旋转机械易损关键零部件故障诊断方法研究
|
摘要
在各类机械设备中,由于旋转机械的特殊地位,其故障理论和诊断方法历来是各国专家和学者的研究热点之一。旋转机械中转子及其相关组件是设备产生故障的主要来源,其振动信号的表现形式大都是非平稳的、动态的和随机的。特别是当旋转设备工作在变转速时,故障信号变得越加复杂,特征提取和故障识别十分困难。本文将信号处理技术和模式识别技术中的一些新方法引入到旋转机械故障诊断中来,对故障易发的滚动轴承、齿轮及转子故障诊断方法进行了一些有益的研究和探索。
(1)针对滚动轴承恒转速条件下故障引发的共振信号信噪比较高但非平稳的特点,利用EMD将其分解并提取包含故障信息的平稳信号,经包络解调后构建了反映轴承故障状态的特征矢量。研究了DHMM多样本训练方法并对轴承状态进行了识别。试验结果表明所提出的方法对单一故障和同时存在多种故障的轴承诊断都取得了良好的识别效果。
(2)指出了滚动轴承故障引发的轴承共振信号并非简单的偏置调幅信号,利用所建的故障模型试验了CAF的解调效果。通过研究、对比和分析,证明了CAF的形貌特征能够反映轴承的故障状态,结合CHMM对动态信号识别能力,提出了一种滚动轴承故障识别方法,并通过试验验证了方法的有效性。
(3)对于滚动轴承必须经历的启动过程,由于转速的变化,故障特征频率不复存在。利用小波分析方法提取故障共振信号,采用瞬时频率估计、阶比跟踪技术对轴承故障振动信号进行分析,给出了故障特征阶比系数的概念和算法,试验了算法在轴承故障诊断中的可行性。
(4)变转速更贴近旋转机械应用中的实际情况,本文指出了变转速滚动轴承故障信号的双变频调制特性并建立了故障模型。给出了OCAF的计算公式,研究了其对双变频信号的解调性能。利用特定转速下的故障动态信息构建特征向量,提出了基于OCAF-CHMM的滚动轴承故障诊断方法,并通过试验对该方法进行了检验。
(5)通过DCS获取齿轮特征信息,并采用DHMM模型对齿轮进行了故障识别。试验表明所提出的方法能够很好地识别断齿、点蚀等齿轮状态。这种方法结合了DCS解调信噪比高、表达相对简单和DHMM运算量相对较小的特点,有利于提高诊断速度。
(6)充分利用HMM和SVM在序列行为分类和小样本方面的优势,提出了基于SVM-HMM模型的旋转机械故障诊断方法。该模型在转子启动过程故障诊断试验中,与以往提出的基于SOM-HMM模型的诊断方法相比,减少了量化过程的信息损失及模型训练所需样本数量,提高了故障识别率并减少了模型训练时间。将该模型用于滚动轴承损伤类故障诊断中,也获得了较满意的效果,验证了SVM-HMM模型在滚动轴承故障诊断中的可行性。
Due to the special status of rotating machine in various types of mechanical equipment. Fault theories and diagnosis methods has always been a research focus of experts and scholars home and broad. In rotating machinery equipment, the rotor and its related components are the major fault sources, and the most of its vibration signal appears unstable, dynamic and random, and the fault signal becomes more complex when the rotating equipment works in the variable speed. So the signal extraction and fault recognition method has always been a complex and interesting subject. With the continual emergence of new technologies and methods of a variety of signal processing and pattern recognition, new vitality has been injected in solving these problems. This paper aims to introduce some new methods of signal processing and pattern recognition technology in fault diagnosis of rotating machine, and some useful researches and explorations of fault diagnosis methods for fault-prone bearings, gears and rotor.
(1) For the high noise ratio but unstable characteristics of resonance signal caused by rolling bearing fault at the constant speed conditions, EMD method is employed so that the resonance signal can be decomposed and fault information that contains smooth signal can be extracted, after demodulation by envelope the characteristics in reflecting the bearing fault state vector is constructed. Diverse training methods of DHMM are researched and the status of the bearing is also identified. Experimental result shows that the proposed method is capable in carrying out satisfactory result in testing both single and multiple faults.
(2) Pointing out that the resonance signal of the bearing caused by the failure of the antifriction bearing is not a simple bias AM signal, utilizing the fault models to test the outcome after demodulated by CAF. It is proved that the morphology of CAF could report the failure status of the bearing based on research, comparison and analysis. Combining the ability of CHMM to recognize the dynamic signal, a method of antifriction bearing fault detection is proposed, and the validity of the method has been verified.
(3) The fault characteristic frequency disappears during the starting procedure of the antifriction bearing due to the change of the rotate speed. The fault resonance signal is figured out by the method of wavelet analysis, the vibration signals of the antifriction bearing are analyzed by the method of estimation of instantaneous frequency and ratio tracking, so that the concept and algorithm of the fault characteristics and ratio coefficient are raised, and the feasibility of the algorithm has been verified.
(4) The change of the rotate speed is closer to real situation of the application of the rotating machinery. This article indicates the dual frequency conversion characteristics of variable speed antifriction bearing fault signal. Computational formula of OCAF is proposed, and research on the demodulation capability on dual-frequency signal has been done. A method of fault diagnosis on the rolling bearing is carried out based on OCAF-CHMM and the characteristic rector of the.multidate information under the specific rotate speed, and the method has been verified to be effective.
(5) The feature information of the gear is obtained via DCS, and the failure of the gear has been identified via DHMM model. The experiment indicates that the proposed method can recognize the broken tooth, corrosive pitting and sort of status correctly. The specific method is the combination of the three features, such as the high demodulation SNR of DCS, simplicity of expression and the reduced quantity of computation, which can improve the diagnostic rate.
(6) Fully taking the advantage of HMM model and SVM model on the sequence behavior classification and small sample, a rotating machine faults diagnosis method is proposed based on SVM-HMM model. Comparing with the SOM-HMM method, SVM-HMM method reduces the sample numbers of the information loss and model training, decreases the time of model training and increases the rate of identification. A satisfactory outcome on the test of diagnosis on the damage of rolling bearing has been obtained, and the feasibility of SVM-HMM model on the diagnosis of the rolling bearing has been verified.
(1)针对滚动轴承恒转速条件下故障引发的共振信号信噪比较高但非平稳的特点,利用EMD将其分解并提取包含故障信息的平稳信号,经包络解调后构建了反映轴承故障状态的特征矢量。研究了DHMM多样本训练方法并对轴承状态进行了识别。试验结果表明所提出的方法对单一故障和同时存在多种故障的轴承诊断都取得了良好的识别效果。
(2)指出了滚动轴承故障引发的轴承共振信号并非简单的偏置调幅信号,利用所建的故障模型试验了CAF的解调效果。通过研究、对比和分析,证明了CAF的形貌特征能够反映轴承的故障状态,结合CHMM对动态信号识别能力,提出了一种滚动轴承故障识别方法,并通过试验验证了方法的有效性。
(3)对于滚动轴承必须经历的启动过程,由于转速的变化,故障特征频率不复存在。利用小波分析方法提取故障共振信号,采用瞬时频率估计、阶比跟踪技术对轴承故障振动信号进行分析,给出了故障特征阶比系数的概念和算法,试验了算法在轴承故障诊断中的可行性。
(4)变转速更贴近旋转机械应用中的实际情况,本文指出了变转速滚动轴承故障信号的双变频调制特性并建立了故障模型。给出了OCAF的计算公式,研究了其对双变频信号的解调性能。利用特定转速下的故障动态信息构建特征向量,提出了基于OCAF-CHMM的滚动轴承故障诊断方法,并通过试验对该方法进行了检验。
(5)通过DCS获取齿轮特征信息,并采用DHMM模型对齿轮进行了故障识别。试验表明所提出的方法能够很好地识别断齿、点蚀等齿轮状态。这种方法结合了DCS解调信噪比高、表达相对简单和DHMM运算量相对较小的特点,有利于提高诊断速度。
(6)充分利用HMM和SVM在序列行为分类和小样本方面的优势,提出了基于SVM-HMM模型的旋转机械故障诊断方法。该模型在转子启动过程故障诊断试验中,与以往提出的基于SOM-HMM模型的诊断方法相比,减少了量化过程的信息损失及模型训练所需样本数量,提高了故障识别率并减少了模型训练时间。将该模型用于滚动轴承损伤类故障诊断中,也获得了较满意的效果,验证了SVM-HMM模型在滚动轴承故障诊断中的可行性。
Due to the special status of rotating machine in various types of mechanical equipment. Fault theories and diagnosis methods has always been a research focus of experts and scholars home and broad. In rotating machinery equipment, the rotor and its related components are the major fault sources, and the most of its vibration signal appears unstable, dynamic and random, and the fault signal becomes more complex when the rotating equipment works in the variable speed. So the signal extraction and fault recognition method has always been a complex and interesting subject. With the continual emergence of new technologies and methods of a variety of signal processing and pattern recognition, new vitality has been injected in solving these problems. This paper aims to introduce some new methods of signal processing and pattern recognition technology in fault diagnosis of rotating machine, and some useful researches and explorations of fault diagnosis methods for fault-prone bearings, gears and rotor.
(1) For the high noise ratio but unstable characteristics of resonance signal caused by rolling bearing fault at the constant speed conditions, EMD method is employed so that the resonance signal can be decomposed and fault information that contains smooth signal can be extracted, after demodulation by envelope the characteristics in reflecting the bearing fault state vector is constructed. Diverse training methods of DHMM are researched and the status of the bearing is also identified. Experimental result shows that the proposed method is capable in carrying out satisfactory result in testing both single and multiple faults.
(2) Pointing out that the resonance signal of the bearing caused by the failure of the antifriction bearing is not a simple bias AM signal, utilizing the fault models to test the outcome after demodulated by CAF. It is proved that the morphology of CAF could report the failure status of the bearing based on research, comparison and analysis. Combining the ability of CHMM to recognize the dynamic signal, a method of antifriction bearing fault detection is proposed, and the validity of the method has been verified.
