车辆与内燃机振声信号盲分离及噪声源识别的研究
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摘要
近几年来,随着中国的汽车和高速列车产业迅猛发展,交通噪声的问题也越来越严重。内燃机和牵引系统分别是汽车和高速列车的主要噪声源。其中内燃机气门启闭时的拍击激励和活塞行程换向时的敲缸激励而产生的机械噪声信号,与内燃机燃烧激励引起的燃烧噪声都是典型的非稳态时变信号,且都出现在活塞行程上止点附近,是内燃机振动噪声源诊断与识别的重点和难点问题。同样,在高速列车底部机舱内的牵引系统也包含丰富的机械、电磁、流体及空气动力噪声,也是列车噪声源诊断的难点问题。由于目前传统的频谱分析技术很难对这些复杂噪声源进行有效的识别与分离,因此,对汽车内燃机和高速列车牵引系统的振动噪声特性和噪声源进行系统、深入的识别分析研究很有必要。本文围绕这些问题主要开展了以下研究工作：
对基于能量集中程度度量法的广义自适应S变换(AGST)的基本理论和实现过程进行了研究,通过仿真和实际内燃机振动信号验证了AGST是具有更高的时-频分辨率。采用AGST对某4缸4冲程内燃机的关键部件振动信号和关键位置的噪声信号进行了研究,结果表明该内燃机的各种机械激励(包括气门拍击激励、活塞敲击激励和往复惯性力激励等)引起的振动(噪声)成分和各种燃烧激励引起的振动(噪声)成分都能较为准确地被识别出来。
在集总平均经验模态分解(EEMD)理论基础上,综合目前的各种改进方法,提出了改进集总平均经验模态分解(MEEMD),并通过仿真信号和实际内燃机振动信号验证了MEEMD在有效抑制经验模态分解(EMD)的模态混叠问题和EEMD的模态分裂问题的同时还能够在极大程度上抑制白噪声在信号中的残余量,保证了分解结果的准确性和完备性。
采用MEEMD和AGST相结合的方法对某4缸4冲程内燃机振动信号中的机械激励和燃烧激励成分进行了分离与识别研究,并与相同转速下倒拖工况的振动信号分解结果进行了对比验证。研究结果表明,该内燃机振动信号中的机械激励成分和燃烧激励成分能够得到有效分离和识别。
采用MEEMD和AGST相结合的方法对某4缸4冲程内燃机振动成分对辐射噪声的贡献量进行了研究。研究结果表明,引起噪声辐射的主要激励成分能够被准确识别,通过这种方法可以科学有效地指导内燃机的低噪声改进工作,避免了内燃机的振动烈度降低了,而噪声水平没有明显改善的尴尬现象。
采用AGST对高速列车牵引系统的振动噪声特性和主要噪声源进行了识别研究,确定了高速列车下部机舱内牵引系统的噪声主要来自轮轨激励噪声、空气动力噪声、冷却模块风扇噪声和牵引变流器电磁辐射噪声。在此基础上对高速列车牵引系统噪声特性随运行时速的变化情况进行了研究,提出了低噪声运行和改进的建议,为高速列车减振降噪提供了有力的技术支持。
With the fast development of automobile and high-speed train industries recently in China, the traffic noise problem is getting increasingly serious. The internal combustion engine (ICE) and traction system are dominant noise sources of automobile and high-speed train, separately. The combustion noise induced by combustion excitation and mechanical noise induced by valve-slap and piston-slap are generated around the Top Dead Centre (TDC), both of which are typically transient time-varying signals. Therefore it's difficult to separate the mechanical noise from combustion noise. Meanwhile, it's difficult to identify the noise source of traction system concerning the combination of mechanical noise, electromagnetic noise, fluid noise, aerodynamic noise, etc. Therefore, it would be unable to identify and separate such complex noise souces by traditional spectral analysis methods. The following work has been done focusing on the vibro-acoustic characteristic analysis and noise source identification of the ICE and high-speed train's traction system.
An adaptive generalized S transform (AGST) based on concentration measure is introduced. The numerical and experimental ICE vibration signal analysis results both indicate that the time-frequency representation (TFR) obtained by AGST has a better time-frequency resolution. Then, the AGST method is adopted to analyze the vibration and noise signals from the critical parts of a four-stroke and four-cylinder ICE. The results indicate that, the vibration and noise components induced by mechanical excitation (such as valve-slap, piston-slap and reciprocating inertia force) and combustion excitation can be exactly identified in the TFR with the help of AGST.
A modified ensemble empirical mode decomposition (MEEMD) method is proposed base on the recent studies of the improvements of ensemble empirical mode decomposition (EEMD). The numerical and experimental ICE vibration signal analysis show that MEEMD is a better adaptive signal decomposed method, which not only restrains the disadvantage of mode mixing problem of the empirical mode decomposition (EMD) method, but also overcomes the non-IMFs, mode splitting and noise residue problems of EEMD method.
The MEEMD-AGST method is adopted to analyze the vibration signals of a four-stroke and four-cylinder ICE, which is validated by the vibration experiments under motored condition. The analysis results indicate that the mechanical excitation components and combustion excitation components in the vibration signal of ICE can be effectively separated by the MEEMD-AGST method.
The MEEMD-AGST method is adopted to study the noise contributions of a four-stroke and four-cylinder ICE's vibration components and identify the dominant noise sources. The results show that the noise contributions of different vibration components are obtained, and the vibration sources which generate the dominant noise are identified. The research achievements are of great significance to the noise control and structural optimization for ICE.
The AGST method is adopted to analyze the vibro-acoustic characteristic and identify the noise source of high-speed train's traction system. The research achievements indicate that the wheel-rail noise, electromagnetic noise from the traction converter, fan noise from the cooling module and aerodynamic noise are the major noise contributors. Then, the speed-varying characteristic of traction system noise is analyzed, and optimization suggestion for low-noise traction system is proposed.
