程,2006,(9)：40-42.
[27]姚利斌,曹斌,张志新,等.基于小波包分析的往复压缩机故障诊断[J].中国设备工程,2006,(2)：48-50.
[28]程香平,丁雪兴,刘海亮,等.小波分析在往复压缩机故障诊断中的应用[J].压缩机技术,2007,(6)：19-21.
[29]董宁娟,赵洪金,高晶波.基于参数识别和小波包分析的故障特征提取[J].噪声与振动控制,2008,(5)：91-93.
[30]聂士忠,王玉泰.最大熵谱分析方法和MATLAB中对短记录资料的谱分析[J].山东师范大学学报,2005,20(3)：40-42.
[31]Huang N E. Computer implicated empirical mode decomposition method, apparatus and articale of manufacture [P]. U. S. Patent Pending,1996.
[32]Norden E. H., Zheng Shen, Steven R. Long, etc. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc. R. Soc. Lond. A,1998,454:903-995.
[33]Norden E. Huang, Zheng Shen, Steven R. Long. A new view of nonlinear water waves: the Hilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31:417-457.
[34]丛蕊,高学良,刘树林,等.EMD和关联维数在往复压缩机故障诊断中的应用[J].大庆石油学院学报,2008,32(2)：86-89.
[35]张国新,蔡改贫,朱家俊.基于EMD与自回归谱的往复压缩机气阀故障诊断[J].煤矿机械,2007,28(10)：192-193.
[36]苑宇,马孝江.局域波时频域多重分形在故障诊断中的应用[J].振动与冲击,2007,26(5)：60-63.
[37]杜海峰,王孙安,丁国锋.基于粗糙集与模糊神经网络的多级压缩机诊断[J].西安交通大学学报,2001,35(9)：940-944.
[38]王金东,张嘉钟,刘树林.应用神经网络识别往复式压缩机指示图[J].振动、测试与诊断,2003,23(3)：217-219.
[39]高晶波,唐立强,王日新.基于神经网络的往复压缩机气阀故障诊断方法[J].压缩机技术,2005,(1)：25-27.
[40]呼滨,张来斌,段礼祥,等.基于模糊聚类和人工神经网络的往复压缩机气阀剩余寿命预测研究[J].计算机科学,2009,36(4B)：320-322.
[41]Quanmin Ren, Xiaojiang Ma, Gang Miao. Application of support vector machines in reciprocating compressor valve fault diagnosis[J]. Lecture Notes in Computer Science, 2005, (3611):81-84.
[42]王金东,赵海洋,于庆江.基于支持向量机的往复压缩机气缸活塞系统故障诊断技术[J].流体机械,2006,34(7)：43-46.
[43]Houxi Cui, Laibin Zhang, Rongyu Kang, etc. Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method[J]. Journal of Loss Prevention in the Process Industries,2009,22(6):864-867.
[44]刘树林.基于免疫机理的往复压缩机气阀故障检测方法[J].机械工程学报,2004,40(7)：156-160.
[45]刘树林,张嘉钟,徐敏强,等.基于小波包与神经网络的往复压缩机故障诊断方法[J].石油矿场机械,2002,31(5)：1-3.
[46]孙盛宇.基于小波神经网络的往复压缩机故障诊断方法研究[J].压缩机技术,2009,(5)：28-30.
[47]杜海峰,王孙安.基于ART-人工免疫网络的多级压缩机故障诊断[J].机械工程学报,2002,38(4)：88-90.
[48]赵海洋,王金东,刘树林,等.基于神经网络和支持向量机的复合故障诊断技术[J].流体机械,2008,36(1)：39-42.
[49]Bo-Suk Yang, Won-Woo Hwang, Dong-Jo Kim, etc. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines[J]. Mechanical Systems and Signal Processing,2005,19(2):371-390.
[50]刘树林,张嘉钟,黄文虎,等.基于小波包与粗集的往复压缩机故障诊断方法[J].压缩机技术,2002,(2)：1-3.
[51]窦唯,刘树林,孙明,等.往复压缩机气阀故障诊断的信息融合方法研究[J].压缩机技术,2004,(1)：11-13.
[52]王朝晖,姚德群,陈朝晖,等.基于数据挖掘的往复压缩机气阀故障诊断方法研究[J].科学技术与工程,2006,6(2)：136-138.
[53]闫凯,李峰,段礼祥.基于信息融合的往复压缩机气缸性能状态模糊评定方法[J]. 压缩机技术,2010,(1)：10-12.
