基于Hilbert谱图特征和野点检测的旋转机械故障智能诊断
|
摘要
在现代化生产中,机械设备的结构日趋复杂,一旦某个零部件出现故障,就容易引发链式反应,导致整个设备损坏,因此机械设备的故障诊断技术越来越受到重视。任何机械设备在动态下都会产生一定的振动,当设备发生异常或故障时,振动将会发生变化,一般表现为振幅加大,并表现出很强的非线性和非平稳性,这一特点使从振动信号中获取诊断信息,实现智能诊断变为可能。本文针对转子实验器的振动信号,进行了基于Hilbert谱图特征和野点检测的旋转机械故障智能诊断研究,主要工作如下:
一、介绍了旋转机械故障诊断的背景和意义,并综述了旋转机械故障诊断的研究现状和发展概况,特别是对基于时频分析的旋转机械故障诊断方法进行了简要说明,经过几种方法的对比,表明了Hilbert-Huang变换在旋转机械故障诊断中的优越性。
二、研究了Hilbert-Huang变换的基本理论,包括经验模态分解方法的原理、流程和特点,Hilbert变换原理及仿真信号的Hilbert谱计算,以及Hilbert-Huang变换中存在的问题和改进方法,通过对仿真信号的分析,表明了Hilbert-Huang方法对信号分解的有效性。
三、利用ZT-3型转子故障模拟实验台采集转子故障信号,然后对信号进行Hilbert-Huang变换处理,得到了故障信号的Hilbert谱,并使用PCA方法进行故障信号Hilbert谱的特征提取。
四、针对实际设备故障数据较少的现状,提出利用野点检测方法对Hilbert谱特征进行分类,并利用粒子群算法对野点检测模型参数进行优化,得到了模型的最优参数,并利用实验数据进行了分析和验证,表明了该方法的有效性。
In modern production, the structure of machinery and equipment is becoming more and more complex. Once a component is failure, it is easy to trigger chain reaction, resulting in great damage to the equipment. So fault diagnosis technology of mechanical equipment is more and more important. Any dynamic mechanical device will have a certain vibration. When an exception occurs, the vibration will change as amplitude increases usually, and will show a strong non-linear and non-stationary nature of this characteristic. So that diagnostic information should be obtain from the vibration signals, and intelligent fault diagnosis could realize. In this paper, the intelligent fault diagnosis of rotating machinery based on the Hilbert spectrum is studied through vibration signals of the rotor experimental instrument. the main jobs are as follows:
Firstly, the paper introduces the background and the significance of rotating machinery fault diagnosis, and makes an overview of rotating machinery fault diagnosis methods. In particular, time-frequency analysis methods of rotating machinery fault diagnosis are described briefly. After contrast of several methods, the Hilbert-Huang Transform in rotating machinery fault diagnosis shows superiority.
Secondly, the paper introduces the basic theory, processes and characteristics of the Hilbert-Huang transform, including the empirical mode decomposition (EMD) principles, Hilbert transform theory, Hilbert spectrum calculation of simulation signal, as well as the existing problems and improve the method of the Hilbert-Huang transform. HHT method shows the effectiveness of signal decomposition, through the analysis of the simulation signals.
Thirdly, using ZT-3 multiple-function experimental instrument acquires rotor fault signal, and the Hilbert spectrum of fault signals has been obtained through Hilbert-Huang transform. Then feature of fault signal is extracted from Hilbert spectrum using the PCA method.
Finally, for there is limited real fault signal of instrument, so the Hilbert spectrum features are classified using the novelty detection, and parameters of novelty detection are optimized using particle swarm optimization algorithm and the adaptive optimal parameters are obtained. The experiment using experimental data is carried out, and the results show the effectiveness of the method.