对基于能量集中程度度量法的广义自适应S变换(AGST)的基本理论和实现过程进行了研究,通过仿真和实际内燃机振动信号验证了AGST是具有更高的时-频分辨率。采用AGST对某4缸4冲程内燃机的关键部件振动信号和关键位置的噪声信号进行了研究,结果表明该内燃机的各种机械激励(包括气门拍击激励、活塞敲击激励和往复惯性力激励等)引起的振动(噪声)成分和各种燃烧激励引起的振动(噪声)成分都能较为准确地被识别出来。
在集总平均经验模态分解(EEMD)理论基础上,综合目前的各种改进方法,提出了改进集总平均经验模态分解(MEEMD),并通过仿真信号和实际内燃机振动信号验证了MEEMD在有效抑制经验模态分解(EMD)的模态混叠问题和EEMD的模态分裂问题的同时还能够在极大程度上抑制白噪声在信号中的残余量,保证了分解结果的准确性和完备性。
采用MEEMD和AGST相结合的方法对某4缸4冲程内燃机振动信号中的机械激励和燃烧激励成分进行了分离与识别研究,并与相同转速下倒拖工况的振动信号分解结果进行了对比验证。研究结果表明,该内燃机振动信号中的机械激励成分和燃烧激励成分能够得到有效分离和识别。
采用MEEMD和AGST相结合的方法对某4缸4冲程内燃机振动成分对辐射噪声的贡献量进行了研究。研究结果表明,引起噪声辐射的主要激励成分能够被准确识别,通过这种方法可以科学有效地指导内燃机的低噪声改进工作,避免了内燃机的振动烈度降低了,而噪声水平没有明显改善的尴尬现象。
采用AGST对高速列车牵引系统的振动噪声特性和主要噪声源进行了识别研究,确定了高速列车下部机舱内牵引系统的噪声主要来自轮轨激励噪声、空气动力噪声、冷却模块风扇噪声和牵引变流器电磁辐射噪声。在此基础上对高速列车牵引系统噪声特性随运行时速的变化情况进行了研究,提出了低噪声运行和改进的建议,为高速列车减振降噪提供了有力的技术支持。
With the fast development of automobile and high-speed train industries recently in China, the traffic noise problem is getting increasingly serious. The internal combustion engine (ICE) and traction system are dominant noise sources of automobile and high-speed train, separately. The combustion noise induced by combustion excitation and mechanical noise induced by valve-slap and piston-slap are generated around the Top Dead Centre (TDC), both of which are typically transient time-varying signals. Therefore it's difficult to separate the mechanical noise from combustion noise. Meanwhile, it's difficult to identify the noise source of traction system concerning the combination of mechanical noise, electromagnetic noise, fluid noise, aerodynamic noise, etc. Therefore, it would be unable to identify and separate such complex noise souces by traditional spectral analysis methods. The following work has been done focusing on the vibro-acoustic characteristic analysis and noise source identification of the ICE and high-speed train's traction system.
An adaptive generalized S transform (AGST) based on concentration measure is introduced. The numerical and experimental ICE vibration signal analysis results both indicate that the time-frequency representation (TFR) obtained by AGST has a better time-frequency resolution. Then, the AGST method is adopted to analyze the vibration and noise signals from the critical parts of a four-stroke and four-cylinder ICE. The results indicate that, the vibration and noise components induced by mechanical excitation (such as valve-slap, piston-slap and reciprocating inertia force) and combustion excitation can be exactly identified in the TFR with the help of AGST.
A modified ensemble empirical mode decomposition (MEEMD) method is proposed base on the recent studies of the improvements of ensemble empirical mode decomposition (EEMD). The numerical and experimental ICE vibration signal analysis show that MEEMD is a better adaptive signal decomposed method, which not only restrains the disadvantage of mode mixing problem of the empirical mode decomposition (EMD) method, but also overcomes the non-IMFs, mode splitting and noise residue problems of EEMD method.
The MEEMD-AGST method is adopted to analyze the vibration signals of a four-stroke and four-cylinder ICE, which is validated by the vibration experiments under motored condition. The analysis results indicate that the mechanical excitation components and combustion excitation components in the vibration signal of ICE can be effectively separated by the MEEMD-AGST method.
The MEEMD-AGST method is adopted to study the noise contributions of a four-stroke and four-cylinder ICE's vibration components and identify the dominant noise sources. The results show that the noise contributions of different vibration components are obtained, and the vibration sources which generate the dominant noise are identified. The research achievements are of great significance to the noise control and structural optimization for ICE.
The AGST method is adopted to analyze the vibro-acoustic characteristic and identify the noise source of high-speed train's traction system. The research achievements indicate that the wheel-rail noise, electromagnetic noise from the traction converter, fan noise from the cooling module and aerodynamic noise are the major noise contributors. Then, the speed-varying characteristic of traction system noise is analyzed, and optimization suggestion for low-noise traction system is proposed.
引文
[1]马大猷.声学手册[M].北京：科学出版社,2004
[2]. Guidelines for community noise[R]:World Health Organization,1999
[3]洪宗辉.环境噪声控制工程[M].北京：高等教育出版社,2002
[4].金羊网.720万辆!2006年中国汽车销量创新高[OL].http://www.ycwb.com/big5/ycwb/2007-01/19/content_1357983.htm,20070119
[5].网易.中国2010年汽车销量稳居全球第一达1806万辆[OL].http://money.163.com/11/0110/16/6Q23HQJR002526O5.html,20110110
[6].中国新闻网.回眸“十一五”：中国汽车演绎狂飙突进[OL].http://www.chinanews.com/cj/2010/10-17/2592084.shtml,20101017
[7].搜狐.汽车噪音何时不再扰民[OL].http://news.sohu.com/20080717/n258193188.shtml,2008717
[8]Talotte, C., Gautier, P. E.,Thompson, D. J.et al. Identification, modelling and reduction potential of railway noise sources:a critical survey[J]. Journal of Sound and Vibration,2003,267 (3):447-468
[9]朱孟华.内燃机振动与噪声控制[M].北京：国防工业出版社,1995
[10]余成波,何怀波,石晓辉.内燃机振动控制及应用[M].北京：国防工业出版社,1997
[11]张志华,周松,黎苏.内燃机排放与噪声控制[M].哈尔滨：哈尔滨工程大学出版社,1999
[12]庞剑,谌刚,何华.汽车噪声与振动—理论与应用[M].北京：北京理工大学出版社,2006
[13]舒歌群,高文志,刘月辉.动力机械振动与噪声[M].天津：天津大学出版社,2008
[14]黄其柏.工程噪声控制学[M].武汉：华中理工大学出版社,1999
[15]Maynard, J. D., Williams, E. G.,Lee, Y. Nearfield acoustic holography:Ⅰ. Theory of generalized holography and the development of NAH [J]. Journal of the Acoustical Society of America,1985,78 (4):1395-1413
[16]Van Veen, B. D.,Buckley, K. M. Beamforming:a versatile approach to spatial filtering[J]. Assp Magazine, IEEE,1988,5 (2):4-24
[17]Lu, H. C.,Wu, S. F. Reconstruction of vibro-acoustic response of a plate using Helmholtz equation least-squares method[J],2006,120 (5):3344
[18]Lu, H.,Wu, S. F. Reconstruction of vibroacoustic responses of a highly nonspherical structure using Helmholtz equation least-squares method[J]. Journal of the Acoustical Society of America,2009,125 (3) 1538-1548
