旋转机械轴承振动信号分析方法研究
|
摘要
轴承作为旋转机械中广泛使用的关键零部件之一，其运行状态直接关系整台机械设备的工作性能，开展基于振动信号分析的轴承状态监测与故障诊断的相关研究并及时准确地识别故障萌发与演变，对确保设备平稳运行、减少甚至避免重大安全事故具有相当重要的意义。本课题以滚动轴承及滑动轴承故障振动信号为研究对象，针对滚动轴承故障信号易受噪声干扰的影响拓展并丰富了峭度图理论在振动信号降噪以及故障特征提取中的应用，并基于定向循环平稳分析理论研究了滑动轴承在油膜失稳状态下的故障信号特征，最后开发了虚拟式旋转机械轴承测试诊断系统实现了理论创新成果在工程实践中的应用。本文的具体研究内容介绍如下：
本文首先介绍了峭度图理论中涉及的峭度统计理论、谱峭度系数等数学基础，并详细阐述了传统峭度图算法以及基于COT的阶比峭度图算法在故障信号降噪和最优解调频带参数确定方面的优势。针对传统四阶矩累积量谱峭度系数易受信号奇异点影响而不能真实估计信号峰态程度水平的问题，定义了Moors谱峭度、Hogg谱峭度以及Crow-Siddiqui谱峭度等三种鲁棒性谱峭度系数，并提出了能消除信号奇异点干扰的鲁棒性峭度图算法。滚动轴承仿真及实测故障信号分析结果验证了鲁棒性峭度图与传统峭度图相比，能显著提高滤波效果并增强原始信号中的故障瞬态冲击特征。
传统的基于STFT、FIR滤波器以及第一代小波包变换的峭度图算法中存在时频分辨率取舍、运算效率以及小波函数库有限等缺点，本文提出了基于尺度自适应冗余提升小波包变换的峭度图算法，通过基于原始信号本身构造更新器和预测器来实现信号分解与重构的自适应功能，有效地提高了峭度图算法的自适应特性以及滤波后故障信号的准确性。随后利用时间尺度分解进一步增强了峭度图滤波后信号的瞬态冲击故障特征，并将基于固有时间尺度分解的Hilbert谱作为包络解调分析的有益补充，通过与Hilbert-Huang变换的对比验证了其在表征故障信号时频能量信息分布中的优越性。
旋转机械振动信号往往呈现循环平稳特性，本文将机械故障诊断领域中用于分析单通道实信号的循环平稳统计量理论进行了拓展，定义了能用于分析双通道融合复信号的低阶定向循环平稳统计量并提取出了滑动轴承油膜失稳故障信号中周期性时变特征以及转子振动状态信息等。通过对比分析揭示了传统全谱分析以及定向Wigner分布与定向循环统计量之间的内在联系，即全谱分析实质上就是一阶定向循环平稳统计量（定向循环均值），而定向Wigner分布实质上属于二阶定向循环统计量（定向循环谱相关函数）。此外试验结果分别对比分析了加速度振动信号与位移振动信号中所包含系统振动状态信息。
根据轴承振动监测需求分析，基于NI Labview平台开发了轴承振动信号测试、分析与特征提取于一体的虚拟式旋转机械轴承测试系统，为取得的理论创新成果在工程实践中的应用提供了强有力的支持。文章最后对全文工作及主要创新点进行了总结，并展望了后续的研究方向。
Bearings are one of the most widely used elements in the rotating machinery andtheir performance of great necessity to the operation of the mechanical equipment. Inorder to timely identify the deterioration in the condition of the bearings, it is significantto carry out the vibration based condition monitoring and fault diagnosis. In this thesis,the kurtogram based method is firstly developed and improved for the de-noising andfault characterization of the vibration signals buried in strong noise for the rollingelement bearings. Subsequently, the directional cyclostationarity analysis basedalgorithm is proposed to reveal significant features of the vibration signature for thejournal bearings with oil film instability faults. Finally, the integrated and virtualmeasurement and diagnosis system for the bearings of rotary machines is developed tofacilitate the practical application of the achievement in this thesis. The main researchesare listed as follows:
Firstly, the mathematical basis including the kurtosis statistics and spectral kurtosiscoefficients of the kurtogram is reviewed. Subsequently, the specific algorithm of theconventional kurtogram as well as the COT based order-kurtogram is introduced. It isstated that the kurtogram is able to de-noise and identify the optimal demodulation bandfor the following analysis of the vibration signal of the rolling element bearings. Sincethe traditional spectral kurtosis coefficient is easy to be influenced by the singularvalues in the original signal, the robust spectral kurtosis coefficients including theMoors spectral kurtosis, Hogg spectral kurtosis and the Crow-Siddiqui spectral kurtosisare defined. The robust kurtogram is also proposed to eliminate the interference of thesingular values. The simulation test and practical application has verified that theproposed robust kurtogram is able to improve the filtering effect and enhance theimpulsiveness of the fault signal.
Since the filtered signals will be inputted to the distribution of the spectral kurtosis,it is necessary to select the optimal filter in order to improve the ability of the kurtogram.The STFT suffers from the trade-off between the time resolution and frequencyresolution according to the Heisenberg uncertainty principle. The FIR filter-bank suffersfrom the low operation efficiency and WPT from the limit of the wavelet functionlibrary. Therefore, an improved kurtogram is proposed based on the scale-adaptiveredundant lifting wavelet packet transform which can construct the updater and predictor according to the signal itself as well as to adaptively decompose the originalsignal. The results have proved that the proposed method is able to improve theadaptivity of the conventional kurtogram and ensure the accuracy of the signal filteredby the kurtogram. Moreover, the intrisic time-scale decomposition based Hilbertspectrum has been proposed to supplement the envelope analysis. Compared with theHilbert-Huang transform, practical test has verified the superiority of the ITD basedHilbert spectrum when characterizing the distribution of the time-frequency energy ofthe fault signal.