[54]Francis E. H. Tay, Lixiang Shen. Fault diagnosis based on rough set theory[J]. Engineering Applications of Artificial Intelligence,2003,16(1):39-43.
[55]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.
[56]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.
[57]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.
[58]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.
[59]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.
[60]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.
[61]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.
[62]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.
[63]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.
[64]Qi Keyu, He Zhengjia, Zi Yanyang. Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis[J]. Mechanical Systems and Signal Processing,2007,21(7):2750-2760.
[65]Zhao Jinping, Huang Daji. Mirror extending and circular spline function for empirical mode decomposition Method[J].浙江大学学报(英文版),2001,2(3)：247-252.
[66]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.
[67]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.
[68]Q. Chen, N.E. Huang, S. Riemen schneider, etc. A B-spline approach for empirical mode decomposition[J]. Advances in Computational Mathematics,2006,24(1-4):171-195.
[69]G. G. S Pegram, M. C Peel, T. A. McMahon. Empirical mode decomposition using rational splines:an application to rainfall time series[J]. Proc. R. Soc. A,2008, 464(2094):1483-1501.
[70]Hawley Stephen D., Atlas Les E., Chizeck Howard J.. Some properties of an empirical mode type signal decomposition algorithm[J]. IEEE Signal Process Lett,2010,17(1): 24-27.
[71]Gabriel Rilling, Patrick Flandrin, Paulo Goncalves. On empirical mode decomposition and its algorithms[C]. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado (I),2003.
[72]J. Cheng, D. Yu, Y. Yang. Research on the intrinsic mode function (IMF) criterion in EMD method[J]. Mechanical Systems and Signal Processing,2006,20(4):817-824.
[73]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.
[74]G. Rilling, P. Flandrin. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Trans. Signal Processing,2008,56(1):85-95.
[75]Z. K. Peng, Peter W. Tse, F. L. Chu. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J]. Mechnical Systems and Signal Processing,2005,19(5):974-988.
[76]Z. K. Peng, Peter W. Tseb, F. L. Chu. An improved Hilbert-Huang transform and its application in vibration signal analysis[J]. Journal of Sound and Vibration,2005, 286(1-2):187-205.
[77]J.C. Echeverria, J. A. Crowe, M.S. Woolfson, etc. Application of empirical mode decomposition to heart rate variability analysis[J]. Medical & Biological Engineering & Computing,2001 (39):471-479.
[78]John I. S., Ying S.. Assessment of chaotic parameters in nonstationary electrocardiograms by use of empirical mode decompostion[J]. Annals of Biomedical Engineering,2004, 32(10):1348-1354.
[79]Andrade A. O., Kyberd P. J., Taffler S. D.. A novel spectral representation of electromyographic signals[C]. Proceedings of the 25th Annual International Conference of the IEEE EMBS Cancun,2003,3:2598-2601.
[80]H. Liang. Empirical mode decomposition:a method for analyzing neural data[J]. Neurocomputing,2005,65-66:801-807.
[81]M. Bennett. Empirical mode decomposition and tissue harmonic imaging[J]. Ultrasound in Med.& Biol.,2005,301(8):1051-1061.
[82]Hualou Liang, Qiu-Hua Lin, J. D. Chen. Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease[C]. Proceeding of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA, September 1-5,2004.
[83]J. C. Nunes, Y. Bouaoune, E. Delechelle, et al. Image analysis by bidimensional empirical mode decomposition[J]. Image and Vision Computing,2003,21:1019-1026.
[84]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.
[85]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.
[86]Zhang Y., Gao Y., Wang L., etc. The removal of wall components in doppler ultrasound signals by using the empirical mode decomposition algorithm[J]. IEEE Trans Biomed. Eng.,2007,54(9):1631-1642.
[87]L. Hadjileontiadis. Emprical mode decomposition and fractal dimension filter[J]. IEEE Engineering in Medicine and Biology Magazine,2007,26(l):30-39.
[88]H. Liang. Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention[J]. Biological Cybernetics,2005,92:380-392.
[89]Martin Arundell. Hilbert transform of voltammetric data[J]. Electrochemistry Communication,2004,6:366-372.
[90]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.
[91]Per Gloersen, Norden Huang. Comparison of interannual intrinsic modes in hemispheric sea ice covers and other geophysical parameters[C]. IEEE Transactions on Geoscience and Remote Sensing, MAY 2003,41(5):1062-1074.