一、介绍了旋转机械故障诊断的背景和意义,并综述了旋转机械故障诊断的研究现状和发展概况,特别是对基于时频分析的旋转机械故障诊断方法进行了简要说明,经过几种方法的对比,表明了Hilbert-Huang变换在旋转机械故障诊断中的优越性。
二、研究了Hilbert-Huang变换的基本理论,包括经验模态分解方法的原理、流程和特点,Hilbert变换原理及仿真信号的Hilbert谱计算,以及Hilbert-Huang变换中存在的问题和改进方法,通过对仿真信号的分析,表明了Hilbert-Huang方法对信号分解的有效性。
三、利用ZT-3型转子故障模拟实验台采集转子故障信号,然后对信号进行Hilbert-Huang变换处理,得到了故障信号的Hilbert谱,并使用PCA方法进行故障信号Hilbert谱的特征提取。
四、针对实际设备故障数据较少的现状,提出利用野点检测方法对Hilbert谱特征进行分类,并利用粒子群算法对野点检测模型参数进行优化,得到了模型的最优参数,并利用实验数据进行了分析和验证,表明了该方法的有效性。
In modern production, the structure of machinery and equipment is becoming more and more complex. Once a component is failure, it is easy to trigger chain reaction, resulting in great damage to the equipment. So fault diagnosis technology of mechanical equipment is more and more important. Any dynamic mechanical device will have a certain vibration. When an exception occurs, the vibration will change as amplitude increases usually, and will show a strong non-linear and non-stationary nature of this characteristic. So that diagnostic information should be obtain from the vibration signals, and intelligent fault diagnosis could realize. In this paper, the intelligent fault diagnosis of rotating machinery based on the Hilbert spectrum is studied through vibration signals of the rotor experimental instrument. the main jobs are as follows:
Firstly, the paper introduces the background and the significance of rotating machinery fault diagnosis, and makes an overview of rotating machinery fault diagnosis methods. In particular, time-frequency analysis methods of rotating machinery fault diagnosis are described briefly. After contrast of several methods, the Hilbert-Huang Transform in rotating machinery fault diagnosis shows superiority.
Secondly, the paper introduces the basic theory, processes and characteristics of the Hilbert-Huang transform, including the empirical mode decomposition (EMD) principles, Hilbert transform theory, Hilbert spectrum calculation of simulation signal, as well as the existing problems and improve the method of the Hilbert-Huang transform. HHT method shows the effectiveness of signal decomposition, through the analysis of the simulation signals.
Thirdly, using ZT-3 multiple-function experimental instrument acquires rotor fault signal, and the Hilbert spectrum of fault signals has been obtained through Hilbert-Huang transform. Then feature of fault signal is extracted from Hilbert spectrum using the PCA method.
Finally, for there is limited real fault signal of instrument, so the Hilbert spectrum features are classified using the novelty detection, and parameters of novelty detection are optimized using particle swarm optimization algorithm and the adaptive optimal parameters are obtained. The experiment using experimental data is carried out, and the results show the effectiveness of the method.
引文
[1]陈果.航空器检测与诊断技术导论[M].北京:中国民航出版社,2007.12
[2]张华君.基于HHT的机电系统的滚动轴承故障诊断[D].太原理工大学,2006.5
[3]徐章遂,房立清,王希武,左宪章.故障信息诊断原理及应用[M].北京:国防工业出版社,2000
[4]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社,1998
[5] Gabor D. Theory of Communication[J]. Journal of the Institute of Electrical Engineers, 1946, 93(3):429-457
[6]何正嘉,赵纪元,孟庆丰等.时频分析在大型电铲提升系统中的应用[J].中国机械工程, 1994,5(6):12-14.
[7]张绪省,孙金玮,赵新民.用时频分析方法分析非平稳信号[J].宇航计测技术, 1995, 15(5).
[8] Kim YH. Fault detection in a ball bearing system using a moving window. Mechanical Systems and Signal Processing. 1991, 5(6):461~473.
[9] Stazewski WJ. Local tooth fault detection in gearboxes using a moving window procedure[J]. Mechanical Systems and Signal Processing. 1997, 11(3):331~350.