(3) The fault characteristic frequency disappears during the starting procedure of the antifriction bearing due to the change of the rotate speed. The fault resonance signal is figured out by the method of wavelet analysis, the vibration signals of the antifriction bearing are analyzed by the method of estimation of instantaneous frequency and ratio tracking, so that the concept and algorithm of the fault characteristics and ratio coefficient are raised, and the feasibility of the algorithm has been verified.
(4) The change of the rotate speed is closer to real situation of the application of the rotating machinery. This article indicates the dual frequency conversion characteristics of variable speed antifriction bearing fault signal. Computational formula of OCAF is proposed, and research on the demodulation capability on dual-frequency signal has been done. A method of fault diagnosis on the rolling bearing is carried out based on OCAF-CHMM and the characteristic rector of the.multidate information under the specific rotate speed, and the method has been verified to be effective.
(5) The feature information of the gear is obtained via DCS, and the failure of the gear has been identified via DHMM model. The experiment indicates that the proposed method can recognize the broken tooth, corrosive pitting and sort of status correctly. The specific method is the combination of the three features, such as the high demodulation SNR of DCS, simplicity of expression and the reduced quantity of computation, which can improve the diagnostic rate.
(6) Fully taking the advantage of HMM model and SVM model on the sequence behavior classification and small sample, a rotating machine faults diagnosis method is proposed based on SVM-HMM model. Comparing with the SOM-HMM method, SVM-HMM method reduces the sample numbers of the information loss and model training, decreases the time of model training and increases the rate of identification. A satisfactory outcome on the test of diagnosis on the damage of rolling bearing has been obtained, and the feasibility of SVM-HMM model on the diagnosis of the rolling bearing has been verified.
引文
[1]韩捷,张瑞林,等.旋转机械故障机理及诊断技术[M].北京：机械工业出版社,1996.
[2]鞠萍华.旋转机械早期故障特征提取的时频分析方法研究[D].重庆：重庆大学,2010.
[3]毕果.基于循环平稳的滚动轴承及齿轮微弱故障特征提取应用研究[D].上海：上海交通大学机械系统与振动国家重点实验室,2007.
[4]叶大鹏.基于2D-HMM的旋转机械故障诊断方法及其应用研究[D].杭州：浙江大学,2004.
[5]Loutridis, S., E. Douka, L. J. Hadjileontiadis. Forced vibration behaviour and crack detection of cracked beams using instantaneous frequency[J]. NDT&E International,2005.38(5):411-419.
[6]Parey,A., N. Tandon. Impact velocity modelling and signal processing of spur gear vibration for the estimation ofdefect size[J]. Mechanical Systems and Signal Processing,2007.21(1):234-243.
[7]Dejie Yu, Junsheng Cheng, Yu Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing,2005(19):259-270.
[8]梅宏斌,杨叔子,师汉民.滚动轴承故障诊断高频共振法的包络频谱分析[J].洛阳工学院学报,1992,13(4)：77-83.
[9]梅宏斌,吴雅,师汉民,等.滚动轴承损伤类故障的一种精确诊断模型[J].华中理工大学学报,1992,20(增刊)：177-181.
[10]赵联春,马家驹.球轴承的弹性接触振动[J].机械工程学报,2003,39(5)：60-64.
[11]张耀强,陈建军,唐六丁,等.滚动轴承——转子系统非线性参数、强迫联合振动[J].机械强度,2009,31(6)：871-875.
[12]蹼良贵.机械设计(第六版)[M].北京：高等教育出版社,1996.
[13]丁玉兰,石来德.机械设备故障诊断技术[M].上海：上海科学技术文献出版社,1994.
[14]崔宁博.设备诊断技术[M].天津：南开大学出版社,1993.
[15]闻邦椿,武新华,丁千,等.故障旋转机械非线性动力学的理论与试验[M].科学出版社,2004.
[16]高永生,唐力伟,高岩,等.齿轮箱非线性研究现状与展望[J].军械工程学院学报.2007,19(3)：46-49.
[17]罗跃纲,李小彭,闻邦椿.双跨裂纹转子轴承系统非线性动态响应与混沌[J].东北大学学报(自然科学版),2005,26(3)：289-292.
[18]Sohre J S.Trouble-shooting to stop vibration of centrifugal[J]. Petro/Chem engineering,1968,11:22-33.
[19]白木万博阳.机械振动讲演论文集[C].郑州机械研究所.1984.
[20]夏松波,张礼勇.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.
[21]Mayes I W. Analysis of the response of a multi-rotor-bearing system containing a transverse crack in a rotor[J]. ASME Journal of Vibration, Acoustics, Stress, and Reliability in Design,1984,106(2):139-145.
[22]Martin R. Detection of ball bearing malfunctions [J]. Instruments and Control Systems.1976, (11).
[23]Y Kim Paul and Cowe LR. G. A review of rolling element bearing health monitoring. Proceeding of Machinery Vibration Monitoring and Analysis Meeting [C]. April 19-21,1983.
[24]Ronald W. Jacobs. Detection of mechanical faults in rotary blowers. Proceeding of Machinery Vibration Monitoringand Analysis Meeting[C]. April 1983:19-21.
[25]Sunnersjo C. S. Rolling bearing vibration — the effects of geometrical imperfaction and wear[J]. Journal of sound and vibration,1985,98(4),445-474.
[26]金少先,金中石.齿轮振动的边带分布特征与故障诊断实例[J],诊断工程程学报,1999,12(3)：429-433.
[27]徐尹格,颜玉玲。相关技术在滚动轴承故障诊断中的应用[J].应用数学和力学,1992,13(7)：43-37.
[28]梅宏斌,阎明印,杨叔子.滚动轴承故障诊断的高频共振法[J].机械设计与制造,1992(2)：14-16.
[29]芮执元,徐龙云,李贵明.基于倒频谱理论的滚动轴承故障检测[J].轴承,2007(1)：35-37.
[30]吕勇,李友荣,徐金梧等基于FFT-FS频谱细化及共振解调法的电机轴承故障诊断[J].矿山机械,2004,32(3)：64-66.
[31]杨少辉.频谱细化方法在真空泵轴承故障诊断中的应用研究[D].秦皇岛：燕山大学,2008.
[32]岳建海,裘正定.信号处理技术在滚动轴承故障诊断中的应用与发展[J].信号处理,2005,21(2)：185-190.
[33]仇学青,张鑫.滚动轴承故障诊断研究的国内现状与发展方向[J].煤矿机械.2007,28(6)：6-8.
[34]Henry K.Kwok and Douglas L.Jones. Improved Instantaneous Frequency Estimation Using an Adaptive Short-Time Fourier Transform[J]. IEEE Transactions on Signal Processing,2000,48(10):2964-2972.
[35]姚晓东,李言俊,樊寄松.基于改进型STFT的雷达目标识别[J].西北工业人学学报,1999,17：182-185.
[36]许红军,柯建波,党百振.跳频信号的STFT时一频分析[J].桂林电子业学院学报,1998,18(1)：15-17.
[37]张辉,王淑娟,张青森.基于小波包变换的滚动轴承故障诊断方法的研究[J].振动与冲击,2004,32(4)：127-131.
[38]G.Meltzera, Nguyen Phong Dienb. Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps[J]. Mechanical Systems and Signal Processing,2004, (18):985-992.
[39]何波.小波分析在本特利3500故障诊断系统中的应用[D].长春：吉林大学,2006.
[40]丁涛,王芳,任工昌.小波技术在轴承故障诊断中的应用现状及发展[J].煤矿机械,2008,29(7)：1-3.
[41]Morlet J, Arens G, Fourgeau E, et al. Wave, propagation and sampling theory and complex waves[J]. Geophysics,1982,47(2):222-236.
[42]何岭松.小波函数性质及其对小波分析结果的影响[J].振动工程学报,2000,13(1)：143-146.
[43]Mark W D. Spectral analysis of the convolution and filtering of non-stationary stochastic process[J]. Journal of Sound and Vibration,1970,11(1):19-63.
[44]Cohen L. Time-frequency distributions-a review[C]. Proceedings of the IEEE,1989, 77(7):941-981.
[45]D. C. Reed, A. M. Zoubir, B. Boashash. Aircraft flight parameter estimation based on passive acoustic techniques using the polynomial Wigner-Vilie distribution[J]. Acoust. Soc. Am.1997,102(1):207-223.
[46]邵忍平,沈允文,郭万林.基于时频分布的齿轮系统辨识研究[J].中国机械工程,2001.12(7)：738-740.
[47]胡峰,李友荣.小波—AR谱估计在齿轮故障诊断中的应用[J].冶金设备.2004,10(5)：47-49.
[48]柳新民,邱静,刘冠军.基于AR-连续HMM的故障诊断模型及应用[J].机械科学与技术,2005,24(3)：350-352,360.
[49]张晨罡,郝伟.基于AR模型的滚动轴承故障诊断[J].工艺与装备,2006(5)：35-36,40.
[50]刘宇宏,王士同,徐红林.基于AR模型的动态模糊聚类算法[J].计算机工程与设计,2008,29(1)：144-147.
[51]胡红英.局域波分解方法、特征剖析及应用研究[D].大连：大连理工大学,2006.
[52]胡劲松,杨世锡,吴昭同等.旋转机械振动信号基于EMD的HT和STFT时频分析比较[J].汽轮机技术,2002,44(6)：336-338.
[53]胡劲松,杨世锡,吴昭同.基于EMD和HT的旋转机械振动信号时频分析[J].振动、测试与诊断,2004,24(2)：106-110.
[54]曹冲锋.基于EMD的机械振动分析与诊断方法研究[D],杭州：浙江大学,2009.
[55]Yang B., C. S. Suh. Interpretation of crack-induced rotor non-linear response using instantaneous frequency[J]. Mechanical Systems and Signal Processing,2004. 18(3):491-513.
[56]D Guo, Z. P. Vibration analysis of a cracked rotor using Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007.21(8):3030-3041.
[57]胡劲松,杨世锡,周方洁,吴昭同.旋转机械振动信号基于EMD的HT和Winger分布时频分析比较[J].汽轮机技术,2003(45)5：307-309,318.