[19]Gabor, D. Theory of communication [J]. J. lee (London),1946,93 (Ⅲ):429-451
[20]Ville, J. Theorie et applications de la notion de signal analylique[J]. Cables Et Transmission,1948.2A (1) 62-77
[21]Jones, D. L..Baraniuk, R. G. A SIMPLE SCHEME FOR ADAPTING TIME-FREQUENCY REPRESENTATIONS[J]. IEEE Transactions On Signal Processing,1994,42 (12):3530-3535
[22]Rudoy, D., Basu, P.,Quatieri, T. E.et al. Adaptive short-time analysis-synthesis for speech enhancement[M]. NEW YORK:IEEE,2008
[23]Czerwinski, R. N.,Jones, D. L. Adaptive short-time Fourier analysis[J]. IEEE Signal Processing Letters,1997, 4(2):42-45
[24]Jiang, Y. Q.,He, Y. G. Frequency estimation of electric signals based on the adaptive short-time Fourier transform[J]. International Journal of Electronics,2009,96 (3):267-279
[25]Jin, Y., Hao, Z. Y.,Zheng, X. Comparison of different techniques for time-frequency analysis of internal combustion engine vibration signals[J]. Journal of Zhejiang University-Science a,2011,12(7):519-531
[26]Vulli, S., Dunne, J. F.,Potenza, R.et al. Time-frequency analysis of single-point engine-block vibration measurements for multiple excitation-event identification[J]. Journal of Sound and Vibration.2009,321 (3-5): 1129-1143
[27]金阳.基于高斯窗的时-频分析分辨率限值研究及其工程应用[D].博士学位论文,浙江大学,2011
[28]Flandrin, P.,Escudie, B. AN INTERPRETATION OF THE PSEUDO-WIGNER-VILLE DISTRIBUTION[J]. Signal Processing,1984,6(1):27-36
[29]Andria, G.,Savino, M. Interpolated smoothed pseudo Wigner-Ville distribution for accurate spectrum analysis[J]. IEEE Transactions On Instrumentation and Measurement,1996,45 (4):818-823
[30]Auger, F.,Flandrin, P. Improving the readability of time-frequency and time-scale representations by the reassignment method[J]. IEEE Transactions on Signal Processing,1995,43 (8):1069-1089
[31]Choi, H. I.,Williams, W. J. Improved time-frequency representation of multicomponent signals using exponential kernels [J]. IEEE Transactions on Acoustics Speech and Signal Processing,1989,37 (6):862-871
[32]Zhao, Y. X., Atlas, L. E.,Marks, R. J. The use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals [J]. IEEE Transactions on Acoustics Speech and Signal Processing,1990, 38 (7):1084-1091
[33]Stankovic, L. A method for time-frequency analysis [J]. IEEE Transactions on Signal Processing,1994,42 (1):225-229
[34]Stankovic, L.,Bohme, J. F. Time-frequency analysis of multiple resonances in combustion engine signals[J]. Signal Processing,1999,79 (1):15-28
[35]Wang, C. D., Zhang, Y. Y.,Zhong, Z. Y. Fault diagnosis for diesel valve trains based on time-frequency images[J]. Mechanical Systems and Signal Processing,2008,22 (8):1981-1993
[36]Albarbar, A., Gu, F.,Ball, A. D.et al. Acoustic monitoring of engine fuel injection based on adaptive filtering techniques[J]. Applied Acoustics,2010,71 (12):1132-1141
[37]Wu, J. D.,Huang, C. K. An engine fault diagnosis system using intake manifold pressure signal and Wigner-Ville distribution technique [J]. Expert Systems with Applications,2011,38 (1):536-544
[38]Grossmann, A..Morlet, J. DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE[J]. Siam Journal On Mathematical Analysis,1984,15(4):723-736
[39]Mallat, S. G. A A theory for multiresolution signal decomposition:the wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1989, 11 (7):674-693
[40]Mallat, S. G. Multiresolution Approximations and Wavelet Orthonormal Bases of L2(R) [J]. Transactions of the American Mathematical Society,1989.315 (1):69-87
[41]Sweldens, W. The Lifting Scheme:A Construction of Second Generation Wavelets[J]. Siam News,1998,29 (2):511
[42]Le Pennec, E.,Mallat, S. Image compression with geometrical wavelets[C].2000 IEEE International Conference on Image Processing Vancouver, BC, Canada
[43]郝志勇,韩军.小波变换技术在内燃机振声信号分析中的应用[J].内燃机工程,2003,24(6)：7-9
[44]韩军.内燃机的非平稳信号分析方法及其噪声源小波识别技术的研究[D].博士学位论文,天津大学,2004
[45]杨金才,郝志勇,贾维新.内燃机传动噪声识别的小波分析方法[J].内燃机工程,2005,26(5)：74-76
[46]杨金才,郝志勇.用A计权连续小波变换识别内燃机噪声源[J].浙江大学学报(工学版),2006,40(7)：1174-1177
[47]金岩,郝志勇.利用振动信号的小波变换识别内燃机噪声源的研究[J].内燃机工程,2006,27(2)61-63
[48]Jing, G. X.,Hao, Z. Y. A new technique based on traditional wavelet transform used in NVH application of internal combustion engine[J]. Mechanical Systems and Signal Processing,2009,23 (3):979-985
[49]Stockwell, R. G., Mansinha, L.,Lowe, R. P. Localization of the complex spectrum:The S transform[J]. IEEE Transactions On Signal Processing,1996,44 (4):998-1001
[50]Theophanis, S.,Queen, J. Color display of the localized spectrum[J]. Geophysics.2000,65 (4):1330-1340
[51]Pinnegar, C. R.,Mansinha, L. The S-transform with windows of arbitrary and varying shape[J]. Geophysics. 2003,68 (1):381-385
[52]Pinnegar, C. R.,Mansinha, L. The bi-Gaussian S-transform[J]. Siam Journal On Scientific Computing,2003, 24 (5):1678-1692
[53]Varanini, M., De Paolis, G.,Emdin, M.et al. Spectral analysis of cardiovascular time series by the S-transform[C].1997 Computers in Cardiology Lund, Sweden
[54]Livanos, G., Ranganathan, N.,Jiang, J. Heart sound analysis using the S transform[C].2000 Computers in Cardiology Cambridge, MA, USA
[55]Sejdic, E.,Jiang, J. Comparative study of three time-frequency representations with applications to a novel correlation method[C].2004 ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings Montreal, Que, Canada
[56]Mcfadden, P. D., Cook, J. G.,Forster, L. M. Decomposition of gear vibration signals by the generalized S transform[J]. Mechanical Systems and Signal Processing,1999,13 (5):691-708
[57]Rehorn, A. G., Sejdic, E.,Jiang, J. Fault diagnosis in machine tools using selective regional correlation[J]. Mechanical Systems and Signal Processing,2006,20 (5):1221-1238
[58]Kondaveeti, S., Reddy, M. J. B.,Mohanta, D. K. Power quality analysis on EHV transmission line using modified S-transform[C].2010 2010 9th Conference on Environment and Electrical Engineering, EEEIC 2010 Prague, Czech republic
[59]Sejdic, E.,Jiang, J. Selective regional correlation for pattern recognition[J]. IEEE Transactions On Systems, Man, and Cybernetics Part a:Systems and Humans,2007,37(1):82-93