[92]Paul A. Hwang, Norden E. Huang, David W. Wang. A note on analyzing nonlinear and nonstationary ocean wave data[J]. Applied Ocean Research,2003,25:187-193.
[93]A. D. Veltcheva, C. Guedes Soares. Identification of the components of wave spectra by the Hilbert Huang transform method[J]. Applied Ocean Research,2004,26:1-12.
[94]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.
[95]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.
[96]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.
[97]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.
[98]K. T. Coughlin, K. K. Tung.11-Year solar cycle in the stratosphere extracted by the empirical mode decomositiion method[J]. Advances in Space Research,2004,34: 323-329.
[99]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.
[100]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 and structual response[J]. Bulletin of the Seismological Society of America,2001, (91):1339-1357.
[101]R. Balocchi. Deriving the respiratory sinus arrhy thmia from the heartbeat time series using empirical mode decomposition[J]. Chaos, Solitons and Fractas,2004,20:171-177.
[102]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.
[103]S. Loutridis. Forced vibration behaviour and crack detection of cracked beams using instantaneous frequency[J]. NDT&E International,2005,38:411-419.
[104]G. Kerschen, A. F. Vakakis, Y. S. Lee, etc. Toward a fundamental understanding of the Hilbert-Huang transform in nonlinear structural dynamics[J]. Journal of Vibration and Control,2008,14(1-2):77-105.
[105]B. Ning, S. Qiyu, Y. Zhihua, etc. Robust image watermarking based on multiband wavelets and empirical mode decomposition[J]. IEEE Transactions on Image Processing, 2007,16(8):1956-1966.
[106]E. Delechelle. Empirical mode decomposition synthesis of fractional processes in 1D- and 2D-space[J]. Image and Vision Computing,2005,23(9):799-806.
[107]J. C. Nunes, S. Guyout, E. Delechelle. Texture analysis based on local analysis of the bidimensional empirical mode decomposition[J]. Machine Vision and Applications,2005, 16(3):177-188.
[108]Shaohui Chen, Hongbo Su, Renhua Zhang, etc. Improving empirical mode decomposit- ion using support vector machines for multifocus image fusion[J]. Sensors, 2008,8(4):2500-2508.
[109]M. E. Montesinos. Hilbert-Huang analysis of BWR neutron detector signals: application to DR calculation and to corrupted signal analysis[J]. Annals of Nuclear Energy,2003,30(6):715-727.
[110]Chengjie Cai, Weixian Liu, Fu J. S., etc. Empirical mode decomposition of micro-doppler signature[C]. Radar Conference, IEEE International,2005,895-899.
[111]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.
[112]B. Liu, S. Riemenschneider, Y. Xu. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum[J]. Mechanical Systems and Signal Processing, 2006,20(3):718-734.
[113]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.
[114]S. J. Loutridis. Damage detection in gear systems using empirical mode dedomposit-ion[J]. Engineering Structures,2004,26(2):1833-1841.
[115]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.
[116]Z.K. Peng, Peter W. Tse, F.L. Chu. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J]. Mechanical Systems and Signal Processing,2005,19(5):974-988.
[117]Ruqiang Yan, Robert X. Gao. Rotary machine health diagnosis based on empirical mode decomposition[J]. Journal of Vibration and Acoustics,2008,130(2):021007-1-12.
[118]V.K. Rai, A.R. Mohanty. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing, 2007,21(6):2607-2615.
[119]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.
[120]Dejie Yu, Junsheng Chen, Yu Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing,2005,19(2):259-270.
[121]Liu Yibing, Wu Qi, Ma Zhiyong, etc. An improved Hilbert-Huang transform and its application in fault signal analysis[C]. Proceeding of the 2006 IEEE International Conferecne on Mechatronics and Automation,2006,2426-2431.
[122]Q. Gao, C. Duan, H. Fan, etc. Rotating machine fault diagnosis using empirical mode decomposition[J]. Mechanical Systems and Signal Processing,2008,22(5):1072-1081.
[123]Jianmin Zeng, Zhengyi Jiang, Taosen Li, etc. Gear local fault diagnosis with empirical mode decomposition and Hilbert-Huang transform[J]. Advanced Materials Research,2011,199-200:899-904.
[124]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.
[125]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.
[126]Patrick Flandrin, Gabriel Rilling. Empirical mode decomposition as a filter bank[J]. IEEE Signal Processing Letters,2004,11(2):112-114.