[58]程军圣,于德介,杨宇.EMD方法在转子局部碰摩故障诊断中的应用[J].振动、测试与诊断,2006,26(1)：24-27.
[59]石丁丁.基于EMD的异步电动机轴承故障诊断技术的研究[D].中北大学,2009.
[60]Du Q.S. Yang. Application of the EMD method in the vibration analysis of ball bearings[J]. Mechanical Systemsand Signal Processing,2007,21 (6):2634-2644.
[61]Parey, A., et al. Dynamic modelling of spur gear pair and application of empirical mode decomposition-basedstatistical analysis for early detection of localized tooth defect[J]. Journal of Sound and Vibration,2006.294(3):547-561.
[62]S. J. Loutridis. Damage detection in gear systems using empirical mode decomposition [J]. Engineering Structures.2004 (26):1833-1841.
[63]A M Bassiunya, X. L. Flute breakage detection during end milling using Hilbert-Huang transform and smoothednonlinear energy operator[J]. International Journal of Machine Tools and Manufacture,2007,47 (6):1011-1020.
[64]Li, H., X. Deng, H. Dai. Structural damage detection using the combination method of EMD and wavelet analysis [J]. Mechanical Systems and Signal Processing,2007.21 (1): 298-306.
[65]Feldman, M. Considering high harmonics for identification of non-linear systems by Hilbert transform[J]. Mechanical Systems and Signal Processing,2007,21(2): 943-958.
[66]Pines,D., L. Salvino. Structural health monitoring using empirical mode decomposition and the Hilbert phase[J]. Journal of Sound and Vibration,2006. 294(1-2):97-124.
[67]Danjiang Yu, W. R. EMD-based stochastic subspace identification of structures from operational vibration measurements[J]. Engineering Structures,2005, (27): 1741-1751.
[68]Spanos, P. D., A. Giaralis, N. P. Politis. Time-frequency representation of earthquake accelerograms and inelasticstructural response records using the adaptive chirplet decomposition and empirical mode decomposition[J]. Soil Dynamics and Earthquake Engineering,2007.27(7):675-689.
[69]Sekhar, S. C., T. V Sreenivas. Auditory motivated level-crossing approach . to instantaneous frequency estimation. Signal Processing[J].2005,53(4):1450-1462.
[70]全海燕,刘增力,吴庆畅.Emd模态混叠消除及其在语音基音提取中的应用研究[J].昆明理工大学学报(理工版),2006,31(4)：43-45.
[71]Zhihui,Z., S. Yunlian, L. Huangqiang. Hybrid of EMD and SVMs for Short-Term Load Forecasting[C]. in Control and Automation,2007. ICCA 2007. IEEE International Conference on 2007.
[72]姜鸣,陈进,秦恺,江慰军.一阶循环矩分析在旋转机械振动信号分析中的应用[J].振动工程学报,2001,14(4)：424-428.
[73]D. V. Zante, R. L. Federsen, P. J. Sherman. The stochastic structure of downstream pressure from an axial compressor — Ⅰ:A periodic time-frequency spectral description[J]. Mechanical Systems and Signal Processing,1996,10:413-422.
[74]陈再良,王传洋.一阶循环平稳新方法在旋转机械状态检测中的应用[J].新技术新工艺,2004,(6)：18-19.
[75]陈进,姜鸣.高阶循环统计量理论在机械故障诊断中的应用[J].振动工程学报,2001,14(2)：125-134.
[76]P. D. Mcfadden, J. D. Smith. Model for the vibration produced by a single point defect in a rolling element bearing [J]. Journal of Sound and Vibration,1984,96(1): 69-82.
[77]P.D.Mcfadden, J. D. Smith. The vibration produced by multiple point defects in a rolling element bearing[J]. Journal of Sound and Vibration,1985,98(2):263-273.
[78]A. Choudhury, N. Tandon. A theoretical model to predict vibration response of rolling bearings to distributed defects under radial load[J]. ASME Journal of vibration and Acoustics,1998,120:214-220.
[79]李力,屈梁生.二阶循环统计量在机械故障诊断中的应用[J].西安交通大学学报,2002,36(9)：943-946.
[80]周福昌,陈进,何俊,等.循环自相关函数的解调特性分析及其在故障诊断中的应用[J].振动工程学报,2004(17)：280-283.
[81]姜鸣.循环统计量理论及其在滚动轴承故障诊断中的应用研究[D].上海：上海交通大学,2002.
[82]McCormick A C, Nandi A K. Cyclostationarity in Rotating Machine Vibrations [J]. Mechanical System and Signal Processing 1998,12 (2):225-242.
[83]周福昌.基于循环平稳信号处理的滚动轴承故障诊断方法研究[D],上海：上海交通大学机械系统与振动国家重点实验室,2006.
[84]秦恺,陈进,姜鸣.一种滚动轴承故障特征提取的新方法——谱相关密度[J].振动与冲击,2001,20(1)：34-37.
[85]李力,廖湘辉.应用循环自相关函数诊断齿轮故障[J].二峡大学学报,2003,25(2)：129-133.
[86]C. Capdessus, M. Sidahmed, J. L. Lacoume. Cyclostationary processes:application in gear faults early diagnosis[J]. Mechanical System and Signal Processing, 2000,14(3):371-385.
[87]L.Bouillaut, M. Sidahmed. Cyclostatioanry approach and bilinear approach: comparison, applications to early diagnosis helicopter gearbox and classification method bsed on HOCS[J].Mechanical Systems and Signal Processing,2001, 15 (5):923-943.
[88]L.Bouilaut,M. Sidahmed. Cyclostationary and bilinear approaches for gears vibrating signals[J].Key Engineering Materials,1999,67:354-362.
[89]陈仲生,杨拥民.二阶循环平稳分析在转子早期碰摩故障识别中的应用[J].机械科学与技术,2004,23(2)：221-223.
[90]J. Antoni, J. Daniere, F. Guillet. Effective vibration analysis of IC engines using cyclostationarity. part Ⅱ — new results on the reconstruction of the cylinder pressures[J]. Journal of Sound and Vibration,2002,257(5):839-856.
[91]J. Antoni, J. Daniere, F. Guillet. Effective vibration analysis of IC engines using cyclostationarity. part Ⅰ — methodology for condition monitoring[J]. Journal of Sound and Vibration,2002,257(5):815-837.
[92]赵晓平,张令弥,郭勤涛.旋转机械阶比跟踪技术研究进展综述[J].地震工程与工程振动,2008,28(6)：49-53.
[93]Mingsian Bai, Jiamin Huang, Minghong Hong, et al. Fault diagnosis of rotating machinery using an intelligent order tracking system[J]. Journal of Sound and Vibration,2005(280):699-718.
[94]Bossley K M, Mckendrick R J, Harris C J, et al. Hybrid conputed order tracking[J]. Mechanical System and Signal Processing,1999,13(4):627-641.
[95]康海英,栾军英,田燕,等.阶次跟踪在齿轮磨损中的应用[J].振动与冲击,2006,25(4)：112-113，118.
[96]康海英,栾军英,郑海起.基于阶次包络谱的轴承故障诊断研究[J].振动与冲击,2006,25(5)：166-167,174.
[97]Fyf e K R, Munck E D S. Analysis of conputed order tracking[J]. Mechanical System and Signal Processing,1997,11 (2):187-205.
[98]郭瑜,秦树人,汤宝平,纪跃波.基于瞬时频率估计的旋转机械阶比跟踪[J].机械工程学报,2003,39(3)：32-36.
[99]郭瑜,秦树人.无转速计旋转机械升降速振动信号零相位阶比跟踪滤波[J].机械工程学报,2004,40(3)：50-54.
[100]孔庆鹏.发动机变速阶段振动信号阶比跟踪研究[D].杭州：浙江大学,2006.
[101]王琳.机械设备故障诊断与监测的常用方法及其发展趋势[J].武汉工业大学学报,2000,22(3)：62-64.
[102]Yufeng Zhao, Yao Zhao, Zhenfeng Zhu. TSVM-HMM:Transductive SVM based hidden Markov model for automatic image annotation[J]. Expert Systems with Applications, 2009(36):9813-9818.
[103]A. Ciehoeki, W. KasPrzka. Nonlinear learning algorithms for blind separation of natural images[J]. Neural Network World,1996,4:515-523.
[104]王作英,肖熙.基于段长分布的HMM语音识别模型[J].电子学报,2004,32(1)：46-49.
[105]Qing Wang, Zheru Chi, Feng, D. D., Rongchun Zhao. Hidden Markov random field based approachfor off-line handwritten Chinese character recognition[C]. Pattern Recognition,2000. Proceedings.15th International Conference on,2000,2: 347-350.
[106]谢锦辉.隐Markov模型(HMM)及其在语音处理中的应用[M].武汉：华中理工大学出版社,1995.
[107]J. Karhumen, A. HyVarinen, R. Vigario, et al. Applications of neural blind separation to signal and image Proeessing[C]. IEEE Int. Conf.on Aeoustics, speech and Signal Proeessing(ICASSP,97),1997:131-134.
[108]李宁.旋转机械的测试信号分析及隐马尔科夫模型应用研究[D].重庆：重庆大学,2010.
[109]冯长建,邵强,吴斌,等.混合SVM-HMM方法在旋转机械启动过程故障诊断中的应用研究[J].汽轮机技术,2008,50(6)：445-447,450.
[110]杨宇.基于EMD和支持向量机的旋转机械故障诊断方法研究[D].长沙：湖南大学,2005.
[111]Frank P.M. Fault diagnosis in dynaic systems using analytical and knowledge based redundancy-A Survey and Some New Results[J]. Automatica,1990,26(3):459-474.
[112]J. B. Theoeharis. A high-order neuron-fuzzy system internal dynamies:APPlication to the adaptive cancellation[J]. Fuzzy Sets and Systems.2006,157(1):471-500.
[113]Hasan Ocak, Kenneth A. Loparo. A new bearing fault detection and diagnosis scheme based on Hidden Markov Modeling of vibration signals[C]. IEEE International Conference on Acoustics, Speech and Signal Processing,2001(5):3141-3144.