Cyclostationarity defined to represent the correlation features of periodicphenomenon has been widely used to characterizing the fault vibration signal obtainedfrom the rotating machinery. Based on the conventional cyclic statistics, the directionalcyclic parameters including the directional cyclic mean, directional cyclic correlation,and directional spectral correlation density are defined to represent the complex-valuesignal integrated from double channel. It is found that the directional cyclic statistics areable to extract the periodically varying characteristics as well as the information relatedto the vibrating condition of the journal bearing supported rotor under oil film instabilityfault. In addition, the relationship between the conventional full spectrum, thedirectional Wigner distribution and the directional cyclic statistics is revealed. The fullspectrum can be interpreted as the first order cyclic statistics, namely the cyclic mean.The directional Wigner distribution belongs to the second order cyclic parameters(directional cyclic correlation function). Moreover, the acceleration signal basedcondition monitoring is compared with the displacement signal based conditionmonitoring when describing the system vibration.
According to the requirements analysis of the vibration based condition monitoringon bearings, the NI Labview based virtual measurement and diagnosis system for thebearings of rotary machines is developed. The system which is able to conduct dataacquisition, signal analysis and fault diagnosis has facilitated the practical application ofthe achievement in this thesis.
本文首先介绍了峭度图理论中涉及的峭度统计理论、谱峭度系数等数学基础，并详细阐述了传统峭度图算法以及基于COT的阶比峭度图算法在故障信号降噪和最优解调频带参数确定方面的优势。针对传统四阶矩累积量谱峭度系数易受信号奇异点影响而不能真实估计信号峰态程度水平的问题，定义了Moors谱峭度、Hogg谱峭度以及Crow-Siddiqui谱峭度等三种鲁棒性谱峭度系数，并提出了能消除信号奇异点干扰的鲁棒性峭度图算法。滚动轴承仿真及实测故障信号分析结果验证了鲁棒性峭度图与传统峭度图相比，能显著提高滤波效果并增强原始信号中的故障瞬态冲击特征。
传统的基于STFT、FIR滤波器以及第一代小波包变换的峭度图算法中存在时频分辨率取舍、运算效率以及小波函数库有限等缺点，本文提出了基于尺度自适应冗余提升小波包变换的峭度图算法，通过基于原始信号本身构造更新器和预测器来实现信号分解与重构的自适应功能，有效地提高了峭度图算法的自适应特性以及滤波后故障信号的准确性。随后利用时间尺度分解进一步增强了峭度图滤波后信号的瞬态冲击故障特征，并将基于固有时间尺度分解的Hilbert谱作为包络解调分析的有益补充，通过与Hilbert-Huang变换的对比验证了其在表征故障信号时频能量信息分布中的优越性。
旋转机械振动信号往往呈现循环平稳特性，本文将机械故障诊断领域中用于分析单通道实信号的循环平稳统计量理论进行了拓展，定义了能用于分析双通道融合复信号的低阶定向循环平稳统计量并提取出了滑动轴承油膜失稳故障信号中周期性时变特征以及转子振动状态信息等。通过对比分析揭示了传统全谱分析以及定向Wigner分布与定向循环统计量之间的内在联系，即全谱分析实质上就是一阶定向循环平稳统计量（定向循环均值），而定向Wigner分布实质上属于二阶定向循环统计量（定向循环谱相关函数）。此外试验结果分别对比分析了加速度振动信号与位移振动信号中所包含系统振动状态信息。
根据轴承振动监测需求分析，基于NI Labview平台开发了轴承振动信号测试、分析与特征提取于一体的虚拟式旋转机械轴承测试系统，为取得的理论创新成果在工程实践中的应用提供了强有力的支持。文章最后对全文工作及主要创新点进行了总结，并展望了后续的研究方向。
Bearings are one of the most widely used elements in the rotating machinery andtheir performance of great necessity to the operation of the mechanical equipment. Inorder to timely identify the deterioration in the condition of the bearings, it is significantto carry out the vibration based condition monitoring and fault diagnosis. In this thesis,the kurtogram based method is firstly developed and improved for the de-noising andfault characterization of the vibration signals buried in strong noise for the rollingelement bearings. Subsequently, the directional cyclostationarity analysis basedalgorithm is proposed to reveal significant features of the vibration signature for thejournal bearings with oil film instability faults. Finally, the integrated and virtualmeasurement and diagnosis system for the bearings of rotary machines is developed tofacilitate the practical application of the achievement in this thesis. The main researchesare listed as follows:
Firstly, the mathematical basis including the kurtosis statistics and spectral kurtosiscoefficients of the kurtogram is reviewed. Subsequently, the specific algorithm of theconventional kurtogram as well as the COT based order-kurtogram is introduced. It isstated that the kurtogram is able to de-noise and identify the optimal demodulation bandfor the following analysis of the vibration signal of the rolling element bearings. Sincethe traditional spectral kurtosis coefficient is easy to be influenced by the singularvalues in the original signal, the robust spectral kurtosis coefficients including theMoors spectral kurtosis, Hogg spectral kurtosis and the Crow-Siddiqui spectral kurtosisare defined. The robust kurtogram is also proposed to eliminate the interference of thesingular values. The simulation test and practical application has verified that theproposed robust kurtogram is able to improve the filtering effect and enhance theimpulsiveness of the fault signal.