[60]Sejdic, E., Djurovic, I.,Jiang, J. S-transform with frequency dependent Kaiser window[C].20072007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol Ⅲ, Pts 1-3, Proceedings NEW YORK: IEEE
[61]Man, W. S., Wu, B. Y.,Gao, J. H.et al. A data-adaptive S-transform[C].2008 Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR'07 Beijing, China
[62]Sejdic, E., Djurovic, I.,Jiang, J. A window width optimized S-transform[J]. Eurasip Journal On Advances in Signal Processing,2008 (672941)
[63]Djurovic, I., Sejdic, E., Jiang, J. Frequency-based window width optimization for S-transform [J]. Aeu-International Journal of Electronics and Communications,2008,62 (4):245-250 [64]Stankovic, L. A measure of some time-frequency distributions concentration[J]. Signal Processing,2001,81 (3):621-631
[65]王成栋,张优云,夏勇.基于S变换的柴油机气阀机构故障诊断研究[J].内燃机学报,2003(4)：271-275
[66]徐红梅,郝志勇,贾维新等.基于S变换的内燃机气缸盖振动特性研究[J].内燃机工程,2008(3)：68-71
[67]徐红梅,郝志勇,贾维新等.基于S变换的内燃机噪声信号时频特性[J].江苏大学学报(自然科学版),2008(2)：115-118
[68]Hao, Z. Y., Xu, H. M.,Zheng, G. T.et al. Study on the time-frequency characteristics of engine induction noise in acceleration based on S transform[C].2008 CISP 2008:FIRST INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING
[69]Li, B., Zhang, P. L.,Liang, S. B.et al. Feature extraction for engine fault diagnosis utilizing the generalized S-transform and non-negative tensor factorization[J]. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science,2011,225 (C8):1936-1949
[70]Jutten, C.,Herault, J. Blind separation of sources, part Ⅰ. An adaptive algorithm based on neuromimetic architecture[J]. Signal Processing,1991,24 (1):1-10
[71]Comon, P.. Jutten, C.,Herault, J. Blind separation of sources, part II. Problems statement[J]. Signal Processing, 1991,24 (1):11-20
[72]Sorouchyari, E. Blind separation of sources, part III. Stability analysisfJ]. Signal Processing,1991,24 (1) 21-29
[73]Comon, P. Independent component analysis. A new concept?[J]. Signal Processing,1994,36 (3):287-314
[74]Cardoso, J. F..Souloumiac, A. Blind beamforming for non-Gaussian signals[J]. lee Proceedings, Part F:Radar and Signal Processing,1993.140 (6):362-370
[75]Cardoso, J. High-order contrasts for independent component analysis[J]. Neural Computation,1999,11(1) 157-192
[76]Bell, A. J.,Sejnowski. T. J. An Information-Maximization Approach to Blind Separation and Blind Deconvolution[J]. Neural Computation,1995,7(6):1129-1159
[77]Lee, T., Girolami. M.Sejnowski, T. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources[J]. Neural Computation,1999,11 (2):417-441
[78]Hyvarinen. A.,Oja, E. A Fast Fixed-Point Algorithm for Independent Component Analysis[J]. Neural Computation,1997,9(7):1483-1492
[79]Hyvarinen, A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Transactions On Neural Networks,1999,10(3):626-634
[80]Amari, S., Cichocki, A.,Yang, H. H. A new learning algorithm for blind separation of sources[J]. Advances in Neural Information Processing Systems,1996,8:755-763
[81]Amari, S. Natural Gradient Works Efficiently in Learning[J]. Neural Computation,1998,10(2):251-276
[82]Li, W., Gu, F.,Ball, A. D.et al. The identification of diesel engine noise sources[C].2001 NOISE AND VIBRATION ENGINEERING, VOLS 1-3, PROCEEDINGS HEVERLEE:KATHOLIEKE UNIV LEUVEN, DEPT WERKTUIGKUNDE
[83]Liu, X. H.,Randall, R. B. Blind source separation of internal combustion engine piston slap from other measured vibration signals[J]. Mechanical Systems and Signal Processing,2005,19(6):1196-1208
[84]金岩,郝志勇,杨陈.内燃机噪声信号的独立分量分析[J].内燃机工程,2007(4)：81-83
[85]Hao, Z. Y., Jin, Y.,Yang. C. Study of engine noise based on independent component analysis[J]. Journal of Zhejiang University-Science a,2007,8(5):772-777
[86]徐红梅,郝志勇,郭磊等.基于独立成分小波分析的内燃机噪声源识别[J].内燃机工程,2007(6)61-65
[87]Albarbar, A., Gu, F.,Ball, A. D. Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis[J]. Measurement,2010,43 (10):1376-1386
[88]Huang, N. E., Shen, Z.,Long, S. R.et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceeding of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences,1998,454A:903-995
[89]Huang. N. E., Shen, Z..Long, S. R. A new view of nonlinear water waves:The Hilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31:417-457
[90]Flandrin, P., Rilling, G.,Goncalves, P. Empirical mode decomposition as a filter bank[J]. IEEE Signal Processing Letters,2004.11(2 PART I):112-114
[91]Wu, Z. H..Huang, N. E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceeding of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 2004,460A:1597-1611
[92]Flandrin, P., Goncalves, P.,Rilling, G. EMD Equivalent filter banks, from interpretation to applicationsfM]: World Scientific, Singapore,2005
[93]Gledhill, R. J. Methods for investigating conformational change in biomolecular simulations[D]. A dissertation for the degree of Doctor of Philosophy at the Department of Chemistry, doctoral dissertation, the University of Southampton,2003
[94]Wu, Z. H.,Huang, N. Ensemble Empirical Mode Decomposition:A Noise Assised Data Analysis Method[R]: COLA Technical Reports,2005
[95]Wu, Z.,Huang, N. E. Ensemble empirical mode decomposition:A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1 (1):1-41
[96]Yeh, J. R., Shieh, J. S.,Huang, N. E. Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis,2010,2 (2):135-156
[97]Wu, Z.,Huang, N. E. On the filtering properties of the empirical mode decomposition[J]. Advances in Adaptive Data Analysis,2010,2 (4):397-414
[98]Hwang, P. A., Huang, N. E.,Wang, D. W. A note on analyzing nonlinear and nonstationary ocean wave data[J]. Applied Ocean Research,2003,25 (4):187-193
[99]Datig, M.,Schlurmann, T. Performance and limitations of the Hilbert-Huang transformation (HHT) with an application to irregular water waves[J]. Ocean Engineering,2004,31 (14-15):1783-1834
[100]Lai, R. J.,Huang, N. Investigation of vertical and horizontal momentum transfer in the Gulf of Mexico using empirical mode decomposition method[J]. Journal of Physical Oceanography,2005,35 (8):1383-1402