[10] Wigner E P.On the quantum correction for thermodynamic equilibrium[J]. Phys.Rew, 1932, 40:748-759
[11] VILLE J.Theorie et application de la notion de signal analytique[J].Caables et transmission, 1948,2A:61-94
[12] Classen T C M, Mecklenbrauker W F G. The Wigner distribution-a tool for time frequency, part I: Continuous time signal[J]. Philips Journal of Research, 1980,35(3):217-250
[13] Classen T C M,Mecklenbrauker W F G. The Wigner distribution-a tool for time frequency, part II: Discrete time signals.[J]. Philips Journal of Research, 1980,35(4/5):276-300
[14] Classen T C M,Mecklenbrauker W F G. The Wigner distribution-a tool for time frequency, part III: Relation with other time-frequency transforms.[J]. Philips Journal of Research, 1980,35(6):372-389
[15] Meng Q, Qu L.Rotating machinery fault diagnosis using Wigner distribution[J]. Mechanical Systems and Signal Processing,1991,5(5):155-166
[16] Richard C. Time-frequency-based detection uding discrete-time discrete-frequency Wigner distribution[J]. IEEE Transaction on Signal Processing,2002,50(9):2170-2176
[17] Rosriguez Fonollosa J,Nilias C L. Transient signal detection using the Wigner bispectrum[C].Conference Record of the Twenty-fifth Asilomar Conference on Signals, Systems and Computers,1991,2:1087-1092
[18] Griffin C. A comparison study on the Wigner and Choi-Williams distributions for detection[J]. International Conference on Acoustucs, Speech, and Signal Processing,1991,2:14-17
[19] O’Shea P, Boashash B. Instantaneous frequency estimation using the cross Wigner-Vill distribution with application to nonstationary transient detection[J]. International Conference on Acoustics, Speech, and Signal Processing,1990,5:3-6
[20] Zou J,Chen J. A comparative study on tine-frequency feature of cracked rotor by Wigner-Ville distribution and wavelet transform[J]. Journal of Sound and Vibration,2004,276(1-2):1-11
[21] Wang W J,McFadden P D. Early detection of gear failure by vibration analysis-I Calculation of the time-frequency distribution[J]. Mechanical Systems and Signal Processing,1993,7(3):193-203
[22] Stazewski W J, Worden K, Tomlinson G R. Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition[J]. Mechanical Systems and Signal Processing,1997,11(5):673-692
[23]耿遵敏,郑晓忠,种方. Wigner分布的改进及其在非平稳振动信号分析及诊断中的应用[J].信号处理,1995,11(2):116-123
[24]孟庆丰,屈梁生.Wigner分布及其在机械故障诊断中的应用[J].信号处理,1990,6(3):155-162
[25] Stazewski W J, Tomlinson G R. Application of the wavelet transform to fault detection in a spur gear[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING.1994,8(3):289-307
[26] Zhongming Ye, Bin Wu, Sadeghian A. Current signature analysis of induction motor mechanical faults by wavelet packet decomposition[J] IEEE Transaction on Industrial Electronics, 2003, 50(6):1217-1228
[27] Lin Jing. Feature extraction of machine sound using wavelet and its application in fault diagnosis[J].NDT&E International,2001,34(1):25-30
[28] Zheng H, Li Z, Chen X. Gear fault diagnosis based on continuous wavelet transform[J]. Mechanical Systems and Signal Processing,2002,16(2-3):447-457
[29] Tsse Peter W, Yang Wen-xian, Tam H Y. Machine fault diagnosis through an effective exact wavelet analysis[J]. Journal of Sound and Vibration, 2004,277(4-5):1005-1024
[30] Staszewski W J. Wavelet based compression and feature selection for vibration analysis[J]. Journal of Sound and Vibration,1998,211(5):736-760
[31] Pabhakar S, Mohanty A R, Sekhar A S.Application of discrete wavelet transform for detection og ball bearing race faults[J]. Tribology International,2002,35(12):793-800
[32] Nikolaou N G, Antoniadis I A. Rolling element bearing faulr diagnosis using wavelet packets[J]. NDT&E International, 2002,35(3):197-205
[33] Li C James,Ma Jun. Wavelet decomposition of vibrations for detection of bearing localized defects[J]. NDT&E International,1997,30(3):143-149
[34] Geng Z,Qu L. Vibrational diagnosis of machine parts using the wavelet packet technique[J]. The British Journal of Non-Destructive Testing,1994,36(1):11-15
[35] Mori K,Kasashima N,Yinhioka T et al. Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals[J].Wear,1996,195(1-2):163-168
[36] Coifman R R, Wickerhauser M V. Entropy-based algorithms for best basis selection[J]. IEEE Transaction on Information theory, 1992,38(2):713-718
[37] Lin Jing, Qu Liangsheng. Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis[J]. Journal of Sound and Vibration, 2000,234(1):135-148
[38] Lin J, Zou M J. Gearbox fault diagnosis using adaptive wavelet filter[J]. Mechanical Systems and Signal Processing,2003,17(6):1259-1269
[39] Liu B, Ling S F. On the selection of informative wavelets for machinery diagnosis[J]. Mechanical Systems and Signal Procesing,1999,13(1):145-162
[40] Schukin E L, Zanaraev R U, Schclin L I. The optimization of wavelet transform for impulse analysis in vibration signals[J]. Mechanical System and Signal Processing,2004,18(6): 1315-1333
[41]别锋锋,王奉涛,吕凤霞.基于局域波时频图像处理技术的压缩机故障诊断[J].农业机械学报.2007,9:159-162
[42] Gravier B.M.,N.N.J.,Pelstring J.A.,An assessment of the application of the Hilbert spectrum to the fatigue analysis of marine risers[J],2001.2
[43]钟佑明,秦树人,汤宝平.一种振动信号新变换法的研究[J].振动工程学报.2002.(2):233-237
[44]于德介,程军圣.EMD方法在齿轮故障诊断中的应用[J].湖南大学学报(自然科学版).2002(6):48-51
[45]胡劲松.面向旋转机械故障诊断的经验模态分解时频分析方法及实验研究[D].浙江大学,2003
[46]谭善文,秦树人.Hilbert-Huang变换的滤波特性及其应用[J].重庆大学学报,2004,27(2):10-13
[47] Nunes J C, Bouaoune Y, Delechelle E.Image Analysis by Bidimensional Empirical Mode Decomposition[J]. Image and Vision Computing, 2003,21(12):1019-1026.