Since the filtered signals will be inputted to the distribution of the spectral kurtosis,it is necessary to select the optimal filter in order to improve the ability of the kurtogram.The STFT suffers from the trade-off between the time resolution and frequencyresolution according to the Heisenberg uncertainty principle. The FIR filter-bank suffersfrom the low operation efficiency and WPT from the limit of the wavelet functionlibrary. Therefore, an improved kurtogram is proposed based on the scale-adaptiveredundant lifting wavelet packet transform which can construct the updater and predictor according to the signal itself as well as to adaptively decompose the originalsignal. The results have proved that the proposed method is able to improve theadaptivity of the conventional kurtogram and ensure the accuracy of the signal filteredby the kurtogram. Moreover, the intrisic time-scale decomposition based Hilbertspectrum has been proposed to supplement the envelope analysis. Compared with theHilbert-Huang transform, practical test has verified the superiority of the ITD basedHilbert spectrum when characterizing the distribution of the time-frequency energy ofthe fault signal.
Cyclostationarity defined to represent the correlation features of periodicphenomenon has been widely used to characterizing the fault vibration signal obtainedfrom the rotating machinery. Based on the conventional cyclic statistics, the directionalcyclic parameters including the directional cyclic mean, directional cyclic correlation,and directional spectral correlation density are defined to represent the complex-valuesignal integrated from double channel. It is found that the directional cyclic statistics areable to extract the periodically varying characteristics as well as the information relatedto the vibrating condition of the journal bearing supported rotor under oil film instabilityfault. In addition, the relationship between the conventional full spectrum, thedirectional Wigner distribution and the directional cyclic statistics is revealed. The fullspectrum can be interpreted as the first order cyclic statistics, namely the cyclic mean.The directional Wigner distribution belongs to the second order cyclic parameters(directional cyclic correlation function). Moreover, the acceleration signal basedcondition monitoring is compared with the displacement signal based conditionmonitoring when describing the system vibration.
According to the requirements analysis of the vibration based condition monitoringon bearings, the NI Labview based virtual measurement and diagnosis system for thebearings of rotary machines is developed. The system which is able to conduct dataacquisition, signal analysis and fault diagnosis has facilitated the practical application ofthe achievement in this thesis.
引文
[1]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.
[2]钟群鹏,张峥,有移亮.我国安全生产（含安全制造）的科学发展若干问题[J].机械工程学报,2007,43:7－18.
[3]王国彪,何正嘉,陈雪峰,赖一楠.机械故障诊断基础研究“何去何从”[J].机械工程学报,2013,49:63－72.
[4]中华人民共和国科学技术部.国家“十一五”科学技术发展规划[Z].2006.
[5]国家自然科学基金委员会工程与材料科学部.机械工程学科发展战略报告（2011-2020年）
[M].北京：科学出版社,2010.
[6]屈梁生,何正嘉著.机械故障诊断学[M].上海：上海科学技术出版社,1986.
[7]陈晖.基于多源信息融合的大型水压机故障诊断与状态评估研究[D].长沙：中南大学,2012.
[8]张键著.机械故障诊断技术[M].北京：机械工业出版社,2008.
[9]马伦,康建设,赵春宇,刘凯东.面向生产过程的CBM策略效益评估方法研究[J].计算机应用研究,2013,30:436－439.
[10]李莹,孟凡光.发电设备状态检修的收益分析.水利天地,2005,2:22
[11]范世东.船舶视情维修与检验的发展及实施对策[J].中国修船,2001,3:37－39.
[12] H.N. Aghjagan. Lubeoil analysis expert system [C]. Canadian maintenance engineeringconference, Toronto,1989:45－50.
[13]王英.设备状态维修系统结构与决策模型研究[D].哈尔滨：哈尔滨工业大学,2007.
[14]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.
[15] R.B. Randall. Vibration-based condition monitoring: industrial, aerospace and automotiveapplications [M]. John Wiley&Sons Ltd,2011.
[16] V. Leemans, M.F. Destain, B. Kilundu, P. Dehombreux. Evaluation of the performance ofinfrared thermography for on-line condition monitoring of rotating machines [J]. Engineering,2011,3:1030－1039.
[17] C.K. Tana, P. Irving, D. Mba. A comparative experimental study on the diagnostic andprognostic capabilities of acoustics emission [J]. Mechanical system and signal processing,2007,21:208－233.
[18]陈向民.基于形态分量分析和线性调频小波路径追踪的机械故障诊断方法研究[D].长沙：湖南大学,2013.
[19]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,2006.
[20]苏文胜.滚动轴承振动信号处理及特征提取方法研究[D].大连：大连理工大学,2010.
[21] P.G. Wheeler. Bearing analysis equipment keeps downtime down [J]. Plant Engineering,1968,25:87－89.
[22] R. Martin. Detection of ball bearing malfunctions [J]. Instruments and control systems,1970,12:79－82.
[23] D.R. Harting. Demodulated resonance analysis system. U.S. Patent3842,1974,663.
[24] D.R. Harting. Incipient failure detection by demodulated resonance analysis [J].Instrumentation Technology,1977,9:59－63.
[25] R.B. Randall，J. Antoni. Rolling element bearing diagnostics－A tutorial [J]. Mechanicalsystem and signal processing,2011,25:485－520.
[26] N. Sawalhi, R.B. Randall. Semi-automated bearing diagnostics-three case studies [J]. inProceedings of the Comadem Conference, Faro, Portugal, June,2007.
[27] K.R. Fyfe, E.D.S. Munck. Analysis of computed order tracking [J]. Mechanical system andsignal processing,1997,11:187－205.
[28] K.M. Bossley, R.J. Mckendrick. Hybrid computed order tracking [J]. Mechanical system andsignal processing,1999,13:627－641.