[127]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.
[128]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.
[129]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.
[130]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.
[131]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].工程热物理学报,2007,28(3)：442-444.
[132]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.
[133]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.
[134]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.
[135]Chang Che, Dan Hu. An approach based on Hilbert-Huang transform and support vector machine for intelligent fault diagnosis[J]. Lecture Notes in Electrical Engineering, 2010,67(8):811-817.
[136]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.
[137]李云,姜培正.过程流体机械[M].化学工业出版社,北京,2008.
[138]刘卫华,郁永章.往复压缩机故障分析及智能故障诊断系统[J].压缩机技术,2004,(4)：27-30.
[139]科恩.时频分析：理论与应用[M].西安：西安交通大学出版社,1998.
[140]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.
[141]Schlurmann T.. Spectral frequency analysis of nonlinear water waves derived from the Hilbert-Huang transformation[J]. Journal of Offshore Mechanics and Arctic Engineering (JOMAE), American Society of Mechanical Engineers (ASME),2002,124 (1):22-27.
[142]L. Cohen. Time frequency analysis[M]. Englewood Cliffs, NJ:Prentice-Hall,1995.
[143]Loughlin P. J., Tacer B.. Comments on the interpretation of instantaneous frequency[C]. IEEE Singal Processing Letters,1997,4(5):123-125.
[144]L. Mandel. Interpretation of instrantaneous frequencies[J]. Amer. J. Phys., Oct.1974, 42:840-846.
[145]Feldmann, M.. Nonlinear free vibration identification via the Hilbert transform[J]. Journal of Sound and Vibration,1997,208(3):475-489.
[146]C. Moler. Numerical Computing with Matlab[M]. Society for Industrial and Applied Mathematics,2004.
[147]Liu Shulin, Zhao Haifeng, Wang Hui, et al. Application of improved EMD algorithm for the fault diagnosis of reciprocating pump valves with spring failure[C].9th International Symposium on Signal Processing and Its Application,2007,1-4.
[148]Deering R, Kaiser J F. The use of masking signal to improve empirical mode decomposition [C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'05), Philadelphia, Pennsylvania, USA,2005,4:485-488.
[149]李宏坤,周帅,黄文宗.基于时频图像特征提取的状态识别方法研究与应用[J].振动与冲击,2010,29(7)：184-188.
[150]李宏坤,周帅,孙志辉.Hilbert谱特征提取与支持向量机的状态识别方法研究[J].振动与冲击,2009,28(6)：131-134.
[151]柏林,刘小峰,秦树人.小波-形态-EMD综合分析法及其应用[J].振动与冲击,2008,27(5)：1-4.
[152]林瑞霖,孙云岭,孟祥东.基于小波降噪的经验模式分解方法研究[J].船海工程,2010,39(2)：30-32.
[153]曲巍葳,高峰.基于噪声方差估计的小波阈值降噪研究[J].机械工程学报,2010, 46(2)：28-32.
[154]Leon C., Lorenzo G., Robert H., et al. Time frequency analysis of a variable stiffness model for fault development. Digital Signal Processing,2002,12:429-440.
[155]Neild S. A., Mcfadden P. D., Williams M. S. A review of time frequency methods for structural vibration analysis[J]. Engineering Structures,2003,25:713-728.
[156]陈光雄,周仲荣.摩擦振动的时频特性[J].机械工程学报,2006,42(2)：1-5.
[157]刘卫华,郁永章,昂海松.气阀故障诊断的实验研究[J].压缩机技术,2001,(2)：3-5.
[158]张贤达.现代信号处理[M].2版.北京：清华大学出版社,2002：102-115.
[159]Wear K. A., Wagner R. F., Garra B. S.. A comparison of autoregressive spectral estimation algorithms and order determination methods in ultrasonic tissue characterization[J]. IEEE Trans on Ultrasonic,1995,42(4):709-716.
[160]吴道虎.基于最大熵谱估计的工程非平稳信号的频谱分析研究与实践[J].继电器,2006,34(13)：38-41.
[161]卢亚玲.最大熵谱估计在昆虫鸣声特征提取中的应用[J].计算机与数字工程,2007,35(11)：117-121.
[162]聂士忠,王玉泰.最大熵谱分析方法和MATLAB中对短记录资料的谱分析[J].山东师范大学学报,2005,20(3)：40-42.
© 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 |