[48] Linderhed A. 2D Empirical Mode Decompositions in the Spirit of Image Compression[C] //Wavelet and Independent Component Analysis Applications IX, Proceedings of the SPIE, 2002,4738:1-8
[49]刘忠轩,彭思龙.方向EMD分解与其在纹理分割中的应用[J].中国科学(E辑),2005,35 (2):113-123.
[50]刘伟,张宏,周洁,童勤业.使用希尔伯特-黄变换的医学图像检索[J].传感技术学报.2007, 20(5):1077-1081
[51] 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 Royal Society London, 1998, 454(A): 903-995
[52] Huang N E, Shen Z, Long S R.. A new view of nonlinear water wave: the Hilbert spectrum[J]. Ann Rev Fluid Mech,1999,31:417-457
[53] G. Rilling, P. Flandrin, P. Goncalves, On empirical mode decomposition and its algorithms[J], in: Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP-03) Grado (I), 2003.
[54]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): 257-263
[55] Zhao Jinping, Huang Daji. Mirror extending and circular spline function for empirical mode decomposition method[J]. Journal of Zhejiang University (Science), 2001, 2(3):274-252
[56]黄大吉,赵进平,苏纪兰.希尔伯特-黄变换的端点延拓[J].海洋学报, 2003, 25(1):1-11
[57]陈忠,郑时雄.EMD信号分析方法边缘效应的分析[J].数据采集与处理, 2003,18(1):114-118
[58]黎洪生,吴小娟,葛源. EMD信号分析方法端点问题的处理[J].电力自动化设备,2005, 25(9): 47-49
[59]张郁山,梁建文,胡聿贤.应用自回归模型处理EMD方法中的边界问题[J].自然科学进展,2003, 13(10): 1054-1059
[60]贾民平,林娟,许云飞等.基于时序分析的经验模式分解法及其应用[J].机械工程学报, 2004, 40(9): 54-57
[61] Jackson J E. A user's guide to principal components[C]. NewYork: John Wiley, 1991.
[62] Wang H Q, Song Z H, Wang H. Statistical process monitoring using improved PCA with optimized sensor locations[J]. Journal of Process Control, 2002, 12(6): 735-744.
[63]刘涛,杨风暴.主成分分析在图像压缩中的应用[J].中北大学, 2008.
[64] Kirby M and Sirovich L. Application of the K-L procedure for the characterization of human faces[J]. IEEE Transactions on Pattern Anal Machine Intel, 1990, 12(1):103-108.
[65] Turk M, Pentland A. Face processing: Models for recognition[C]. Proceedings of the SPIE Conference on Intelligent Robots and Computer Vision, 1989, 192(1):22-32.
[66] Turk M and Pentland A. Eigenfaces for recognition[J]. Cognitive Neuroscience, 1991,3(1):71-86.
[67] Richard O.Duda, Peter E.Hart, David G.Stork.模式分类[M].机械工程出版社.2003
[68]方浩,朱海军,吴华意.主成分分析用于图像压缩预处理的比较研究[J],2005
[69] Malhi A, Gao R. PCA-based feature selection scheme for machine detect classification[J]. IEEE Transactions on Instrumentation and Measurement, 2004, 53(6): 1517-1525.