[29] K.S. Wang, P.S. Heyns. Application of computed order tracking, Vold-Kalman filtering andEMD in rotating machine vibration [J]. Mechanical system and signal processing,2011,25:416－430.
[30]郭瑜,秦树人,梁玉前.时频分析阶比跟踪技术[J].重庆大学学报（自然科学版）,2002,25:17－20.
[31]郭瑜,秦树人.旋转机械非稳定信号的伪转速跟踪阶比分析[J].振动与冲击,2004,23:61－64.
[32]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,2004,40:50－54.
[33]郭瑜,秦树人,汤宝平.基于分段重叠零相位滤波的阶比跟踪滤波法[J].振动工程学报,2003,16:399－403.
[34]刘小峰,柏林.角域AR谱技术在齿轮故障诊断中的应用[J].振动工程学报,2010,23:113－118.
[35] Xiaofeng Liu, Lin Bo, Chang Peng. Application of order cyclostationary demodulation todamage detection in a direct-driven wind turbine bearing [J]. Measurement science andtechnology,2014,25:1－8.
[36]陈向民,于德介,罗洁思.基于线调频小波路径追踪阶比循环平稳解调的齿轮故障诊断[J].机械工程学报,2012,48:95－101.
[37]陈向民,于德介等.基于线调频小波路径追踪阶比循能量解调的齿轮轮齿裂纹故障诊断[J].中国机械工程,2011,22:2598－2603.
[38] Z. Feng, M. Liang, F.L. Chu. Recent advances in time-frequency analysis methods formachinery fault diagnosis: a review with application examples [J]. Mechanical systems andsignal processing,2013,38:165－205.
[39] M.S. Kay, S.L. Marple. Spectrum analysis-a modern perspective [J]. Proceedings of IEEE,1981,69:1380－1419.
[40]李健宝,彭涛.基于AR模型和KFDA的滚动轴承故障诊断[J].华中科技大学学报,2009,37:213－216.
[41] D. Ho. Bearing diagnostics and self-adaptive noise cancellation [D]. New South Wales: TheUniversity of New South Wales,2000.
[42] D. Ho, R.B. Randall. Effects of time delay, order of fir filter and convergence factor onself-adaptive noise cancellation [C]. ICSV5, Adelaide,1997.
[43] J. Antoni, R.B. Randall. Optimisation of SANC for separating gear and bearing signals [C]. in:Proceedings of the Comadem Conference, Manchester,2001,89－96.
[44] G.K. Chaturvedi, D.W. Thomas. Bearing fault detection using adaptive noise cancelling [J].Journal of sound and vibration,1982,104:280－289.
[45] C.C. Tan, B. Dawson. An adaptive noise cancellation approach for condition monitoring ofgearbox bearings [C]. in: Proceedings of the International Tribology Conference, Melbourne,1987.
[46] P.D. McFadden. A revised model for the extraction of periodic waveforms by time domainaveraging [J]. Mechanical system and signal processing,1987,1:83－95.
[47] N. Sawalhi, R.B. Randall. Localised fault diagnosis in rolling element bearings in gearboxes[C]. in: Proceedings of the Fifth International Conference on Condition monitoring andMachinery Failure Prevention Technologies, Edinburgh,2008,15－18.
[48] S. Braun. The synchronous (time domain) average revisited [J]. Mechanical system and signalprocessing,2011,25:1087－1102.
[49]沈国际,陶利民,徐永成.时域同步平均的相位误差累积效应研究[J].振动工程学报,2007,20:335－339.
[50]邵毅敏,周晓君,欧家福.变周期信号的时域同步平均新算法[J].振动工程学报,2009,22:497－502.
[51]吴雪明,于德介,陈向民.基于自适应线调频基原子分解的时域同步平均方法及其在变转速齿轮故障诊断中的应用[J].中国机械工程,2012,23:1025－1211.
[52] N. Sawalhi, R.B. Randall, H. Endo. The enhancement of fault detection and diagnosis inrolling element bearings using minimum entropy deconvolution combined with spectralkurtosis [J]. Mechanical system and signal processing,2007,21:2616－2633.
[53]王宏超,陈进,董广明.基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报,2013,49:88－94.
[54]李峰,张英堂,王宁等.基于小波和奇异谱降噪理论的轴承故障诊断研究[J].轴承,2003,7:30－33.
[55]王太勇,王正英,胥永刚等.基于SVD降噪的经验模式分解及其工程应用[J].振动与冲击,2005,24:96－98.
[56]章立军,徐金梧等.自适应多尺度形态学分析及其在滚动轴承故障诊断中应用[J].北京科技大学学报,2008,30:441－445.
[57]唐英,孙巧.滚动轴承振动信号的小波奇异性故障检测研究[J].振动工程学报,2002,15:111－113.
[58]李富才,何正嘉,陈进.小波域相关滤波法及其早期故障预示应用[J].振动工程学报,2005,18:145－148.
[59] D.L. Donoho. De-noising by soft-thresholding [J]. IEEE Transaction on information theory,1995,41:613－627.
[60]藏玉萍,张德江,王维正.小波分层阈值降噪法及其在发动机振动信号分析中的应用[J].振动与冲击,2009,28:57－60.
[61] D. Dyer, R.M. Stewart. Detection of rolling element bearing damage by statistical vibrationanalysis [J]. ASME Transactions-Journal of mechanical design,1977,100:229－235.
[62] J. Antoni. The spectral kurtosis: a useful tool for characterizing nonstationary signals [J].Mechanical system and signal processing,2006,20:282－307.
[63] J. Antoni, R.B. Randall. The spectral kurtosis: application to the vibratory surveillance anddiagnostics of rotating machines [J]. Mechanical systems and signal processing,2006,2:308－331.