[114]Mastorocostas, P. A, Theoehairs, J. B. A recurrent fuzzy-neural model for dynamic system identification[J]. IEEE Transactions on Systems, Man and Cybernetics, part B(Cybernetics).2002,32(2):179-190.
[115]丁夏完,刘金朝,王成国,等.利用SVM和相关山形聚类分析识别滚动轴承状态[J].2006(6)：29-32.
[116]程军圣,于德介,杨宇。基于EMD和SVM的滚动轴承故障诊断方法[J].航空动力学报,2006,21(3)：575-580.
[117]宋雪萍,马辉,毛国豪等.基于CHMM的旋转机械故障诊断技术[J].机械工程学报,2006,42(5)：126-130.
[118]李永龙,邵忍平,薛腾.基于小波神经网络的齿轮系统故障诊断[J].航空动力学报,2010,25(1)：234-240.
[119]A General Architecture for Supervised Classification of Multivariate Time Series, http://www.cse.unsw.edu.au/waleed/phd/tr9806/tr.html.
[120]Alexander Ypma, Co Melissant, Ole Baunbxk-Jensen, Robert P. W Duin. Health Monitoring with Learning Methods[C]. ICANN 2001:547-553.
[121]David M. J. Tax, Alexander Ypma, P W. Duin. Failure Detection Using Support Vector Data Descriptions[C]. IDA 1999:415-426.
[122]沈清,胡德文,时春编著.神经网络应用技术[M].长沙：国防科技大学出版社,1993.
[123]刘家锋,黄健华,唐降龙.基于HMM的联机汉字识别系统及其改进的训练方法[J].中文信息学报,15(4)：47-52.
[124]Changjun Zhou, Xiaopeng Wei, Qiang Zhang. Face recognition based on HMM-SVM[J]. ICIC Express Letters,2007,1(2):137-143.
[125]张军,陈付彬,付强,等.基于HMM的机动目标识别[J].国防科技大学学报,2003,25(2)：51-55.
[126]童进,吴昭同,等.大型旋转机械升降速过程故障诊断研究[J].振动测试与诊断,1999,19(3)：193-195.
[127]Heck, L. P., McClellan, J. H. Mechanical system monitoring using hidden Markov models[C]. International Conference on coustics, Speech, and Signal Processing, 1991,3:1697-1700.
[128]冯长建.HMM动态模式识别理论、方法以及在旋转机械故障诊断中的应用[D].杭州：浙江大学,2002.
[129]徐刚,骆志高,李明义等.变工况运行的机械故障诊断研究[J].机械工程学报,2001,37(12)：104-107.
[130]L. R. Rabiner. A tutorial on Hidden Markov Models and selected application in speech recognition[J].Proc. IEEE,1989,77 (2):257-286.
[131]邵强.切削加工过程中颤振的监测与识别方法研究[D].大连：大连理工大学.2010.
[132]茅晓泉,胡光锐等.一种DHMM的混合训练方法[J].电子学报,2002,30(1)：148-150.
[133]高雨清,陈永彬.隐Markov模型参数估计的一种新方法[J].自动化学报,1991,17(1)：56-62.
[134]Heck, L. P. Signal processing research in automatic tool wear monitoring[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993(1):55-58.
[135]Rabiner L R,Juang B H. An Introduction to Hidden Markov Models[J]. IEEE ASSP Magazine.1986,3(1):4-6.
[136]Nadas A, et al. On a Model-Robust Training Method for Speech Recognition[J]. IEEE Trains. ASSP,1988,36 (9):1432-1436.
[137]Tanaka, M., Sakawa, M., Shiromaru, I., Matsumoto, T. Application of Kohonen's self-organizing network to the diagnosis system for rotating machinery[M]. Systems, Man and Cybernetics,1995.
[138]战普明,等.语音识别隐马尔可夫模型的改进[J].电子学报,1994,22(1)：9-15.
[139]何强,张有为.多观察序列连续隐含马尔柯夫模型的无溢出参数重估[J].电子学报,2000,28(10)：98-101.
[140]何强,何英.Matlab扩展编程[M].北京：清华大学出版社,2002.
[141]Keerthi S S.Shavade S K. A fast interative nearest point algorithm for support vector machine classifier design[J]. IEEE Trans on Neural Networks,2000,11 (1): 124-136.
[142]Li Xuemei, Ding Lixing, Li Jincheng, Xu Gang. Combining KPCA and LSSVM for HVAC fan machinery fault recognition[C]. IEEE International Conference on Robotics and Biomimetics,2009:1241-1246.
[143]梅宏斌.滚动轴承振动监控与诊断[M].机械工业出版社,1995.
[144]Rubibi R. and Meneghetti U. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings[J]. Mechanical Systems and Signal Processing,2001,15(2),287-302.
[145]JR. Weaver W, Timoshenko S P, Young D H. Vibration Problems in Engineering, 5th Edition [M]. John Wiley&Sons,1990:478-484.
[146]Huang N E, shen Z, Long S R et al. The empirical mode decomposition and Hilbert for Spectrum for Nonlinear and Non-stationary Time Series Analysis[C], Proceedings of the Royal Society of London, Series A,1998,454(3):903-995.
[147]张漩.基于小波包和EMD的滚动轴承故障信号分析[D].北京：北京化工大学,2008.
[148]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].杭州：浙江大学,2003.
[149]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
[150]高广华.基于EMD的时频分析方法在机车轴承故障诊断中的应用研究[D].中南大学,2007.
[151]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 structural responses [J]. Bulletin of the Seismological Society of America,2001, 91 (5):1339-1357.
[152]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.
[153]陈进.旋转机械设备状态检测与故障诊断[M].上海：上海交通大学出版社.1999.
[154]毕果,陈进,李富才,等.谱相关密度分析在轴承点蚀故障诊断中的研究[J].振动工程学报,2006,19(3),388-391.
[155]He J, Chen j, Bi G. Frequency-demodulated analysis based on cyclostationarity for local fault detection[J]. Key Engineering Materials,2005,293,87-94.
[156]Seongkyu Y, John F, MacGregor. Fault diagnosis with multivariate statistical models part I:using steady state fault signatures [J]. Journal of process control, 2001,11:387-400.
[157]丁康,米林,王志杰.解调分析在故障诊断中应用的局限性问题[J].振动工程学报,i997,10(1)：13-20.
[158]Gardner W A, Sponer C M. The curnulant threoy of cyclostationary tirne-series, purt I:foundation[J]. IEEE Trans on Signal Processing,1994,42(12):3387-3408.
[159]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海大学学报,2002,30(6)：799-802.
[160]I.Antoniadis and G. Glossiotis. Cyclostatonary analysis of rolling-element bearing vibration signals[J]. Journal of Sound and vibration.2001,248(5): 829-845.
[161]Marple L. A. New Autoregreessive Spectrum Analysis Agorithm[J]. IEEE Trans, on ASSP,1980,28(4):441-454.
[162]胡广书.数字信号处理[M].北京：清华人学出版社,2002.
[163]赵联春,马家驹,范树迁等.滚动轴承振动分析中的AR模型研究[J].中国机械工程,2004,15(3),210-213.
[164]宋文健.阶比分析方法的研究[D].秦皇岛：燕山大学,2006.
[165]M. F. Albright, S. Qian, A Comparision of the newly Proposed Gabor Order Tracking Technique vs. Other Order Tracking Methods[C], in SAE Noise & vibration Conference.2001.
[166]Boualem Boashash. Estimating and interpreting the instantaneous frequency of signal-part2:algorithms and applications[C]. Proceeding of the IEEE,1992, 80(4):540-568.
[167]Min-Chun Pan, Shiu-Wei Liao, Chun-Chin Chiu. Improvement on Gabor order tracking and objective comparison with Vold-Kalman filtering order tracking[J]. Mechanical Systems and Signal Processing,2007(21):653-667.
[168]Blough J R. Development analysis of time variant discrete Fourier transform order tracking [J]. Mechanical systems and Signal Processing,2003,17(6):1185-1199.
[169]李爱先.孙涛等.旋转机械非平稳振动信号阶比特征分析系统设计[J].机械与电子,2007(6)：71-91.
[170]彭志科,何永勇.自适应数字滤波算法应用于旋转机械运行状态辨识的研究[J].机械工程学报,2001,37(9)：49-52.
[171]杨国安,吴贞焕,高金吉.基于自适应滤波的机械故障诊断方法研究[J].振动与冲击,2003,22(4)：4-7.
[172]张君,王金平,朱波.旋转机械振动信号处理中的滤波及特征提取技术[J].南京工程学院学报(自然科学版),2009,7(3)：14-22.
[173]席玉洁,马波,冯坤.滚动轴承故障监测诊断中的自适应滤波算法[J].轴承,2011(5)：47-51.
[174]冯长建,丁启全,吴昭同,李志农.转子启动过程HMM故障诊断方法研究[J].浙江大学学报(工学版),2002,36(6)：642-645.
[175]王平,廖明夫.滚动轴承故障诊断的自适应共振解调技术[J].航空动力学报,2005,20(4)：606-612.
[176]程军圣,于德介,杨宇,邓乾旺.尺度-小波能量谱在滚动轴承故障诊断中的应用[J].振动工程学报,2004,17(1)：606-612.
[177]Peter W Tse, Peng Y H, Richard Yam. Wavelet anylysis and envelop detection for rolling element bearing fault diagnosis-their effectives and flexibilities[J]. Journal of Vibration and Acoustics,2000,123:303-310.
[178]梅宏斌.滚动轴承损伤检测的小波分析法[J].轴承,1995,7：10-13.
[179]Wi jnant Y H, Wensing J A, Van Ni jen G C. The influence of lubrication on the dynamic behavior of ball bearings[J]. Journal of Sound and Vibration,1999,222(4): 579-596.
[180]赵联春,马家驹、曹志飞.设计和应用参数对球轴承振动特性的影响[J].轴承,2006(1)：4-8.
[181]陈季云,陈晓平.基于小波包分析的滚动轴承故障特征提取[J].微计算机信息,2007,23(4)：192-193,219.
[182]张国新,刘祚时.轴承早期故障的小波包-自回归谱诊断法[J].轴承,2007(2)：40-42.