[70] Yan A M, Kerschen G. Structural damage diagnosis under varying environmental conditions[J]. Mechanical systems and signal processing, 2005, 19(4): 847-864.
[71]杨晓楠,姜绍飞,倪一清等.基于特征向量简化的复杂土木结构损伤检测[J].沈阳建筑工程学院学报(自然科学版),2003,19(2): 81-84.
[72]边肇祺,张学工.模式识别[M].北京:清华大学出版社, 2002.
[73]肖建华.智能模式识别方法[M].广州.华南理工大学出版社,2006
[74] Manevitz L, Yousef M. One-class document classification via Neural Network[J]. Neurocomputing,2007,70:1466-1481
[75] Seo K K. An application of one-class support vector machines in content-based image retrieval[J].Expert Systems with Applications,2007,33:491-498
[76]肖健华.人脸确认的动态支持向量数据描述方法[J].中国图像图形学报,2006,11(l):159-161.
[77]孙德山,吴今培.一类支持向量机载车辆识别中的应用[J].交通运输系统工程与信息,2003,3(4):34-37
[78]范业范,罗俊,任民川.基于一类辨识的航空发动机故障诊断[J].燃气涡轮试验与研究,2006,19(2):30-32
[79]陈果.基于遗传算法的支持向量机分类器模型参数优化[J].机械科学与技术, 2007, 26(3): 347-350
[80] E. Bonabeau, M. Dorigo, G. Theraulaz. Inspiration for optimization from social insect behavior[J]. Nature, 2000, 406(6): 39-442.
[81] E. Bonabeau, M.Dorigo, G. Theraulaz. Swarm intelligence from natural to artificial systems[M]. New York: Oxford Univ. Press, 1999.
[82] J.Kennedy, R.C.Eberhart. Particle swarm optimization[A]. Proceeding IEEE International Conference on Neura lNetworks[M]. 1995: 1942-1948.
[83]杨维,李岐强.粒子群优化算法综述[J].中国工程科学, 2004, 6(5): 87-92.
[84] R.C.E. Scrhart,Y. Sh.i Evolving Artificial Neural Networks[A]. Proceeding of International Conference on Neural Networks and Brain[M]. Piscataway NJ IEEE Press, 1998: 5-13.
[85] Shi Y H, Eberhart R C. Fuzzy adaptive particle swarm optimization [A]. Proceedings of the IEEECongress on Evolutionary Computation [C]. Piscataway, USA:IEEE Service Center, 2001. 101-106.
[86]吕振肃,候志荣.自适应变异的粒子群优化算法[J].电子学报, 2004,32(3):416-420.
[87]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004,41(6):4-6.
[88] Shi Y H, Eberhart R C. A modified particle swarm optimizer [A]. Proceedings of the IEEE Congress on Evolutionary Computation[C]. Piscataway, USA:IEEE Service Center, 1998. 69-73.
[89] Shi Y, Eberhart R C. Particle swarm optimization: developments applications and resources [A]. Proceedings of the IEEE Congress on Evolutionary Computation[C]. Piscataway, USA: IEEE Service Center,2001:81-86
[90]张选平,杜玉平,秦国强,覃征.一种动态改变惯性权的自适应粒子群算法[J].西安交通大学学报. 2005.10,39(10):1039-1042
[2]张华君.基于HHT的机电系统的滚动轴承故障诊断[D].太原理工大学,2006.5
[3]徐章遂,房立清,王希武,左宪章.故障信息诊断原理及应用[M].北京:国防工业出版社,2000
[4]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社,1998
[5] Gabor D. Theory of Communication[J]. Journal of the Institute of Electrical Engineers, 1946, 93(3):429-457
[6]何正嘉,赵纪元,孟庆丰等.时频分析在大型电铲提升系统中的应用[J].中国机械工程, 1994,5(6):12-14.
[7]张绪省,孙金玮,赵新民.用时频分析方法分析非平稳信号[J].宇航计测技术, 1995, 15(5).
[8] Kim YH. Fault detection in a ball bearing system using a moving window. Mechanical Systems and Signal Processing. 1991, 5(6):461~473.
[9] Stazewski WJ. Local tooth fault detection in gearboxes using a moving window procedure[J]. Mechanical Systems and Signal Processing. 1997, 11(3):331~350.