[64] J. Antoni. Fast computation of the kurtogram for the detection of transient faults [J].Mechanical systems and signal processing,2007,21:108－124.
[65] N. Sawalhi, R.B. Randall. Spectral kurtosis optimization for rolling element bearings [C]. In:Proceedings of the Eighth International Conference on Vibrations in Rotating Machinery,Sydney, August28-31,2005,839－842.
[66] Y.G. Lei, Jin Lin, Zhengjia He, Yanyang Zi. Application of an improved kurtogram method forfault diagnosis of rolling element bearings [J]. Mechanical system and signal processing,2011,25:1738－1749.
[67] J. Chen, Y. Zi, Z. He and J. Yuan. Improved spectral kurtosis with adaptive redundantmultiwavelet packet and its application for rotating machinery fault detection [J]. Measurementscience and technology,2012,23:1－15.
[68]蔡艳平,李艾华等.基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J].振动与冲击,2011,30:167－172.
[69]苏文胜,王奉涛等. EMD降噪与谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29:18－21.
[70]胡爱军,马万里,唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J].中国电机工程学报,2012,32:106－111.
[71]彭畅,柏林,谢小亮基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法[J].振动与冲击,2012,31:143－146.
[72]张志刚,石晓辉等.基于改进EMD和谱峭度法滚动轴承故障特征提取[J].振动、测试与诊断,2013,33:478－482.
[73]刘亭伟,郭瑜等.基于谱峭度的滚动轴承故障包络阶比跟踪分析[J].振动与冲击,2012,31:149－153.
[74]周智,朱永生等.基于MMSE和谱峭度的滚动轴承故障诊断方法[J].振动与冲击,2013,32:73－77.
[75]石林锁,沈金伟等.基于AR模型和谱峭度法的滚动轴承故障诊断[J].振动与冲击,2011,30:257－260.
[76]从飞云,陈进,董广明.基于谱峭度和AR模型的滚动轴承故障诊断[J].振动、测试与诊断,2012,32:538－541.
[77]石林锁,张亚洲,米文鹏.基于WVD的谱峭度法在轴承故障诊断中的应用[J].振动与冲击,2011,31:27－31.
[78]胡清忠,褚福磊.基于Hilbert解调的滚动轴承故障诊断分析[J].振动与冲击,2010,29:242－244.
[79]蔡艳平,李艾华等. Hilbert解调技术在柴油机失火故障诊断中的应用[J].机械科学与技术,2009,10:1349－1352.
[80]张帆,丁康.广义检波解调分析的三种算法及其局限性研究[J].振动工程学报,2002,2:123－124.
[81]鞠萍华,秦树人,赵玲.基于LMD的能量算子解调方法及其在故障特征信号提取中的应用[J].振动与冲击,2011,30:1－4.
[82]樊新海,何嘉武等.基于Teager能量算子的解调方法[J].装甲兵工程学院学报,2009,23:60－63.
[83]张文义,于德介,陈向民.基于信号共振稀疏分解与能量算子解调的轴承故障诊断方法[J].中国电机工程学报,2013,33:111－118.
[84]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海交通大学学报,2002,36:799－802.
[85]胥永刚,何正嘉,王太勇.基于经验模式分解的包络解调技术及其应用[J].西安交通大学学报,2004,38:1169－1172.
[86]刘小峰,彭永金,李慧.谐波小波解调法在齿轮箱故障诊断中的应用[J].重庆大学学报,2011,34:15－20.
[87]杨建刚,蔡霆等.汽轮机轴承动力学特性三维流固耦合分析模型.中国电机工程学报,2004,8:147－151.
[88]蔡林.流体动压滑动轴承静特性研究[D].哈尔滨：哈尔滨工程大学,2012.
[89]成大先著.机械设计手册-轴承[M].北京：化学工业出版社,2005.
[90]温诗铸等.摩擦学原理[M].北京：清华大学出版社,2005.
[91] A. Sommerfeld. Zur hydrodynamische theorie derschmiermittelreibung [J]. Zeit Math. Phys.,1904,50:97－155.
[92]张直明著.滑动轴承的流体动力润滑[M].北京：高等教育出版社,1982.
[93] B. Jakobsson, L. Floberg. Finite journal bearing, considering vaporization [J]. ChalmersTekniska Hogskolas-Handlingar,1957,190:1－116.
[94] L. Floberg. Boundary conditions of cavitation regions in journal bearings [J]. American societyof lubrication engineers-Transactions,1961,4:282－286.
[95] H.G. Elrod, M.L. Adams. A computer program for cavitation and starvation problems [C].Leeds-lyon
[96]王丽丽.高速滑动轴承的界面滑移及空穴机理研究[D].济南：山东大学,2012.
[97] A. Kumar, J.F. Booker. A finite element cavitation algorithm [J]. ASME Journal of tribology,1991,113:276－286.
[98] A.K. Singhal, H.Y. Li, et al. Mathmatical basis and validation of the full cavitation model [C].ASME FEDSM’01, New Orleans, Louisiana,2001.
[99] P.J. Zwart, A.G. Gerber, T. Belamri. A two-phase flow model for predicting cavitationdynamics [C]. In fifth international conference on multiphase flow, Yokohama, Japan,2004.
[100]G.H. Schnerr, J. Sauer. Physical and numerical modeling of unsteady cavitation dynamics [C].In fourth international conference on multiphase flow, New Orleans, USA,2001.
[101]J.D. Knight, P. Ghadimi. Analysis and observation of cavities in a journal bearing consideringflow continuity [J]. Tribology Transactions,2001,44:88－96.