[101]Zhang, R. R., Ma, S.,Hartzell, S. Signatures of the seismic source in EMD-based characterization of the 1994 Northridge, California, earthquake recordings[J]. Bulletin of the Seismological Society of America,2003,93 (1): 501-518
[102]Liang, H., Lin, Z.,Mccallum, R. W. Artifact reduction in electrogastrogram based on empirical mode decomposition method[J]. Medical and Biological Engineering and Computing,2000,38 (1):35-41
[103]Yang, J. N., Lei, Y.,Pan, S. W.et al. System identification of linear structures based on Hilbert-Huang spectral analysis. Part 1:normal modes[J]. Earthquake Engineering & Structural Dynamics,2003,32 (9):1443-1467
[104]Yang, J. N., Lei, Y.,Pan, S. W.et al. System identification of linear structures based on Hilbert-Huang spectral analysis. Part 2:Complex modes[J]. Earthquake Engineering & Structural Dynamics,2003,32 (10):1533-1554
[105]Liu, B., Riemenschneider, S.,Xu, Y. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectram[J]. Mechanical Systems and Signal Processing,2006,20 (3):718-734
[106]Wu, T. Y.,Chung, Y. L. Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach[J]. Smart Materials and Structures,2009,18(9)
[107]Chen, L., Li, X.,Li, X. B.et al. Signal extraction using ensemble empirical mode decomposition and sparsity in pipeline magnetic flux leakage nondestructive evaluation[J]. Review of Scientific Instruments,2009,80 (2)
[108]Lei, Y.,Zuo, M. J. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs[J]. Measurement Science and Technology,2009,20 (12)
[109]Lei, Y., He, Z.,Zi, Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing,2009,23 (4):1327-1338
[110]徐红梅,郝志勇,杨陈等.基于EMD和HHT的内燃机噪声信号时频特性研究[J].内燃机工程,2008,29(6)：60-64，69
[111]樊新海,安钢,张传清等.基于排气噪声EMD的柴油机失火故障诊断[J].内燃机工程,2010,31(1)：78-81
[112]蔡艳平,李艾华,王涛等.基于EMD-Wigner-Ville(?)内燃机振动时频分析[J].振动工程学报,2010,23(4)：430-437
[113]Li, Y. J., Tse, P. W.,Yang, X.et al. EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine[J]. Mechanical Systems and Signal Processing,2010,24 (1):193-210
[114]Badaoui, M. E., Daniere, J.,Guillet, F.et al. Separation of combustion noise and piston-slap in diesel engine- Part I:Separation of combustion noise and piston-slap in diesel engine by cyclic Wiener filtering[J]. Mechanical Systems and Signal Processing,2005,19(6):1209-1217
[115]Serviere, C, Lacoume, J. L.,E1 Badaoui, M. Separation of combustion noise and piston-slap in diesel engine-Part Ⅱ:Separation of combustion noise and piston-slap using blind source separation methods[J]. Mechanical Systems and Signal Processing,2005,19(6):1218-1229
[116]Liu, X., Randall, R. B.,Antoni, J. Blind separation of internal combustion engine vibration signals by a deflation method[J]. Mechanical Systems and Signal Processing,2008,22 (5):1082-1091
[117]Cooley, J. W.,Tukey, J. W. An algorithm for machine calculation of complex Fourier series[J]. Mathematics of Computation,1965,19 (90):297-301
[118]卢文祥,杜润生.机械工程测试·信息·信号分析[M].武汉：华中理工大学出版社,1999
[119]褚福磊,彭志科,冯志鹏等.机械故障诊断中的现代信号处理方法[M].北京：科学出版社,2009
[120]郑君里,应启珩,著杨为理.信号与系统.上册[M].北京：高等教育出版社,2011
[121]Wikipedia.Gibbs phenomenon[OL]. http://en.wikipedia.org/wiki/Gibbs_phenomenon
[122]Harris, F. J. On the use of windows for harmonic analysis with the discrete Fourier transform[J]. Proceedings of the IEEE,1978,66 (1):51-83
[123]Mathworks.Hann (Hanning) window[OL]. http://www.mathworks.cn/help/toolbox/signal/ref/hann.html
[124]Mathworks.Gaussian window[OL]. http://www.mathworks.cn/help/toolbox/signal/ref/gausswin.html
[125]Cohen, L. Time-Frequency Analysis:Theory and Applications[M]. Englewood Cliffs, New Jersey:Prentice Hall,1995
[126]Grochenig. K. Foundations of Time-Frequency Analysis[M]. Boston:Birkhauser,2001
[127]吴正国,夏立,尹为民.现代信号处理技术—高阶谱、时频分析与小波变换[M].武汉：武汉大学出版社,2003
[128]皇甫堪,陈建文.现代数字信号处理[M].北京：电子工业出版社,2003
[129]胡广书.现代信号处理教程[M].北京：清华大学出版社,2004
[130]Sejdic, E., Djurovic, I.,Jiang, J. Time-frequency feature representation using energy concentration:An overview of recent advances[J]. Digital Signal Processing:A Review Journal,2009,19 (1):153-183
[131]Jones, D. L.,Parks, T. W. HIGH RESOLUTION DATA-ADAPTIVE TIME-FREQUENCY REPRESENTATION.[C].1987 ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings Dallas, TX, USA
[132]Jones, D. L.,Parks, T. W. A high resolution data-adaptive time-frequency representation[J]. IEEE Transactions On Acoustics, Speech, and Signal Processing,1990,38 (12):2127-2135
[133]Auger, F.,Flandrin, P. Why and how of time-frequency reassignmentfC].1994 Philadelphia, PA, USA
[134]Auger, F.,Flandrin, P. Improving the readability of time-frequency and time-scale representations by the reassignment method[J]. IEEE Transactions On Signal Processing,1995,43 (5):1068-1089
[135]Baraniuk. R. G.,Jones, D. L. Signal-dependent time-frequency representation:Fast algorithm for optimal kernel design[J]. IEEE Transactions On Signal Processing,1994,42 (1):134-146
[136]Gillespie, B.,Atlas, L. E. Optimizing time-frequency kernels for classification[J]. IEEE Trans. Signal Process, 2001,49 (3).485-496
[137]Jones, D. L.,Parks, T. W. A high resolution data-adaptive time-frequency representation[J]. IEEE Transactions On Acoustics, Speech, and Signal Processing,1990,38 (12):2127-2135
[138]何正嘉,訾艳阳,张西宁.现代信号处理及工程应用[M].西安：西安交通大学出版社,2007
[139]于德介,程军圣,杨宇.机械故障诊断的Hilbert-Huang变换方法[M].北京：科学出版社,2006
[2]. Guidelines for community noise[R]:World Health Organization,1999
[3]洪宗辉.环境噪声控制工程[M].北京：高等教育出版社,2002
[4].金羊网.720万辆!2006年中国汽车销量创新高[OL].http://www.ycwb.com/big5/ycwb/2007-01/19/content_1357983.htm,20070119
[5].网易.中国2010年汽车销量稳居全球第一达1806万辆[OL].http://money.163.com/11/0110/16/6Q23HQJR002526O5.html,20110110
[6].中国新闻网.回眸“十一五”：中国汽车演绎狂飙突进[OL].http://www.chinanews.com/cj/2010/10-17/2592084.shtml,20101017
[7].搜狐.汽车噪音何时不再扰民[OL].http://news.sohu.com/20080717/n258193188.shtml,2008717
[8]Talotte, C., Gautier, P. E.,Thompson, D. J.et al. Identification, modelling and reduction potential of railway noise sources:a critical survey[J]. Journal of Sound and Vibration,2003,267 (3):447-468
[9]朱孟华.内燃机振动与噪声控制[M].北京：国防工业出版社,1995
[10]余成波,何怀波,石晓辉.内燃机振动控制及应用[M].北京：国防工业出版社,1997
[11]张志华,周松,黎苏.内燃机排放与噪声控制[M].哈尔滨：哈尔滨工程大学出版社,1999
[12]庞剑,谌刚,何华.汽车噪声与振动—理论与应用[M].北京：北京理工大学出版社,2006