[10] Wigner E P.On the quantum correction for thermodynamic equilibrium[J]. Phys.Rew, 1932, 40:748-759
[11] VILLE J.Theorie et application de la notion de signal analytique[J].Caables et transmission, 1948,2A:61-94
[12] Classen T C M, Mecklenbrauker W F G. The Wigner distribution-a tool for time frequency, part I: Continuous time signal[J]. Philips Journal of Research, 1980,35(3):217-250
[13] Classen T C M,Mecklenbrauker W F G. The Wigner distribution-a tool for time frequency, part II: Discrete time signals.[J]. Philips Journal of Research, 1980,35(4/5):276-300
[14] Classen T C M,Mecklenbrauker W F G. The Wigner distribution-a tool for time frequency, part III: Relation with other time-frequency transforms.[J]. Philips Journal of Research, 1980,35(6):372-389
[15] Meng Q, Qu L.Rotating machinery fault diagnosis using Wigner distribution[J]. Mechanical Systems and Signal Processing,1991,5(5):155-166
[16] Richard C. Time-frequency-based detection uding discrete-time discrete-frequency Wigner distribution[J]. IEEE Transaction on Signal Processing,2002,50(9):2170-2176
[17] Rosriguez Fonollosa J,Nilias C L. Transient signal detection using the Wigner bispectrum[C].Conference Record of the Twenty-fifth Asilomar Conference on Signals, Systems and Computers,1991,2:1087-1092
[18] Griffin C. A comparison study on the Wigner and Choi-Williams distributions for detection[J]. International Conference on Acoustucs, Speech, and Signal Processing,1991,2:14-17
[19] O’Shea P, Boashash B. Instantaneous frequency estimation using the cross Wigner-Vill distribution with application to nonstationary transient detection[J]. International Conference on Acoustics, Speech, and Signal Processing,1990,5:3-6
[20] Zou J,Chen J. A comparative study on tine-frequency feature of cracked rotor by Wigner-Ville distribution and wavelet transform[J]. Journal of Sound and Vibration,2004,276(1-2):1-11
[21] Wang W J,McFadden P D. Early detection of gear failure by vibration analysis-I Calculation of the time-frequency distribution[J]. Mechanical Systems and Signal Processing,1993,7(3):193-203
[22] Stazewski W J, Worden K, Tomlinson G R. Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition[J]. Mechanical Systems and Signal Processing,1997,11(5):673-692
[23]耿遵敏,郑晓忠,种方. Wigner分布的改进及其在非平稳振动信号分析及诊断中的应用[J].信号处理,1995,11(2):116-123
[24]孟庆丰,屈梁生.Wigner分布及其在机械故障诊断中的应用[J].信号处理,1990,6(3):155-162
[25] Stazewski W J, Tomlinson G R. Application of the wavelet transform to fault detection in a spur gear[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING.1994,8(3):289-307
[26] Zhongming Ye, Bin Wu, Sadeghian A. Current signature analysis of induction motor mechanical faults by wavelet packet decomposition[J] IEEE Transaction on Industrial Electronics, 2003, 50(6):1217-1228
[27] Lin Jing. Feature extraction of machine sound using wavelet and its application in fault diagnosis[J].NDT&E International,2001,34(1):25-30
[28] Zheng H, Li Z, Chen X. Gear fault diagnosis based on continuous wavelet transform[J]. Mechanical Systems and Signal Processing,2002,16(2-3):447-457
[29] Tsse Peter W, Yang Wen-xian, Tam H Y. Machine fault diagnosis through an effective exact wavelet analysis[J]. Journal of Sound and Vibration, 2004,277(4-5):1005-1024
[30] Staszewski W J. Wavelet based compression and feature selection for vibration analysis[J]. Journal of Sound and Vibration,1998,211(5):736-760
[31] Pabhakar S, Mohanty A R, Sekhar A S.Application of discrete wavelet transform for detection og ball bearing race faults[J]. Tribology International,2002,35(12):793-800