[183]马辉,宋雪萍,赵鑫,等.小波包分析在转子裂纹故障检测中的应用[J].振动、测试与诊断,2006,26(4)：300-304.
[184]熊军,李凤英,沈玉娣。齿轮故障诊断技术应用综述[J]、机床与液压.2005(12)：12-15.
[185]刘仲川,张志伟,陈宝佳.齿轮故障诊断润滑油在线监测[M].西安：西安交通大学出版社,1992.
[186]汪泽民.齿轮及滚动轴承故障特征分离方法的研究[D].太原：太原理工大学,2010.
[187]成琼.基于小波分析的齿轮故障诊断研究[D].长沙：湖南大学,2001.
[188]淮良贵,纪名刚.机械设计[M].北京：高等教育出版社,2005：201-320.
[189]徐敏等主编.设备故障诊断手册—机械设备状态监测和故障诊断[M].西安：西安交通大学出版社,1998.
[190]曾法力.小波包分析在齿轮故障诊断中的应用[D].武汉：武汉科技大学,2005.
[191]P. D. McFadden. Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration[J]. ASME Transactions Journal of Vibration Acoustics Stress and Reliability in Design,1986(108):165-170.
[192]徐敏强,王日新.齿轮箱故障诊断中信号解调方法的研究[J].中国机械工程,2002,13(17)：1462-1465.
[193]孟涛.齿轮与滚动轴承故障的振动分析与诊断[D].西安：西北工业大学,2003.
[194]何俊,陈进,毕果,等.循环平稳度解调频原理分析及其在齿轮故障诊断中的应用[J].上海交通大学学报,2007,41(11)：1862-1866.
[195]GD. Zivanovic and W. A. Gardner. Degrees of Cyclostationarity and Their Application to Signal Detection and Estimation [J]. IEEE Signal Processing Magazine,1991 (22): 287-297.
[196]朱利民,钟秉林,黄仁.齿轮故障振动信号检波解调分析中的混频效应[J].振动工程学报,1999,12(3)：331-337.
[197]马波,魏强,徐春林,等.基于Hilbert变换的包络分析及其在滚动轴承故障诊断中的应用[J].北京化工大学学报,2004,31(6)：95-97.
[198]Xianfeng Fan, Ming J. Zuo. Gearbox fault detection using Hilbert and wavelet packet transform [J]. Mechanical Systems and Signal Processing.2006(20): 966-982.
[199]Hatzipatelis E, Murray A, Panman J. Comparing Hidden Markov Models with Artificial Neural Network Architectures for Condition Monitoring Applications[C]. Fourth International Conference on Artificial Neural Networks, Cambridge, U. K,1995.
[200]Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, NY, USA,1995.
[201]柳新民,邱静,刘冠军,基于HMM-SVM混合故障诊断模型及应用[J].航空学报.2005,26(4)：496-500.
[202]忻东,杨莹春,吴朝辉.基于SVM-HMM混合模型的说话人确认[J].计算机辅助设计与图形学学报.2002,14(11)：1080-1082.
[203]V. Vapnik and A. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities[J]. Theory of Probability and its Applications, 1971,16(2):264-280.
[204]Suykens J A K, Brabanter J Se, Lukas L, et al. Weighted least squares support vector machines:robustness and sparse approximation[J]. Neurocomputing,2002(48): 85-105.
[205]邓乃扬,田英杰.数据挖掘中的新方法—支持向量机[M].北京：科学出版社,2004.
[206]Lu Shuxia, Wang Xizhao. A comparison among four SVM classification methods: LSVM, NLSVM, SSVM and NSVM[C]. Proceedings of the Third International Conference on Machine Learning and Cybernetics. New York,2004:4277-4282.
[207]ReynaR. A., HemandezN., EsteveD.et al. Segmeniing images with support vector machines[C]. Proeeedings 2000 Intemational Conference on Image processing,2000, 1(1),820-823.
[208]K. Lee, H. Byun. A new face authentication system for memory-constrained devices[J]. IEEE Transactions on Consumer Electronics,2003,49(4):1214-1222.
[209]D. Gorgevik, D. Cakmakov. Combining SVM classifiers for handwritten digit recognition[J]. In Proceedings of 16th International Conference on Pattern Recognition,2002(3):102-105.
[210]W. M. Campbell. A SVM/HMM system for speaker recognition[C]. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing,2003(2): 209-212.
[211]李国正,王猛,曾华军.支持向量机导论[M].北京：电子工业出版社,2004.
[212]S. Chen, L. Hanzo. Block-adaptive kernel-based CDMA multiuser detection[C]. IEEE International Conference on Communications,2002(2):682-686.
[213]张宝昌,陈熙霖等.基于支持向量的Kernel判别分析[J].计算机学报,2006,29(12)：2143-2150.
[214]S. Osowski, L. T. Hoai, T. Markiewicz. Support vector machine-based expert system for reliable heartbeat recognition[J]. IEEE Transactions on Biomedical Engineering, 2004,51(4):582-589.
[215]C.Batur, L. Zhou, C. C. Chan. Support vector machines for fault detection[C].Proceedings of the 41st IEEE Conference on Decision and Control,2002(2):1355-1366.
[216]张键机械故障诊断技术[M].北京：机械工业出版社,2008.
[217]杨国安,张瑞林.机械设备故障诊断实用技术[M].北京：中国石化出版社,2008.
[218]刘文彬.旋转机械非平稳信号特征分析仪开发[D].昆明：昆明理工大学2008.
[219]赵晓平.旋转机械阶比分析研究与软件实现[D].南京：南京航空航天大学,2008.
[220]T. Joachims, SVM Light:Support Vector Machine, http://www-ai. informatik. uni-dortmund.de/FORSCHUNG/VERFAHREN/SVM_LIGHT/svm_light.eng.html, University of Dortmund, November 1999.
[221]冯长建,康晶,吴斌,胡红英.DHMM动态模式识别理论在旋转机械故障诊断中的应用[J],大连民族学院学报.2006,(3)：12-15,27.
[2]鞠萍华.旋转机械早期故障特征提取的时频分析方法研究[D].重庆：重庆大学,2010.
[3]毕果.基于循环平稳的滚动轴承及齿轮微弱故障特征提取应用研究[D].上海：上海交通大学机械系统与振动国家重点实验室,2007.
[4]叶大鹏.基于2D-HMM的旋转机械故障诊断方法及其应用研究[D].杭州：浙江大学,2004.
[5]Loutridis, S., E. Douka, L. J. Hadjileontiadis. Forced vibration behaviour and crack detection of cracked beams using instantaneous frequency[J]. NDT&E International,2005.38(5):411-419.
[6]Parey,A., N. Tandon. Impact velocity modelling and signal processing of spur gear vibration for the estimation ofdefect size[J]. Mechanical Systems and Signal Processing,2007.21(1):234-243.
[7]Dejie Yu, Junsheng Cheng, Yu Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing,2005(19):259-270.
[8]梅宏斌,杨叔子,师汉民.滚动轴承故障诊断高频共振法的包络频谱分析[J].洛阳工学院学报,1992,13(4)：77-83.
[9]梅宏斌,吴雅,师汉民,等.滚动轴承损伤类故障的一种精确诊断模型[J].华中理工大学学报,1992,20(增刊)：177-181.
[10]赵联春,马家驹.球轴承的弹性接触振动[J].机械工程学报,2003,39(5)：60-64.
[11]张耀强,陈建军,唐六丁,等.滚动轴承——转子系统非线性参数、强迫联合振动[J].机械强度,2009,31(6)：871-875.
[12]蹼良贵.机械设计(第六版)[M].北京：高等教育出版社,1996.
[13]丁玉兰,石来德.机械设备故障诊断技术[M].上海：上海科学技术文献出版社,1994.
[14]崔宁博.设备诊断技术[M].天津：南开大学出版社,1993.
[15]闻邦椿,武新华,丁千,等.故障旋转机械非线性动力学的理论与试验[M].科学出版社,2004.
[16]高永生,唐力伟,高岩,等.齿轮箱非线性研究现状与展望[J].军械工程学院学报.2007,19(3)：46-49.
[17]罗跃纲,李小彭,闻邦椿.双跨裂纹转子轴承系统非线性动态响应与混沌[J].东北大学学报(自然科学版),2005,26(3)：289-292.
[18]Sohre J S.Trouble-shooting to stop vibration of centrifugal[J]. Petro/Chem engineering,1968,11:22-33.
[19]白木万博阳.机械振动讲演论文集[C].郑州机械研究所.1984.
[20]夏松波,张礼勇.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.
[21]Mayes I W. Analysis of the response of a multi-rotor-bearing system containing a transverse crack in a rotor[J]. ASME Journal of Vibration, Acoustics, Stress, and Reliability in Design,1984,106(2):139-145.
[22]Martin R. Detection of ball bearing malfunctions [J]. Instruments and Control Systems.1976, (11).
[23]Y Kim Paul and Cowe LR. G. A review of rolling element bearing health monitoring. Proceeding of Machinery Vibration Monitoring and Analysis Meeting [C]. April 19-21,1983.
[24]Ronald W. Jacobs. Detection of mechanical faults in rotary blowers. Proceeding of Machinery Vibration Monitoringand Analysis Meeting[C]. April 1983:19-21.
[25]Sunnersjo C. S. Rolling bearing vibration — the effects of geometrical imperfaction and wear[J]. Journal of sound and vibration,1985,98(4),445-474.
[26]金少先,金中石.齿轮振动的边带分布特征与故障诊断实例[J],诊断工程程学报,1999,12(3)：429-433.
[27]徐尹格,颜玉玲。相关技术在滚动轴承故障诊断中的应用[J].应用数学和力学,1992,13(7)：43-37.
[28]梅宏斌,阎明印,杨叔子.滚动轴承故障诊断的高频共振法[J].机械设计与制造,1992(2)：14-16.
[29]芮执元,徐龙云,李贵明.基于倒频谱理论的滚动轴承故障检测[J].轴承,2007(1)：35-37.
[30]吕勇,李友荣,徐金梧等基于FFT-FS频谱细化及共振解调法的电机轴承故障诊断[J].矿山机械,2004,32(3)：64-66.