[102]L. San Andres, O. de Santigo. Dynamic response of squeeze film dampers operating withbubbly mixtures [J]. ASME Journal of gas turbines and power,2002,408－415.
[103]M.L. Sun, Z.M. Zhang, et al. Experimental study of cavitation in an oscillatory oil squeeze film[J]. Tribology Transactions,2008,51:341－350.
[104]徐华,姜歌东等.四瓦可倾瓦径向滑动轴承动力特性的试验研究[J].摩擦学学报,2001,21:385－389.
[105]C. Lu, X. Ai, J. Li. Analysis and research on spiral oil wedge hydrodynamic bearing for precisemachine tool spindles [J]. Int. J. Mach. Tools Manufact.,1998,38:197－203.
[106]张文明,孟光,陈迪.微转子系统径向气体轴承特性[J].机械工程学报,2008,44:25－33.
[107]D.L. Cabrera, N.H. Woolley, D.R. Allanson. Film pressure distribution in water-lubricatedrubber journal bearings. Proc. IMechE. Part J: Engineering Tribology,2003,219:125－132.
[108]屈梁生著.机械故障的全息诊断原理[M].北京：科学出版社,2007.
[109]贺光明.滑动轴承油膜振荡及其在线消除研究[D].太原：太原理工大学,2011.
[110]A. Muszynska. Whirl and whip-rotor/bearing stability problems [J]. Journal of sound andvibration,1986,110:443－462.
[111]A. Muszynska. Stability of whirl and whip in rotor/bearing systems [J]. Journal of sound andvibration,1988,127:49－64.
[112]A. Zanarini, A. Cavallini. Experiencing rotor and fluid film bearing dynamics [J]. inProceedings of Quinta giornata di studio Ettore Funaioli,1998,23－38.
[113]J.C. Deepak, S.T. Noah. Experiment verification of sub-critical whirl bifurcation of a rotorsupported on a fluid-film bearing [J]. Transactions of ASME Journal of tribology,1998,120:605－609.
[114]S. Li, Q. Xu, X. Zhang. Nonlinear dynamic behaviors of rotor labyrinth seal system [J].Nonlinear Dynamics,2007,47:321－329.
[115]马辉,李辉等.两种不同载荷形式下转子系统油膜失稳的数值研究[J].振动工程学报,2013,26:105－111.
[116]张彦梅,陆启韶,张思进.一种非稳态油膜力模型下转子系统的碰摩分岔分析[J].振动工程学报,2002,15:68－72.
[117]马金奎.滑动轴承非线性轴心轨迹的瞬态与周期特性研究[D].济南：山东大学,2010.
[118]江志农,李艳妮.旋转机械轴心轨迹特征提取技术研究[J].振动、测试与诊断,2007,27：98－101.
[119]许飞云,钟秉林,黄仁.轴心轨迹自动识别及其在旋机诊断中的应用[J].振动、测试与诊断,2009,29：141－144.
[120]刘桂珍,于影等.转子系统非稳态油膜力的非线性分析[J].机床与液压,2013,41:31－34.
[121]王朝晖,张来斌,万波.基于轴心轨迹的滑动轴承运行稳定性综合评价方法[J].煤炭学报,2007,32:1328－1333.
[122]马金奎,路长厚,陈淑江.瞬变载荷作用下滑动轴承轴心轨迹计算仿真[J].振动、测试与诊断,2010,30:6－10.
[123]祝晓燕,常宏飞等.基于SVM与轴心轨迹不变矩的转子状态识别方法[J].汽轮机技术,2012,54:469－472.
[124]李舜酩.谐波小波包方法及其对转子亚频轴心轨迹的提取[J].机械工程学报,2004,40:133－137.
[125]张文斌,周晓军等.基于谐波窗方法的转子轴心轨迹提纯[J].振动与冲击,2009,28:74－77.
[126]陈仁祥,汤宝平,吕中亮. EEMD滤波的转子轴心轨迹提纯方法[J].重庆大学学报,2012,35:15－20.
[127]Don Southwick. Using full spectrum plots [J]. Orbit,1993,14:19－21.
[128]Don Southwick. Using full spectrum plots part2[J]. Orbit,1994,15:11－15.
[129]Fengqi Wu, G. Meng. Compound rub malfunctions feature extraction based on full spectrumcascade analysis and SVM [J]. Mechanical systems and signal processing,2006,20:2007－2021.
[130]N. Bachschmid, P. Pennacchi, A. Vania. Diagnostic significance of orbit shape analysis and itsapplication to improve machine fault detection [J]. Journal of Brazilian Society of MechanicalSciences and Engineering,2004,26:200－208.
[131]T.H. Patel, A.K. Darpe. Vibration response of a cracked rotor in presence of rotor-stator rub [J].Journal of sound and vibration,2008,317:841－865.
[132]吴峰崎,孟光,荆建平.基于加速度信号全谱分析的转子碰摩故障特征提取实验研究[J].振动与冲击,2006,25:44－47.
[133]吴峰崎,孟光.转子故障加速度信号的全谱分析[C].第十二届全国信号处理学术年会,苏州,2005,21:613－616.
[134]吴凝,丁康.全频谱进动分析原理及在故障诊断中的应用[C].2008年全国振动工程及应用学术会议暨第十一届全国设备故障诊断学术会议论文集,西宁,2008,27:170－172.
[135]屈梁生,邱海,徐光华.全息动平衡技术：原理与实践[J].中国机械工程,1988,9:60－63.
[136]刘石,屈梁生.全息谱技术在轴系现场动平衡方法中的应用[J].热能动力工程,2009,24:24－30.