[13]舒歌群,高文志,刘月辉.动力机械振动与噪声[M].天津：天津大学出版社,2008
[14]黄其柏.工程噪声控制学[M].武汉：华中理工大学出版社,1999
[15]Maynard, J. D., Williams, E. G.,Lee, Y. Nearfield acoustic holography:Ⅰ. Theory of generalized holography and the development of NAH [J]. Journal of the Acoustical Society of America,1985,78 (4):1395-1413
[16]Van Veen, B. D.,Buckley, K. M. Beamforming:a versatile approach to spatial filtering[J]. Assp Magazine, IEEE,1988,5 (2):4-24
[17]Lu, H. C.,Wu, S. F. Reconstruction of vibro-acoustic response of a plate using Helmholtz equation least-squares method[J],2006,120 (5):3344
[18]Lu, H.,Wu, S. F. Reconstruction of vibroacoustic responses of a highly nonspherical structure using Helmholtz equation least-squares method[J]. Journal of the Acoustical Society of America,2009,125 (3) 1538-1548
[19]Gabor, D. Theory of communication [J]. J. lee (London),1946,93 (Ⅲ):429-451
[20]Ville, J. Theorie et applications de la notion de signal analylique[J]. Cables Et Transmission,1948.2A (1) 62-77
[21]Jones, D. L..Baraniuk, R. G. A SIMPLE SCHEME FOR ADAPTING TIME-FREQUENCY REPRESENTATIONS[J]. IEEE Transactions On Signal Processing,1994,42 (12):3530-3535
[22]Rudoy, D., Basu, P.,Quatieri, T. E.et al. Adaptive short-time analysis-synthesis for speech enhancement[M]. NEW YORK:IEEE,2008
[23]Czerwinski, R. N.,Jones, D. L. Adaptive short-time Fourier analysis[J]. IEEE Signal Processing Letters,1997, 4(2):42-45
[24]Jiang, Y. Q.,He, Y. G. Frequency estimation of electric signals based on the adaptive short-time Fourier transform[J]. International Journal of Electronics,2009,96 (3):267-279
[25]Jin, Y., Hao, Z. Y.,Zheng, X. Comparison of different techniques for time-frequency analysis of internal combustion engine vibration signals[J]. Journal of Zhejiang University-Science a,2011,12(7):519-531
[26]Vulli, S., Dunne, J. F.,Potenza, R.et al. Time-frequency analysis of single-point engine-block vibration measurements for multiple excitation-event identification[J]. Journal of Sound and Vibration.2009,321 (3-5): 1129-1143
[27]金阳.基于高斯窗的时-频分析分辨率限值研究及其工程应用[D].博士学位论文,浙江大学,2011
[28]Flandrin, P.,Escudie, B. AN INTERPRETATION OF THE PSEUDO-WIGNER-VILLE DISTRIBUTION[J]. Signal Processing,1984,6(1):27-36
[29]Andria, G.,Savino, M. Interpolated smoothed pseudo Wigner-Ville distribution for accurate spectrum analysis[J]. IEEE Transactions On Instrumentation and Measurement,1996,45 (4):818-823
[30]Auger, F.,Flandrin, P. Improving the readability of time-frequency and time-scale representations by the reassignment method[J]. IEEE Transactions on Signal Processing,1995,43 (8):1069-1089
[31]Choi, H. I.,Williams, W. J. Improved time-frequency representation of multicomponent signals using exponential kernels [J]. IEEE Transactions on Acoustics Speech and Signal Processing,1989,37 (6):862-871
[32]Zhao, Y. X., Atlas, L. E.,Marks, R. J. The use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals [J]. IEEE Transactions on Acoustics Speech and Signal Processing,1990, 38 (7):1084-1091
[33]Stankovic, L. A method for time-frequency analysis [J]. IEEE Transactions on Signal Processing,1994,42 (1):225-229
[34]Stankovic, L.,Bohme, J. F. Time-frequency analysis of multiple resonances in combustion engine signals[J]. Signal Processing,1999,79 (1):15-28
[35]Wang, C. D., Zhang, Y. Y.,Zhong, Z. Y. Fault diagnosis for diesel valve trains based on time-frequency images[J]. Mechanical Systems and Signal Processing,2008,22 (8):1981-1993
[36]Albarbar, A., Gu, F.,Ball, A. D.et al. Acoustic monitoring of engine fuel injection based on adaptive filtering techniques[J]. Applied Acoustics,2010,71 (12):1132-1141
[37]Wu, J. D.,Huang, C. K. An engine fault diagnosis system using intake manifold pressure signal and Wigner-Ville distribution technique [J]. Expert Systems with Applications,2011,38 (1):536-544
[38]Grossmann, A..Morlet, J. DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE[J]. Siam Journal On Mathematical Analysis,1984,15(4):723-736
[39]Mallat, S. G. A A theory for multiresolution signal decomposition:the wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1989, 11 (7):674-693
[40]Mallat, S. G. Multiresolution Approximations and Wavelet Orthonormal Bases of L2(R) [J]. Transactions of the American Mathematical Society,1989.315 (1):69-87
[41]Sweldens, W. The Lifting Scheme:A Construction of Second Generation Wavelets[J]. Siam News,1998,29 (2):511
[42]Le Pennec, E.,Mallat, S. Image compression with geometrical wavelets[C].2000 IEEE International Conference on Image Processing Vancouver, BC, Canada
[43]郝志勇,韩军.小波变换技术在内燃机振声信号分析中的应用[J].内燃机工程,2003,24(6)：7-9
[44]韩军.内燃机的非平稳信号分析方法及其噪声源小波识别技术的研究[D].博士学位论文,天津大学,2004
[45]杨金才,郝志勇,贾维新.内燃机传动噪声识别的小波分析方法[J].内燃机工程,2005,26(5)：74-76
[46]杨金才,郝志勇.用A计权连续小波变换识别内燃机噪声源[J].浙江大学学报(工学版),2006,40(7)：1174-1177
[47]金岩,郝志勇.利用振动信号的小波变换识别内燃机噪声源的研究[J].内燃机工程,2006,27(2)61-63
[48]Jing, G. X.,Hao, Z. Y. A new technique based on traditional wavelet transform used in NVH application of internal combustion engine[J]. Mechanical Systems and Signal Processing,2009,23 (3):979-985
[49]Stockwell, R. G., Mansinha, L.,Lowe, R. P. Localization of the complex spectrum:The S transform[J]. IEEE Transactions On Signal Processing,1996,44 (4):998-1001
[50]Theophanis, S.,Queen, J. Color display of the localized spectrum[J]. Geophysics.2000,65 (4):1330-1340
[51]Pinnegar, C. R.,Mansinha, L. The S-transform with windows of arbitrary and varying shape[J]. Geophysics. 2003,68 (1):381-385
[52]Pinnegar, C. R.,Mansinha, L. The bi-Gaussian S-transform[J]. Siam Journal On Scientific Computing,2003, 24 (5):1678-1692
[53]Varanini, M., De Paolis, G.,Emdin, M.et al. Spectral analysis of cardiovascular time series by the S-transform[C].1997 Computers in Cardiology Lund, Sweden
[54]Livanos, G., Ranganathan, N.,Jiang, J. Heart sound analysis using the S transform[C].2000 Computers in Cardiology Cambridge, MA, USA
[55]Sejdic, E.,Jiang, J. Comparative study of three time-frequency representations with applications to a novel correlation method[C].2004 ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings Montreal, Que, Canada