[31]杨少辉.频谱细化方法在真空泵轴承故障诊断中的应用研究[D].秦皇岛：燕山大学,2008.
[32]岳建海,裘正定.信号处理技术在滚动轴承故障诊断中的应用与发展[J].信号处理,2005,21(2)：185-190.
[33]仇学青,张鑫.滚动轴承故障诊断研究的国内现状与发展方向[J].煤矿机械.2007,28(6)：6-8.
[34]Henry K.Kwok and Douglas L.Jones. Improved Instantaneous Frequency Estimation Using an Adaptive Short-Time Fourier Transform[J]. IEEE Transactions on Signal Processing,2000,48(10):2964-2972.
[35]姚晓东,李言俊,樊寄松.基于改进型STFT的雷达目标识别[J].西北工业人学学报,1999,17：182-185.
[36]许红军,柯建波,党百振.跳频信号的STFT时一频分析[J].桂林电子业学院学报,1998,18(1)：15-17.
[37]张辉,王淑娟,张青森.基于小波包变换的滚动轴承故障诊断方法的研究[J].振动与冲击,2004,32(4)：127-131.
[38]G.Meltzera, Nguyen Phong Dienb. Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps[J]. Mechanical Systems and Signal Processing,2004, (18):985-992.
[39]何波.小波分析在本特利3500故障诊断系统中的应用[D].长春：吉林大学,2006.
[40]丁涛,王芳,任工昌.小波技术在轴承故障诊断中的应用现状及发展[J].煤矿机械,2008,29(7)：1-3.
[41]Morlet J, Arens G, Fourgeau E, et al. Wave, propagation and sampling theory and complex waves[J]. Geophysics,1982,47(2):222-236.
[42]何岭松.小波函数性质及其对小波分析结果的影响[J].振动工程学报,2000,13(1)：143-146.
[43]Mark W D. Spectral analysis of the convolution and filtering of non-stationary stochastic process[J]. Journal of Sound and Vibration,1970,11(1):19-63.
[44]Cohen L. Time-frequency distributions-a review[C]. Proceedings of the IEEE,1989, 77(7):941-981.
[45]D. C. Reed, A. M. Zoubir, B. Boashash. Aircraft flight parameter estimation based on passive acoustic techniques using the polynomial Wigner-Vilie distribution[J]. Acoust. Soc. Am.1997,102(1):207-223.
[46]邵忍平,沈允文,郭万林.基于时频分布的齿轮系统辨识研究[J].中国机械工程,2001.12(7)：738-740.
[47]胡峰,李友荣.小波—AR谱估计在齿轮故障诊断中的应用[J].冶金设备.2004,10(5)：47-49.
[48]柳新民,邱静,刘冠军.基于AR-连续HMM的故障诊断模型及应用[J].机械科学与技术,2005,24(3)：350-352,360.
[49]张晨罡,郝伟.基于AR模型的滚动轴承故障诊断[J].工艺与装备,2006(5)：35-36,40.
[50]刘宇宏,王士同,徐红林.基于AR模型的动态模糊聚类算法[J].计算机工程与设计,2008,29(1)：144-147.
[51]胡红英.局域波分解方法、特征剖析及应用研究[D].大连：大连理工大学,2006.
[52]胡劲松,杨世锡,吴昭同等.旋转机械振动信号基于EMD的HT和STFT时频分析比较[J].汽轮机技术,2002,44(6)：336-338.
[53]胡劲松,杨世锡,吴昭同.基于EMD和HT的旋转机械振动信号时频分析[J].振动、测试与诊断,2004,24(2)：106-110.
[54]曹冲锋.基于EMD的机械振动分析与诊断方法研究[D],杭州：浙江大学,2009.
[55]Yang B., C. S. Suh. Interpretation of crack-induced rotor non-linear response using instantaneous frequency[J]. Mechanical Systems and Signal Processing,2004. 18(3):491-513.
[56]D Guo, Z. P. Vibration analysis of a cracked rotor using Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007.21(8):3030-3041.
[57]胡劲松,杨世锡,周方洁,吴昭同.旋转机械振动信号基于EMD的HT和Winger分布时频分析比较[J].汽轮机技术,2003(45)5：307-309,318.
[58]程军圣,于德介,杨宇.EMD方法在转子局部碰摩故障诊断中的应用[J].振动、测试与诊断,2006,26(1)：24-27.
[59]石丁丁.基于EMD的异步电动机轴承故障诊断技术的研究[D].中北大学,2009.
[60]Du Q.S. Yang. Application of the EMD method in the vibration analysis of ball bearings[J]. Mechanical Systemsand Signal Processing,2007,21 (6):2634-2644.
[61]Parey, A., et al. Dynamic modelling of spur gear pair and application of empirical mode decomposition-basedstatistical analysis for early detection of localized tooth defect[J]. Journal of Sound and Vibration,2006.294(3):547-561.
[62]S. J. Loutridis. Damage detection in gear systems using empirical mode decomposition [J]. Engineering Structures.2004 (26):1833-1841.
[63]A M Bassiunya, X. L. Flute breakage detection during end milling using Hilbert-Huang transform and smoothednonlinear energy operator[J]. International Journal of Machine Tools and Manufacture,2007,47 (6):1011-1020.
[64]Li, H., X. Deng, H. Dai. Structural damage detection using the combination method of EMD and wavelet analysis [J]. Mechanical Systems and Signal Processing,2007.21 (1): 298-306.
[65]Feldman, M. Considering high harmonics for identification of non-linear systems by Hilbert transform[J]. Mechanical Systems and Signal Processing,2007,21(2): 943-958.
[66]Pines,D., L. Salvino. Structural health monitoring using empirical mode decomposition and the Hilbert phase[J]. Journal of Sound and Vibration,2006. 294(1-2):97-124.
[67]Danjiang Yu, W. R. EMD-based stochastic subspace identification of structures from operational vibration measurements[J]. Engineering Structures,2005, (27): 1741-1751.
[68]Spanos, P. D., A. Giaralis, N. P. Politis. Time-frequency representation of earthquake accelerograms and inelasticstructural response records using the adaptive chirplet decomposition and empirical mode decomposition[J]. Soil Dynamics and Earthquake Engineering,2007.27(7):675-689.
[69]Sekhar, S. C., T. V Sreenivas. Auditory motivated level-crossing approach . to instantaneous frequency estimation. Signal Processing[J].2005,53(4):1450-1462.
[70]全海燕,刘增力,吴庆畅.Emd模态混叠消除及其在语音基音提取中的应用研究[J].昆明理工大学学报(理工版),2006,31(4)：43-45.
[71]Zhihui,Z., S. Yunlian, L. Huangqiang. Hybrid of EMD and SVMs for Short-Term Load Forecasting[C]. in Control and Automation,2007. ICCA 2007. IEEE International Conference on 2007.
[72]姜鸣,陈进,秦恺,江慰军.一阶循环矩分析在旋转机械振动信号分析中的应用[J].振动工程学报,2001,14(4)：424-428.
[73]D. V. Zante, R. L. Federsen, P. J. Sherman. The stochastic structure of downstream pressure from an axial compressor — Ⅰ:A periodic time-frequency spectral description[J]. Mechanical Systems and Signal Processing,1996,10:413-422.
[74]陈再良,王传洋.一阶循环平稳新方法在旋转机械状态检测中的应用[J].新技术新工艺,2004,(6)：18-19.
[75]陈进,姜鸣.高阶循环统计量理论在机械故障诊断中的应用[J].振动工程学报,2001,14(2)：125-134.
[76]P. D. Mcfadden, J. D. Smith. Model for the vibration produced by a single point defect in a rolling element bearing [J]. Journal of Sound and Vibration,1984,96(1): 69-82.
[77]P.D.Mcfadden, J. D. Smith. The vibration produced by multiple point defects in a rolling element bearing[J]. Journal of Sound and Vibration,1985,98(2):263-273.
[78]A. Choudhury, N. Tandon. A theoretical model to predict vibration response of rolling bearings to distributed defects under radial load[J]. ASME Journal of vibration and Acoustics,1998,120:214-220.
[79]李力,屈梁生.二阶循环统计量在机械故障诊断中的应用[J].西安交通大学学报,2002,36(9)：943-946.
[80]周福昌,陈进,何俊,等.循环自相关函数的解调特性分析及其在故障诊断中的应用[J].振动工程学报,2004(17)：280-283.
[81]姜鸣.循环统计量理论及其在滚动轴承故障诊断中的应用研究[D].上海：上海交通大学,2002.
[82]McCormick A C, Nandi A K. Cyclostationarity in Rotating Machine Vibrations [J]. Mechanical System and Signal Processing 1998,12 (2):225-242.
[83]周福昌.基于循环平稳信号处理的滚动轴承故障诊断方法研究[D],上海：上海交通大学机械系统与振动国家重点实验室,2006.
[84]秦恺,陈进,姜鸣.一种滚动轴承故障特征提取的新方法——谱相关密度[J].振动与冲击,2001,20(1)：34-37.
[85]李力,廖湘辉.应用循环自相关函数诊断齿轮故障[J].二峡大学学报,2003,25(2)：129-133.
[86]C. Capdessus, M. Sidahmed, J. L. Lacoume. Cyclostationary processes:application in gear faults early diagnosis[J]. Mechanical System and Signal Processing, 2000,14(3):371-385.
[87]L.Bouillaut, M. Sidahmed. Cyclostatioanry approach and bilinear approach: comparison, applications to early diagnosis helicopter gearbox and classification method bsed on HOCS[J].Mechanical Systems and Signal Processing,2001, 15 (5):923-943.
[88]L.Bouilaut,M. Sidahmed. Cyclostationary and bilinear approaches for gears vibrating signals[J].Key Engineering Materials,1999,67:354-362.
[89]陈仲生,杨拥民.二阶循环平稳分析在转子早期碰摩故障识别中的应用[J].机械科学与技术,2004,23(2)：221-223.
[90]J. Antoni, J. Daniere, F. Guillet. Effective vibration analysis of IC engines using cyclostationarity. part Ⅱ — new results on the reconstruction of the cylinder pressures[J]. Journal of Sound and Vibration,2002,257(5):839-856.