[137]刘石,屈梁生.全息谱技术在现场动平衡前故障诊断中的应用[J].振动、测试与诊断,2004,24:269－274.
[138]徐宾刚,屈梁生.非对称转子的全息动平衡技术[J].西安交通大学学报,2000,34:50－54.
[139]刘淑莲,李强,郑水英.基于全息谱分析的非线性转子系统不平衡量识别[J].机械工程学报,2010,46:62－67.
[140]李宏坤,周帅等.基于经验模式分解的全息谱故障识别方法[J].振动、测试与诊断,2011,31:20－22.
[141]刘然,许宝杰.基于EMD和全息谱的设备故障诊断方法研究[J].机械科学与技术,2011,30:1922－1926.
[142]汤宝平,陈建波,章国稳.基于相位差校正法的全息谱研究[J].振动与冲击,2009,28:99－102.
[143]魏玉果,汤宝平等.基于阶比跟踪的全息谱技术[J].重庆大学学报（自然科学版）,2007,30:10－12.
[144]梅检民,陈祥龙等.基于稳健全息阶比谱提取微弱故障特征[J].振动与冲击,2012,31:49－52.
[145]刘石,屈梁生.回转机械故障诊断中的三维全息谱技术[J].西安交通大学学报,2004,38:899－903.
[146]王琇峰,屈梁生,廖与禾.三维全息谱在诊断负荷诱发的转子热弯曲与标高故障中的运用[J].热能动力工程,2008,23:462－467.
[147]史东峰,屈梁生.三维全息谱分解在回转机械诊断中的应用研究[J].西安交通大学学报,1998,32:94－97.
[148]韩捷,石来德著.全矢谱技术及其工程应用[M].北京：机械工业出版社,2008.
[149]韩捷,石来德.旋转机械的全信息能量谱分析方法研究[J].机械强度,2003,25:364－368.
[150]韩捷,董辛旻等.旋转机械同源数据全信息倒频谱分析与应用[J].机械强度,2005,27:452－455.
[151]巩晓赟,韩捷等.全矢小波包-包络分析方法及其在齿轮故障诊断中的应用[J].振动与冲击,2012,31:92－95.
[152]李凌均,韩捷等.矢双谱分析及其在机械故障诊断中的应用[J].机械工程学报,2012,31:92－95.
[153]马辉,李辉等.转子系统二阶油膜失稳故障仿真及试验研究[J].机械工程学报,2013,49:24－29.
[154]马辉,李辉等.两种转子-轴承系统模型的失稳故障仿真[J].中国工程机械学报,2013,11:340－346.
[155]H. Ma, et al. Time-frequency features of two types of coupled rub-impact faults in rotorsystems [J]. Journal of sound and vibration,2009,321:1109－1128.
[156]C. Lee, Y. Han. The directional wigner distribution and its application [J]. Journal of sound andvibration,1998,216:585－600.
[157]C. Lee, Y. Han. Directional wigner distribution for order analysis in rotating/reciprocatingmachines [J]. Mechanical systems and signal processing,1999,13:723－737.
[158]王丽雅,韩捷,陈磊.基于信息融合的短时矢功率谱分析方法[J].机械科学与技术,2007,26:437－440.
[159]腾山邦久著.声发射（AE）技术的运用[M].北京：冶金工业出版社,1996.
[160]A. Albers, W. Burger, et al. Monitoring lubrication regimes in sliding bearings-using acousticemission analysis [J]. Practical oil analysis magazine,2006, May:8－12.
[161]王晓伟,刘占生等.基于声发射的可倾瓦径向滑动轴承碰摩故障诊断[J].中国电机工程学报,2009,29:64－69.
[162]吴峰崎,孟光,荆建平.基于声信号三维谱分析的转子复合碰摩故障特征提取[J].振动与冲击,2005,24:79－84.
[163]秦萍,傅和平等.基于声发射监测的静载荷滑动轴承接触摩擦故障诊断的实验研究[J].摩擦学学报,2006,26:585－589.
[164]朱益军,李录平等.滑动轴承润滑状态与声发射信号特征关系研究[J].汽轮机技术,2011,53:50－55.
[165]T. Kim，H. White. On more robust estimation of skewness and kurtosis [J]. Finance ResearchLetters,2003,1:65－70.
[166]K. Pearson. Das Fehlergesetz und seine Verallgemeinerungen durch Fechner und Pearson [J]. ARejoinder. Biometrika,4:169－212.
[167]D. Ruppert. What is kurtosis?: An influence function approach [J]. The American StatisticalAssociation,1987,41:1－5.
[168]R.F. Dwyer. Detection of non-Gaussian signals by frequency domain kurtosis estimation [C]. in:Proceedings of the International Conference on Acoustics, Speech, and Signal Processing,Boston,1983,607－610.
[169]R.F. Dwyer. A technique for improving detection and estimation of signals contaminated byunder ice noise [J]. Journal of the Acoustical Society of America,1983,74:124－130.
[170]R.F. Dwyer. Use of the kurtosis statistics in the frequency domain as an aid in detecting randomsignals [J]. IEEE Journal of Oceanic Engineering,1984,9:85－92.
[171]C. Otonnello, S. Pagnan. Modified frequency domain kurtosis for signal processing [J].Electronics Letters,1994,30:1117－1118.
[172]S. Pagnan, et al.. Filtering of randomly occurring signals by kurtosis in frequency domain [C].Proceedings of the12thInternational Conference on Pattern Recognition,1994,3:131－133.
[173]V. Capdevielle, C. Servière, J.L. Lacoume. Blind separation of wide-band sources: applicationto rotating machine signals [C]. Proceedings of the Eighth European Signal ProcessingConference,1996,3:2085－2088.