[56]Mcfadden, P. D., Cook, J. G.,Forster, L. M. Decomposition of gear vibration signals by the generalized S transform[J]. Mechanical Systems and Signal Processing,1999,13 (5):691-708
[57]Rehorn, A. G., Sejdic, E.,Jiang, J. Fault diagnosis in machine tools using selective regional correlation[J]. Mechanical Systems and Signal Processing,2006,20 (5):1221-1238
[58]Kondaveeti, S., Reddy, M. J. B.,Mohanta, D. K. Power quality analysis on EHV transmission line using modified S-transform[C].2010 2010 9th Conference on Environment and Electrical Engineering, EEEIC 2010 Prague, Czech republic
[59]Sejdic, E.,Jiang, J. Selective regional correlation for pattern recognition[J]. IEEE Transactions On Systems, Man, and Cybernetics Part a:Systems and Humans,2007,37(1):82-93
[60]Sejdic, E., Djurovic, I.,Jiang, J. S-transform with frequency dependent Kaiser window[C].20072007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol Ⅲ, Pts 1-3, Proceedings NEW YORK: IEEE
[61]Man, W. S., Wu, B. Y.,Gao, J. H.et al. A data-adaptive S-transform[C].2008 Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR'07 Beijing, China
[62]Sejdic, E., Djurovic, I.,Jiang, J. A window width optimized S-transform[J]. Eurasip Journal On Advances in Signal Processing,2008 (672941)
[63]Djurovic, I., Sejdic, E., Jiang, J. Frequency-based window width optimization for S-transform [J]. Aeu-International Journal of Electronics and Communications,2008,62 (4):245-250 [64]Stankovic, L. A measure of some time-frequency distributions concentration[J]. Signal Processing,2001,81 (3):621-631
[65]王成栋,张优云,夏勇.基于S变换的柴油机气阀机构故障诊断研究[J].内燃机学报,2003(4)：271-275
[66]徐红梅,郝志勇,贾维新等.基于S变换的内燃机气缸盖振动特性研究[J].内燃机工程,2008(3)：68-71
[67]徐红梅,郝志勇,贾维新等.基于S变换的内燃机噪声信号时频特性[J].江苏大学学报(自然科学版),2008(2)：115-118
[68]Hao, Z. Y., Xu, H. M.,Zheng, G. T.et al. Study on the time-frequency characteristics of engine induction noise in acceleration based on S transform[C].2008 CISP 2008:FIRST INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING
[69]Li, B., Zhang, P. L.,Liang, S. B.et al. Feature extraction for engine fault diagnosis utilizing the generalized S-transform and non-negative tensor factorization[J]. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science,2011,225 (C8):1936-1949
[70]Jutten, C.,Herault, J. Blind separation of sources, part Ⅰ. An adaptive algorithm based on neuromimetic architecture[J]. Signal Processing,1991,24 (1):1-10
[71]Comon, P.. Jutten, C.,Herault, J. Blind separation of sources, part II. Problems statement[J]. Signal Processing, 1991,24 (1):11-20
[72]Sorouchyari, E. Blind separation of sources, part III. Stability analysisfJ]. Signal Processing,1991,24 (1) 21-29
[73]Comon, P. Independent component analysis. A new concept?[J]. Signal Processing,1994,36 (3):287-314
[74]Cardoso, J. F..Souloumiac, A. Blind beamforming for non-Gaussian signals[J]. lee Proceedings, Part F:Radar and Signal Processing,1993.140 (6):362-370
[75]Cardoso, J. High-order contrasts for independent component analysis[J]. Neural Computation,1999,11(1) 157-192
[76]Bell, A. J.,Sejnowski. T. J. An Information-Maximization Approach to Blind Separation and Blind Deconvolution[J]. Neural Computation,1995,7(6):1129-1159
[77]Lee, T., Girolami. M.Sejnowski, T. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources[J]. Neural Computation,1999,11 (2):417-441
[78]Hyvarinen. A.,Oja, E. A Fast Fixed-Point Algorithm for Independent Component Analysis[J]. Neural Computation,1997,9(7):1483-1492
[79]Hyvarinen, A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Transactions On Neural Networks,1999,10(3):626-634
[80]Amari, S., Cichocki, A.,Yang, H. H. A new learning algorithm for blind separation of sources[J]. Advances in Neural Information Processing Systems,1996,8:755-763
[81]Amari, S. Natural Gradient Works Efficiently in Learning[J]. Neural Computation,1998,10(2):251-276
[82]Li, W., Gu, F.,Ball, A. D.et al. The identification of diesel engine noise sources[C].2001 NOISE AND VIBRATION ENGINEERING, VOLS 1-3, PROCEEDINGS HEVERLEE:KATHOLIEKE UNIV LEUVEN, DEPT WERKTUIGKUNDE
[83]Liu, X. H.,Randall, R. B. Blind source separation of internal combustion engine piston slap from other measured vibration signals[J]. Mechanical Systems and Signal Processing,2005,19(6):1196-1208
[84]金岩,郝志勇,杨陈.内燃机噪声信号的独立分量分析[J].内燃机工程,2007(4)：81-83
[85]Hao, Z. Y., Jin, Y.,Yang. C. Study of engine noise based on independent component analysis[J]. Journal of Zhejiang University-Science a,2007,8(5):772-777
[86]徐红梅,郝志勇,郭磊等.基于独立成分小波分析的内燃机噪声源识别[J].内燃机工程,2007(6)61-65
[87]Albarbar, A., Gu, F.,Ball, A. D. Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis[J]. Measurement,2010,43 (10):1376-1386
[88]Huang, N. E., Shen, Z.,Long, S. R.et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceeding of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences,1998,454A:903-995
[89]Huang. N. E., Shen, Z..Long, S. R. A new view of nonlinear water waves:The Hilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31:417-457
[90]Flandrin, P., Rilling, G.,Goncalves, P. Empirical mode decomposition as a filter bank[J]. IEEE Signal Processing Letters,2004.11(2 PART I):112-114
[91]Wu, Z. H..Huang, N. E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceeding of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 2004,460A:1597-1611
[92]Flandrin, P., Goncalves, P.,Rilling, G. EMD Equivalent filter banks, from interpretation to applicationsfM]: World Scientific, Singapore,2005
[93]Gledhill, R. J. Methods for investigating conformational change in biomolecular simulations[D]. A dissertation for the degree of Doctor of Philosophy at the Department of Chemistry, doctoral dissertation, the University of Southampton,2003
[94]Wu, Z. H.,Huang, N. Ensemble Empirical Mode Decomposition:A Noise Assised Data Analysis Method[R]: COLA Technical Reports,2005
[95]Wu, Z.,Huang, N. E. Ensemble empirical mode decomposition:A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1 (1):1-41
[96]Yeh, J. R., Shieh, J. S.,Huang, N. E. Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis,2010,2 (2):135-156