[91]J. Antoni, J. Daniere, F. Guillet. Effective vibration analysis of IC engines using cyclostationarity. part Ⅰ — methodology for condition monitoring[J]. Journal of Sound and Vibration,2002,257(5):815-837.
[92]赵晓平,张令弥,郭勤涛.旋转机械阶比跟踪技术研究进展综述[J].地震工程与工程振动,2008,28(6)：49-53.
[93]Mingsian Bai, Jiamin Huang, Minghong Hong, et al. Fault diagnosis of rotating machinery using an intelligent order tracking system[J]. Journal of Sound and Vibration,2005(280):699-718.
[94]Bossley K M, Mckendrick R J, Harris C J, et al. Hybrid conputed order tracking[J]. Mechanical System and Signal Processing,1999,13(4):627-641.
[95]康海英,栾军英,田燕,等.阶次跟踪在齿轮磨损中的应用[J].振动与冲击,2006,25(4)：112-113，118.
[96]康海英,栾军英,郑海起.基于阶次包络谱的轴承故障诊断研究[J].振动与冲击,2006,25(5)：166-167,174.
[97]Fyf e K R, Munck E D S. Analysis of conputed order tracking[J]. Mechanical System and Signal Processing,1997,11 (2):187-205.
[98]郭瑜,秦树人,汤宝平,纪跃波.基于瞬时频率估计的旋转机械阶比跟踪[J].机械工程学报,2003,39(3)：32-36.
[99]郭瑜,秦树人.无转速计旋转机械升降速振动信号零相位阶比跟踪滤波[J].机械工程学报,2004,40(3)：50-54.
[100]孔庆鹏.发动机变速阶段振动信号阶比跟踪研究[D].杭州：浙江大学,2006.
[101]王琳.机械设备故障诊断与监测的常用方法及其发展趋势[J].武汉工业大学学报,2000,22(3)：62-64.
[102]Yufeng Zhao, Yao Zhao, Zhenfeng Zhu. TSVM-HMM:Transductive SVM based hidden Markov model for automatic image annotation[J]. Expert Systems with Applications, 2009(36):9813-9818.
[103]A. Ciehoeki, W. KasPrzka. Nonlinear learning algorithms for blind separation of natural images[J]. Neural Network World,1996,4:515-523.
[104]王作英,肖熙.基于段长分布的HMM语音识别模型[J].电子学报,2004,32(1)：46-49.
[105]Qing Wang, Zheru Chi, Feng, D. D., Rongchun Zhao. Hidden Markov random field based approachfor off-line handwritten Chinese character recognition[C]. Pattern Recognition,2000. Proceedings.15th International Conference on,2000,2: 347-350.
[106]谢锦辉.隐Markov模型(HMM)及其在语音处理中的应用[M].武汉：华中理工大学出版社,1995.
[107]J. Karhumen, A. HyVarinen, R. Vigario, et al. Applications of neural blind separation to signal and image Proeessing[C]. IEEE Int. Conf.on Aeoustics, speech and Signal Proeessing(ICASSP,97),1997:131-134.
[108]李宁.旋转机械的测试信号分析及隐马尔科夫模型应用研究[D].重庆：重庆大学,2010.
[109]冯长建,邵强,吴斌,等.混合SVM-HMM方法在旋转机械启动过程故障诊断中的应用研究[J].汽轮机技术,2008,50(6)：445-447,450.
[110]杨宇.基于EMD和支持向量机的旋转机械故障诊断方法研究[D].长沙：湖南大学,2005.
[111]Frank P.M. Fault diagnosis in dynaic systems using analytical and knowledge based redundancy-A Survey and Some New Results[J]. Automatica,1990,26(3):459-474.
[112]J. B. Theoeharis. A high-order neuron-fuzzy system internal dynamies:APPlication to the adaptive cancellation[J]. Fuzzy Sets and Systems.2006,157(1):471-500.
[113]Hasan Ocak, Kenneth A. Loparo. A new bearing fault detection and diagnosis scheme based on Hidden Markov Modeling of vibration signals[C]. IEEE International Conference on Acoustics, Speech and Signal Processing,2001(5):3141-3144.
[114]Mastorocostas, P. A, Theoehairs, J. B. A recurrent fuzzy-neural model for dynamic system identification[J]. IEEE Transactions on Systems, Man and Cybernetics, part B(Cybernetics).2002,32(2):179-190.
[115]丁夏完,刘金朝,王成国,等.利用SVM和相关山形聚类分析识别滚动轴承状态[J].2006(6)：29-32.
[116]程军圣,于德介,杨宇。基于EMD和SVM的滚动轴承故障诊断方法[J].航空动力学报,2006,21(3)：575-580.
[117]宋雪萍,马辉,毛国豪等.基于CHMM的旋转机械故障诊断技术[J].机械工程学报,2006,42(5)：126-130.
[118]李永龙,邵忍平,薛腾.基于小波神经网络的齿轮系统故障诊断[J].航空动力学报,2010,25(1)：234-240.
[119]A General Architecture for Supervised Classification of Multivariate Time Series, http://www.cse.unsw.edu.au/waleed/phd/tr9806/tr.html.
[120]Alexander Ypma, Co Melissant, Ole Baunbxk-Jensen, Robert P. W Duin. Health Monitoring with Learning Methods[C]. ICANN 2001:547-553.
[121]David M. J. Tax, Alexander Ypma, P W. Duin. Failure Detection Using Support Vector Data Descriptions[C]. IDA 1999:415-426.
[122]沈清,胡德文,时春编著.神经网络应用技术[M].长沙：国防科技大学出版社,1993.
[123]刘家锋,黄健华,唐降龙.基于HMM的联机汉字识别系统及其改进的训练方法[J].中文信息学报,15(4)：47-52.
[124]Changjun Zhou, Xiaopeng Wei, Qiang Zhang. Face recognition based on HMM-SVM[J]. ICIC Express Letters,2007,1(2):137-143.
[125]张军,陈付彬,付强,等.基于HMM的机动目标识别[J].国防科技大学学报,2003,25(2)：51-55.
[126]童进,吴昭同,等.大型旋转机械升降速过程故障诊断研究[J].振动测试与诊断,1999,19(3)：193-195.
[127]Heck, L. P., McClellan, J. H. Mechanical system monitoring using hidden Markov models[C]. International Conference on coustics, Speech, and Signal Processing, 1991,3:1697-1700.
[128]冯长建.HMM动态模式识别理论、方法以及在旋转机械故障诊断中的应用[D].杭州：浙江大学,2002.
[129]徐刚,骆志高,李明义等.变工况运行的机械故障诊断研究[J].机械工程学报,2001,37(12)：104-107.
[130]L. R. Rabiner. A tutorial on Hidden Markov Models and selected application in speech recognition[J].Proc. IEEE,1989,77 (2):257-286.
[131]邵强.切削加工过程中颤振的监测与识别方法研究[D].大连：大连理工大学.2010.
[132]茅晓泉,胡光锐等.一种DHMM的混合训练方法[J].电子学报,2002,30(1)：148-150.
[133]高雨清,陈永彬.隐Markov模型参数估计的一种新方法[J].自动化学报,1991,17(1)：56-62.
[134]Heck, L. P. Signal processing research in automatic tool wear monitoring[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993(1):55-58.
[135]Rabiner L R,Juang B H. An Introduction to Hidden Markov Models[J]. IEEE ASSP Magazine.1986,3(1):4-6.
[136]Nadas A, et al. On a Model-Robust Training Method for Speech Recognition[J]. IEEE Trains. ASSP,1988,36 (9):1432-1436.
[137]Tanaka, M., Sakawa, M., Shiromaru, I., Matsumoto, T. Application of Kohonen's self-organizing network to the diagnosis system for rotating machinery[M]. Systems, Man and Cybernetics,1995.
[138]战普明,等.语音识别隐马尔可夫模型的改进[J].电子学报,1994,22(1)：9-15.
[139]何强,张有为.多观察序列连续隐含马尔柯夫模型的无溢出参数重估[J].电子学报,2000,28(10)：98-101.
[140]何强,何英.Matlab扩展编程[M].北京：清华大学出版社,2002.
[141]Keerthi S S.Shavade S K. A fast interative nearest point algorithm for support vector machine classifier design[J]. IEEE Trans on Neural Networks,2000,11 (1): 124-136.
[142]Li Xuemei, Ding Lixing, Li Jincheng, Xu Gang. Combining KPCA and LSSVM for HVAC fan machinery fault recognition[C]. IEEE International Conference on Robotics and Biomimetics,2009:1241-1246.
[143]梅宏斌.滚动轴承振动监控与诊断[M].机械工业出版社,1995.
[144]Rubibi R. and Meneghetti U. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings[J]. Mechanical Systems and Signal Processing,2001,15(2),287-302.
[145]JR. Weaver W, Timoshenko S P, Young D H. Vibration Problems in Engineering, 5th Edition [M]. John Wiley&Sons,1990:478-484.
[146]Huang N E, shen Z, Long S R et al. The empirical mode decomposition and Hilbert for Spectrum for Nonlinear and Non-stationary Time Series Analysis[C], Proceedings of the Royal Society of London, Series A,1998,454(3):903-995.
[147]张漩.基于小波包和EMD的滚动轴承故障信号分析[D].北京：北京化工大学,2008.
[148]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].杭州：浙江大学,2003.
[149]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
[150]高广华.基于EMD的时频分析方法在机车轴承故障诊断中的应用研究[D].中南大学,2007.
[151]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 structural responses [J]. Bulletin of the Seismological Society of America,2001, 91 (5):1339-1357.
[152]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.
[153]陈进.旋转机械设备状态检测与故障诊断[M].上海：上海交通大学出版社.1999.
[154]毕果,陈进,李富才,等.谱相关密度分析在轴承点蚀故障诊断中的研究[J].振动工程学报,2006,19(3),388-391.
[155]He J, Chen j, Bi G. Frequency-demodulated analysis based on cyclostationarity for local fault detection[J]. Key Engineering Materials,2005,293,87-94.