[32] Nikolaou N G, Antoniadis I A. Rolling element bearing faulr diagnosis using wavelet packets[J]. NDT&E International, 2002,35(3):197-205
[33] Li C James,Ma Jun. Wavelet decomposition of vibrations for detection of bearing localized defects[J]. NDT&E International,1997,30(3):143-149
[34] Geng Z,Qu L. Vibrational diagnosis of machine parts using the wavelet packet technique[J]. The British Journal of Non-Destructive Testing,1994,36(1):11-15
[35] Mori K,Kasashima N,Yinhioka T et al. Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals[J].Wear,1996,195(1-2):163-168
[36] Coifman R R, Wickerhauser M V. Entropy-based algorithms for best basis selection[J]. IEEE Transaction on Information theory, 1992,38(2):713-718
[37] Lin Jing, Qu Liangsheng. Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis[J]. Journal of Sound and Vibration, 2000,234(1):135-148
[38] Lin J, Zou M J. Gearbox fault diagnosis using adaptive wavelet filter[J]. Mechanical Systems and Signal Processing,2003,17(6):1259-1269
[39] Liu B, Ling S F. On the selection of informative wavelets for machinery diagnosis[J]. Mechanical Systems and Signal Procesing,1999,13(1):145-162
[40] Schukin E L, Zanaraev R U, Schclin L I. The optimization of wavelet transform for impulse analysis in vibration signals[J]. Mechanical System and Signal Processing,2004,18(6): 1315-1333
[41]别锋锋,王奉涛,吕凤霞.基于局域波时频图像处理技术的压缩机故障诊断[J].农业机械学报.2007,9:159-162
[42] Gravier B.M.,N.N.J.,Pelstring J.A.,An assessment of the application of the Hilbert spectrum to the fatigue analysis of marine risers[J],2001.2
[43]钟佑明,秦树人,汤宝平.一种振动信号新变换法的研究[J].振动工程学报.2002.(2):233-237
[44]于德介,程军圣.EMD方法在齿轮故障诊断中的应用[J].湖南大学学报(自然科学版).2002(6):48-51
[45]胡劲松.面向旋转机械故障诊断的经验模态分解时频分析方法及实验研究[D].浙江大学,2003
[46]谭善文,秦树人.Hilbert-Huang变换的滤波特性及其应用[J].重庆大学学报,2004,27(2):10-13
[47] Nunes J C, Bouaoune Y, Delechelle E.Image Analysis by Bidimensional Empirical Mode Decomposition[J]. Image and Vision Computing, 2003,21(12):1019-1026.
[48] Linderhed A. 2D Empirical Mode Decompositions in the Spirit of Image Compression[C] //Wavelet and Independent Component Analysis Applications IX, Proceedings of the SPIE, 2002,4738:1-8
[49]刘忠轩,彭思龙.方向EMD分解与其在纹理分割中的应用[J].中国科学(E辑),2005,35 (2):113-123.
[50]刘伟,张宏,周洁,童勤业.使用希尔伯特-黄变换的医学图像检索[J].传感技术学报.2007, 20(5):1077-1081
[51] 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 Royal Society London, 1998, 454(A): 903-995
[52] Huang N E, Shen Z, Long S R.. A new view of nonlinear water wave: the Hilbert spectrum[J]. Ann Rev Fluid Mech,1999,31:417-457
[53] G. Rilling, P. Flandrin, P. Goncalves, On empirical mode decomposition and its algorithms[J], in: Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP-03) Grado (I), 2003.
[54]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): 257-263
[55] Zhao Jinping, Huang Daji. Mirror extending and circular spline function for empirical mode decomposition method[J]. Journal of Zhejiang University (Science), 2001, 2(3):274-252
[56]黄大吉,赵进平,苏纪兰.希尔伯特-黄变换的端点延拓[J].海洋学报, 2003, 25(1):1-11
[57]陈忠,郑时雄.EMD信号分析方法边缘效应的分析[J].数据采集与处理, 2003,18(1):114-118
[58]黎洪生,吴小娟,葛源. EMD信号分析方法端点问题的处理[J].电力自动化设备,2005, 25(9): 47-49
[59]张郁山,梁建文,胡聿贤.应用自回归模型处理EMD方法中的边界问题[J].自然科学进展,2003, 13(10): 1054-1059
[60]贾民平,林娟,许云飞等.基于时序分析的经验模式分解法及其应用[J].机械工程学报, 2004, 40(9): 54-57
[61] Jackson J E. A user's guide to principal components[C]. NewYork: John Wiley, 1991.
[62] Wang H Q, Song Z H, Wang H. Statistical process monitoring using improved PCA with optimized sensor locations[J]. Journal of Process Control, 2002, 12(6): 735-744.