[97]Wu, Z.,Huang, N. E. On the filtering properties of the empirical mode decomposition[J]. Advances in Adaptive Data Analysis,2010,2 (4):397-414
[98]Hwang, P. A., Huang, N. E.,Wang, D. W. A note on analyzing nonlinear and nonstationary ocean wave data[J]. Applied Ocean Research,2003,25 (4):187-193
[99]Datig, M.,Schlurmann, T. Performance and limitations of the Hilbert-Huang transformation (HHT) with an application to irregular water waves[J]. Ocean Engineering,2004,31 (14-15):1783-1834
[100]Lai, R. J.,Huang, N. Investigation of vertical and horizontal momentum transfer in the Gulf of Mexico using empirical mode decomposition method[J]. Journal of Physical Oceanography,2005,35 (8):1383-1402
[101]Zhang, R. R., Ma, S.,Hartzell, S. Signatures of the seismic source in EMD-based characterization of the 1994 Northridge, California, earthquake recordings[J]. Bulletin of the Seismological Society of America,2003,93 (1): 501-518
[102]Liang, H., Lin, Z.,Mccallum, R. W. Artifact reduction in electrogastrogram based on empirical mode decomposition method[J]. Medical and Biological Engineering and Computing,2000,38 (1):35-41
[103]Yang, J. N., Lei, Y.,Pan, S. W.et al. System identification of linear structures based on Hilbert-Huang spectral analysis. Part 1:normal modes[J]. Earthquake Engineering & Structural Dynamics,2003,32 (9):1443-1467
[104]Yang, J. N., Lei, Y.,Pan, S. W.et al. System identification of linear structures based on Hilbert-Huang spectral analysis. Part 2:Complex modes[J]. Earthquake Engineering & Structural Dynamics,2003,32 (10):1533-1554
[105]Liu, B., Riemenschneider, S.,Xu, Y. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectram[J]. Mechanical Systems and Signal Processing,2006,20 (3):718-734
[106]Wu, T. Y.,Chung, Y. L. Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach[J]. Smart Materials and Structures,2009,18(9)
[107]Chen, L., Li, X.,Li, X. B.et al. Signal extraction using ensemble empirical mode decomposition and sparsity in pipeline magnetic flux leakage nondestructive evaluation[J]. Review of Scientific Instruments,2009,80 (2)
[108]Lei, Y.,Zuo, M. J. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs[J]. Measurement Science and Technology,2009,20 (12)
[109]Lei, Y., He, Z.,Zi, Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing,2009,23 (4):1327-1338
[110]徐红梅,郝志勇,杨陈等.基于EMD和HHT的内燃机噪声信号时频特性研究[J].内燃机工程,2008,29(6)：60-64，69
[111]樊新海,安钢,张传清等.基于排气噪声EMD的柴油机失火故障诊断[J].内燃机工程,2010,31(1)：78-81
[112]蔡艳平,李艾华,王涛等.基于EMD-Wigner-Ville(?)内燃机振动时频分析[J].振动工程学报,2010,23(4)：430-437
[113]Li, Y. J., Tse, P. W.,Yang, X.et al. EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine[J]. Mechanical Systems and Signal Processing,2010,24 (1):193-210
[114]Badaoui, M. E., Daniere, J.,Guillet, F.et al. Separation of combustion noise and piston-slap in diesel engine- Part I:Separation of combustion noise and piston-slap in diesel engine by cyclic Wiener filtering[J]. Mechanical Systems and Signal Processing,2005,19(6):1209-1217
[115]Serviere, C, Lacoume, J. L.,E1 Badaoui, M. Separation of combustion noise and piston-slap in diesel engine-Part Ⅱ:Separation of combustion noise and piston-slap using blind source separation methods[J]. Mechanical Systems and Signal Processing,2005,19(6):1218-1229
[116]Liu, X., Randall, R. B.,Antoni, J. Blind separation of internal combustion engine vibration signals by a deflation method[J]. Mechanical Systems and Signal Processing,2008,22 (5):1082-1091
[117]Cooley, J. W.,Tukey, J. W. An algorithm for machine calculation of complex Fourier series[J]. Mathematics of Computation,1965,19 (90):297-301
[118]卢文祥,杜润生.机械工程测试·信息·信号分析[M].武汉：华中理工大学出版社,1999
[119]褚福磊,彭志科,冯志鹏等.机械故障诊断中的现代信号处理方法[M].北京：科学出版社,2009
[120]郑君里,应启珩,著杨为理.信号与系统.上册[M].北京：高等教育出版社,2011
[121]Wikipedia.Gibbs phenomenon[OL]. http://en.wikipedia.org/wiki/Gibbs_phenomenon
[122]Harris, F. J. On the use of windows for harmonic analysis with the discrete Fourier transform[J]. Proceedings of the IEEE,1978,66 (1):51-83
[123]Mathworks.Hann (Hanning) window[OL]. http://www.mathworks.cn/help/toolbox/signal/ref/hann.html
[124]Mathworks.Gaussian window[OL]. http://www.mathworks.cn/help/toolbox/signal/ref/gausswin.html
[125]Cohen, L. Time-Frequency Analysis:Theory and Applications[M]. Englewood Cliffs, New Jersey:Prentice Hall,1995
[126]Grochenig. K. Foundations of Time-Frequency Analysis[M]. Boston:Birkhauser,2001
[127]吴正国,夏立,尹为民.现代信号处理技术—高阶谱、时频分析与小波变换[M].武汉：武汉大学出版社,2003
[128]皇甫堪,陈建文.现代数字信号处理[M].北京：电子工业出版社,2003
[129]胡广书.现代信号处理教程[M].北京：清华大学出版社,2004
[130]Sejdic, E., Djurovic, I.,Jiang, J. Time-frequency feature representation using energy concentration:An overview of recent advances[J]. Digital Signal Processing:A Review Journal,2009,19 (1):153-183
[131]Jones, D. L.,Parks, T. W. HIGH RESOLUTION DATA-ADAPTIVE TIME-FREQUENCY REPRESENTATION.[C].1987 ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings Dallas, TX, USA
[132]Jones, D. L.,Parks, T. W. A high resolution data-adaptive time-frequency representation[J]. IEEE Transactions On Acoustics, Speech, and Signal Processing,1990,38 (12):2127-2135
[133]Auger, F.,Flandrin, P. Why and how of time-frequency reassignmentfC].1994 Philadelphia, PA, USA
[134]Auger, F.,Flandrin, P. Improving the readability of time-frequency and time-scale representations by the reassignment method[J]. IEEE Transactions On Signal Processing,1995,43 (5):1068-1089
[135]Baraniuk. R. G.,Jones, D. L. Signal-dependent time-frequency representation:Fast algorithm for optimal kernel design[J]. IEEE Transactions On Signal Processing,1994,42 (1):134-146
[136]Gillespie, B.,Atlas, L. E. Optimizing time-frequency kernels for classification[J]. IEEE Trans. Signal Process, 2001,49 (3).485-496
[137]Jones, D. L.,Parks, T. W. A high resolution data-adaptive time-frequency representation[J]. IEEE Transactions On Acoustics, Speech, and Signal Processing,1990,38 (12):2127-2135
[138]何正嘉,訾艳阳,张西宁.现代信号处理及工程应用[M].西安：西安交通大学出版社,2007
[139]于德介,程军圣,杨宇.机械故障诊断的Hilbert-Huang变换方法[M].北京：科学出版社,2006
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