[156]Seongkyu Y, John F, MacGregor. Fault diagnosis with multivariate statistical models part I:using steady state fault signatures [J]. Journal of process control, 2001,11:387-400.
[157]丁康,米林,王志杰.解调分析在故障诊断中应用的局限性问题[J].振动工程学报,i997,10(1)：13-20.
[158]Gardner W A, Sponer C M. The curnulant threoy of cyclostationary tirne-series, purt I:foundation[J]. IEEE Trans on Signal Processing,1994,42(12):3387-3408.
[159]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海大学学报,2002,30(6)：799-802.
[160]I.Antoniadis and G. Glossiotis. Cyclostatonary analysis of rolling-element bearing vibration signals[J]. Journal of Sound and vibration.2001,248(5): 829-845.
[161]Marple L. A. New Autoregreessive Spectrum Analysis Agorithm[J]. IEEE Trans, on ASSP,1980,28(4):441-454.
[162]胡广书.数字信号处理[M].北京：清华人学出版社,2002.
[163]赵联春,马家驹,范树迁等.滚动轴承振动分析中的AR模型研究[J].中国机械工程,2004,15(3),210-213.
[164]宋文健.阶比分析方法的研究[D].秦皇岛：燕山大学,2006.
[165]M. F. Albright, S. Qian, A Comparision of the newly Proposed Gabor Order Tracking Technique vs. Other Order Tracking Methods[C], in SAE Noise & vibration Conference.2001.
[166]Boualem Boashash. Estimating and interpreting the instantaneous frequency of signal-part2:algorithms and applications[C]. Proceeding of the IEEE,1992, 80(4):540-568.
[167]Min-Chun Pan, Shiu-Wei Liao, Chun-Chin Chiu. Improvement on Gabor order tracking and objective comparison with Vold-Kalman filtering order tracking[J]. Mechanical Systems and Signal Processing,2007(21):653-667.
[168]Blough J R. Development analysis of time variant discrete Fourier transform order tracking [J]. Mechanical systems and Signal Processing,2003,17(6):1185-1199.
[169]李爱先.孙涛等.旋转机械非平稳振动信号阶比特征分析系统设计[J].机械与电子,2007(6)：71-91.
[170]彭志科,何永勇.自适应数字滤波算法应用于旋转机械运行状态辨识的研究[J].机械工程学报,2001,37(9)：49-52.
[171]杨国安,吴贞焕,高金吉.基于自适应滤波的机械故障诊断方法研究[J].振动与冲击,2003,22(4)：4-7.
[172]张君,王金平,朱波.旋转机械振动信号处理中的滤波及特征提取技术[J].南京工程学院学报(自然科学版),2009,7(3)：14-22.
[173]席玉洁,马波,冯坤.滚动轴承故障监测诊断中的自适应滤波算法[J].轴承,2011(5)：47-51.
[174]冯长建,丁启全,吴昭同,李志农.转子启动过程HMM故障诊断方法研究[J].浙江大学学报(工学版),2002,36(6)：642-645.
[175]王平,廖明夫.滚动轴承故障诊断的自适应共振解调技术[J].航空动力学报,2005,20(4)：606-612.
[176]程军圣,于德介,杨宇,邓乾旺.尺度-小波能量谱在滚动轴承故障诊断中的应用[J].振动工程学报,2004,17(1)：606-612.
[177]Peter W Tse, Peng Y H, Richard Yam. Wavelet anylysis and envelop detection for rolling element bearing fault diagnosis-their effectives and flexibilities[J]. Journal of Vibration and Acoustics,2000,123:303-310.
[178]梅宏斌.滚动轴承损伤检测的小波分析法[J].轴承,1995,7：10-13.
[179]Wi jnant Y H, Wensing J A, Van Ni jen G C. The influence of lubrication on the dynamic behavior of ball bearings[J]. Journal of Sound and Vibration,1999,222(4): 579-596.
[180]赵联春,马家驹、曹志飞.设计和应用参数对球轴承振动特性的影响[J].轴承,2006(1)：4-8.
[181]陈季云,陈晓平.基于小波包分析的滚动轴承故障特征提取[J].微计算机信息,2007,23(4)：192-193,219.
[182]张国新,刘祚时.轴承早期故障的小波包-自回归谱诊断法[J].轴承,2007(2)：40-42.
[183]马辉,宋雪萍,赵鑫,等.小波包分析在转子裂纹故障检测中的应用[J].振动、测试与诊断,2006,26(4)：300-304.
[184]熊军,李凤英,沈玉娣。齿轮故障诊断技术应用综述[J]、机床与液压.2005(12)：12-15.
[185]刘仲川,张志伟,陈宝佳.齿轮故障诊断润滑油在线监测[M].西安：西安交通大学出版社,1992.
[186]汪泽民.齿轮及滚动轴承故障特征分离方法的研究[D].太原：太原理工大学,2010.
[187]成琼.基于小波分析的齿轮故障诊断研究[D].长沙：湖南大学,2001.
[188]淮良贵,纪名刚.机械设计[M].北京：高等教育出版社,2005：201-320.
[189]徐敏等主编.设备故障诊断手册—机械设备状态监测和故障诊断[M].西安：西安交通大学出版社,1998.
[190]曾法力.小波包分析在齿轮故障诊断中的应用[D].武汉：武汉科技大学,2005.
[191]P. D. McFadden. Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration[J]. ASME Transactions Journal of Vibration Acoustics Stress and Reliability in Design,1986(108):165-170.
[192]徐敏强,王日新.齿轮箱故障诊断中信号解调方法的研究[J].中国机械工程,2002,13(17)：1462-1465.
[193]孟涛.齿轮与滚动轴承故障的振动分析与诊断[D].西安：西北工业大学,2003.
[194]何俊,陈进,毕果,等.循环平稳度解调频原理分析及其在齿轮故障诊断中的应用[J].上海交通大学学报,2007,41(11)：1862-1866.
[195]GD. Zivanovic and W. A. Gardner. Degrees of Cyclostationarity and Their Application to Signal Detection and Estimation [J]. IEEE Signal Processing Magazine,1991 (22): 287-297.
[196]朱利民,钟秉林,黄仁.齿轮故障振动信号检波解调分析中的混频效应[J].振动工程学报,1999,12(3)：331-337.
[197]马波,魏强,徐春林,等.基于Hilbert变换的包络分析及其在滚动轴承故障诊断中的应用[J].北京化工大学学报,2004,31(6)：95-97.
[198]Xianfeng Fan, Ming J. Zuo. Gearbox fault detection using Hilbert and wavelet packet transform [J]. Mechanical Systems and Signal Processing.2006(20): 966-982.
[199]Hatzipatelis E, Murray A, Panman J. Comparing Hidden Markov Models with Artificial Neural Network Architectures for Condition Monitoring Applications[C]. Fourth International Conference on Artificial Neural Networks, Cambridge, U. K,1995.
[200]Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, NY, USA,1995.
[201]柳新民,邱静,刘冠军,基于HMM-SVM混合故障诊断模型及应用[J].航空学报.2005,26(4)：496-500.
[202]忻东,杨莹春,吴朝辉.基于SVM-HMM混合模型的说话人确认[J].计算机辅助设计与图形学学报.2002,14(11)：1080-1082.
[203]V. Vapnik and A. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities[J]. Theory of Probability and its Applications, 1971,16(2):264-280.
[204]Suykens J A K, Brabanter J Se, Lukas L, et al. Weighted least squares support vector machines:robustness and sparse approximation[J]. Neurocomputing,2002(48): 85-105.
[205]邓乃扬,田英杰.数据挖掘中的新方法—支持向量机[M].北京：科学出版社,2004.
[206]Lu Shuxia, Wang Xizhao. A comparison among four SVM classification methods: LSVM, NLSVM, SSVM and NSVM[C]. Proceedings of the Third International Conference on Machine Learning and Cybernetics. New York,2004:4277-4282.
[207]ReynaR. A., HemandezN., EsteveD.et al. Segmeniing images with support vector machines[C]. Proeeedings 2000 Intemational Conference on Image processing,2000, 1(1),820-823.
[208]K. Lee, H. Byun. A new face authentication system for memory-constrained devices[J]. IEEE Transactions on Consumer Electronics,2003,49(4):1214-1222.
[209]D. Gorgevik, D. Cakmakov. Combining SVM classifiers for handwritten digit recognition[J]. In Proceedings of 16th International Conference on Pattern Recognition,2002(3):102-105.
[210]W. M. Campbell. A SVM/HMM system for speaker recognition[C]. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing,2003(2): 209-212.
[211]李国正,王猛,曾华军.支持向量机导论[M].北京：电子工业出版社,2004.
[212]S. Chen, L. Hanzo. Block-adaptive kernel-based CDMA multiuser detection[C]. IEEE International Conference on Communications,2002(2):682-686.
[213]张宝昌,陈熙霖等.基于支持向量的Kernel判别分析[J].计算机学报,2006,29(12)：2143-2150.
[214]S. Osowski, L. T. Hoai, T. Markiewicz. Support vector machine-based expert system for reliable heartbeat recognition[J]. IEEE Transactions on Biomedical Engineering, 2004,51(4):582-589.
[215]C.Batur, L. Zhou, C. C. Chan. Support vector machines for fault detection[C].Proceedings of the 41st IEEE Conference on Decision and Control,2002(2):1355-1366.
[216]张键机械故障诊断技术[M].北京：机械工业出版社,2008.
[217]杨国安,张瑞林.机械设备故障诊断实用技术[M].北京：中国石化出版社,2008.
[218]刘文彬.旋转机械非平稳信号特征分析仪开发[D].昆明：昆明理工大学2008.
[219]赵晓平.旋转机械阶比分析研究与软件实现[D].南京：南京航空航天大学,2008.
[220]T. Joachims, SVM Light:Support Vector Machine, http://www-ai. informatik. uni-dortmund.de/FORSCHUNG/VERFAHREN/SVM_LIGHT/svm_light.eng.html, University of Dortmund, November 1999.
[221]冯长建,康晶,吴斌,胡红英.DHMM动态模式识别理论在旋转机械故障诊断中的应用[J],大连民族学院学报.2006,(3)：12-15,27.
© 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 |