[63]刘涛,杨风暴.主成分分析在图像压缩中的应用[J].中北大学, 2008.
[64] Kirby M and Sirovich L. Application of the K-L procedure for the characterization of human faces[J]. IEEE Transactions on Pattern Anal Machine Intel, 1990, 12(1):103-108.
[65] Turk M, Pentland A. Face processing: Models for recognition[C]. Proceedings of the SPIE Conference on Intelligent Robots and Computer Vision, 1989, 192(1):22-32.
[66] Turk M and Pentland A. Eigenfaces for recognition[J]. Cognitive Neuroscience, 1991,3(1):71-86.
[67] Richard O.Duda, Peter E.Hart, David G.Stork.模式分类[M].机械工程出版社.2003
[68]方浩,朱海军,吴华意.主成分分析用于图像压缩预处理的比较研究[J],2005
[69] Malhi A, Gao R. PCA-based feature selection scheme for machine detect classification[J]. IEEE Transactions on Instrumentation and Measurement, 2004, 53(6): 1517-1525.
[70] Yan A M, Kerschen G. Structural damage diagnosis under varying environmental conditions[J]. Mechanical systems and signal processing, 2005, 19(4): 847-864.
[71]杨晓楠,姜绍飞,倪一清等.基于特征向量简化的复杂土木结构损伤检测[J].沈阳建筑工程学院学报(自然科学版),2003,19(2): 81-84.
[72]边肇祺,张学工.模式识别[M].北京:清华大学出版社, 2002.
[73]肖建华.智能模式识别方法[M].广州.华南理工大学出版社,2006
[74] Manevitz L, Yousef M. One-class document classification via Neural Network[J]. Neurocomputing,2007,70:1466-1481
[75] Seo K K. An application of one-class support vector machines in content-based image retrieval[J].Expert Systems with Applications,2007,33:491-498
[76]肖健华.人脸确认的动态支持向量数据描述方法[J].中国图像图形学报,2006,11(l):159-161.
[77]孙德山,吴今培.一类支持向量机载车辆识别中的应用[J].交通运输系统工程与信息,2003,3(4):34-37
[78]范业范,罗俊,任民川.基于一类辨识的航空发动机故障诊断[J].燃气涡轮试验与研究,2006,19(2):30-32
[79]陈果.基于遗传算法的支持向量机分类器模型参数优化[J].机械科学与技术, 2007, 26(3): 347-350
[80] E. Bonabeau, M. Dorigo, G. Theraulaz. Inspiration for optimization from social insect behavior[J]. Nature, 2000, 406(6): 39-442.
[81] E. Bonabeau, M.Dorigo, G. Theraulaz. Swarm intelligence from natural to artificial systems[M]. New York: Oxford Univ. Press, 1999.
[82] J.Kennedy, R.C.Eberhart. Particle swarm optimization[A]. Proceeding IEEE International Conference on Neura lNetworks[M]. 1995: 1942-1948.
[83]杨维,李岐强.粒子群优化算法综述[J].中国工程科学, 2004, 6(5): 87-92.
[84] R.C.E. Scrhart,Y. Sh.i Evolving Artificial Neural Networks[A]. Proceeding of International Conference on Neural Networks and Brain[M]. Piscataway NJ IEEE Press, 1998: 5-13.
[85] Shi Y H, Eberhart R C. Fuzzy adaptive particle swarm optimization [A]. Proceedings of the IEEECongress on Evolutionary Computation [C]. Piscataway, USA:IEEE Service Center, 2001. 101-106.
[86]吕振肃,候志荣.自适应变异的粒子群优化算法[J].电子学报, 2004,32(3):416-420.
[87]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004,41(6):4-6.
[88] Shi Y H, Eberhart R C. A modified particle swarm optimizer [A]. Proceedings of the IEEE Congress on Evolutionary Computation[C]. Piscataway, USA:IEEE Service Center, 1998. 69-73.
[89] Shi Y, Eberhart R C. Particle swarm optimization: developments applications and resources [A]. Proceedings of the IEEE Congress on Evolutionary Computation[C]. Piscataway, USA: IEEE Service Center,2001:81-86
[90]张选平,杜玉平,秦国强,覃征.一种动态改变惯性权的自适应粒子群算法[J].西安交通大学学报. 2005.10,39(10):1039-1042
© 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 |