[174]刘亭伟.基于谱峭度的齿轮箱故障特征提取[D].昆明：昆明理工大学,2011.
[175]J.J.A. Moors. The meaning of the kurtosis: Darlington reexamined [J]. The AmericanStatistician,1986,40:283－284.
[176]R.B. Darlington. Is kurtosis really ‘peakedness’?[J]. The American Statistician,1970,24:19－22.
[177]J.J.A. Moors. A quantile alternative for kurtosis [J]. The Statistician,1988,37:25－32.
[178]R.V. Hogg. More light on the kurtosis and related statistics [J]. Journal of the AmericanStatistical Association,1972,67:422－424.
[179]R.V. Hogg. Adaptive robust procedures: a partial review and some suggestions for futureapplications and theory [J]. Journal of the American Statistical Association,1974,69:909－923.
[180]E.L. Crow, M.M. Siddiqui. Robust estimation of location [J]. Journal of the AmericanStatistical Association,1967,62:353－389.
[181]Seeded fault test data [OL]. Bearing Data Center, Case Western Reserve University,2012,http://csegroups.case.edu/bearingdatacenter/home.
[182]高西全，丁玉美，阔永红著.数字信号处理：原理、实现及应用[M].北京：电子工业出版社，2006.
[183]L. Shi, R.B. Randall, J. Antoni. Rolling element bearing fault detection using improvedenvelope analysis [C]. In: Proceedings of the Eighth International Conference on Vibrations inRotating Machinery, Swansea, Wales, September7-9,2004,301－311.
[184]N. Sawalhi, R.B. Randall. Spectral kurtosis optimization for rolling element bearings [C]. In:Proceedings of the Eighth International Conference on Vibrations in Rotating Machinery,Sydney, August28-31,2005,839－842.
[185]M.G. Frei, I. Osorio. Intrinsic time-scale decomposition: time-frequency-energy analysis andreal-time filtering of non-stationary signals [J]. Proceedings of the royal society A,2007,463:321－342.
[186]秦毅.一体化小波分析仪的研究[D].重庆：重庆大学,2008.
[187]周瑞.基于第二代小波的机械故障信号处理方法研究[D].哈尔滨：哈尔滨工业大学,2009.
[188]S.-F. Lei and R.P. Hamernik. Wavelet transform and frequency domain kurtosis: application toassessment of hearing hazard from noise exposure [C]. Applications of signal processing toaudio and acoustics, IEEE ASSP Workshop on,1995,19－21.
[189]S.-F. Lei, W.A. Ahroon and R.P. Hamernik. The application of frequency and time domainkurtosis to the assessment of hazardous noise exposures [J]. The journal of the acousticalsociety of America,1994,96:1435－1444.
[190]J. Lin and M.J. Zuo. Gearbox fault diagnosis using adaptive wavelet filter [J]. Mechanicalsystems and signal processing,2003,17:1259－1269.
[191]石林锁.滚动轴承故障检测的改进包络分析法[J].轴承,2006,2:36－39.
[192]W. Sweldens. The lifting scheme: A custom-design construction of biorthogonal wavelets [J].Applied and computational harmonic analysis,1996,3:186－200.
[193]W. Sweldens. The lifting scheme: A construction of second generation wavelets [J]. SIAMjournal of mathematical analysis,1998,29:511－546.
[194]R.L. Claypoole, R.G. Baraniuk, R.D. Nowak. Adaptive wavelet transforms via lifting [C].IEEE International conference on Acoustics, Speech and Signal processing,1998,3:1513－1516.
[195]C.S. Lee, C.K. Lee, K.Y. Yoo. New lifting based structure for undecimated wavelet transform[J]. Electronics Letters,2000,36:1894－1895.
[196]H.K. Jiang, Z. He, C. Duan and P. Chen. Gearbox fault diagnosis using adaptive redundantlifting scheme [J]. Mechanical systems and signal processing,2006,20:1992－2006.
[197]姜洪开,王仲生.第二代小波包构造及发动机微弱损伤识别[J].北京航空航天大学学报,2007,33:777－780.
[198]姜洪开,王仲生,何正嘉.基于自适应提升小波包的故障微弱信号特征早期识别[J].西北工业大学学报,2008,26:99－103.
[199]R. Zhou, W. Bao, N. Li, X. Huang, D.R. Yu. Mechanical equipment fault diagnosis based onredundant second generation wavelet packet transform [J]. Digital signal processing,2010,20:276－288.
[200]N.E. Huang, Z. Shen, S.R. Long, et al. The empirical mode decomposition and the Hilbertspectrum for the nonlinear and non-stationary time series analysis [J]. Proceedings of the RoyalSociety of London, Series A,1988,454:903－995.
[201]Z. Wu, N.E. Huang. Ensemble empirical mode decomposition: a noise-assisted data analysismethod [J]. Advances in adaptive data analysis,2009,1:1－41.
[202]Z.K. Peng, P.W. Tse, F.L. Chu. A comparison study of improved Hilbert-Huang transform andwavelet transform: application to fault diagnosis for rolling bearing [J]. Mechanical systemsand signal processing,2005,19:974－988.
[203]Z.K. Peng, P.W. Tse, F.L. Chu. An improved Hilbert-Huang transform and its application invibration signal analysis [J]. Journal of sound and vibration,2005,286:187－205.
[204]Y. Qin, S. Qin, Y. Mao. Research on iterated Hilbert transform and its application inmechanical fault diagnosis [J]. Mechanical systems and signal processing,2008,22:1967－1980.
[205]N.E. Huang, Z. Wu. A review on Hilbert-Huang transform: method and its applications togeophysical studies [J]. Reviews of Geophysics,2008,46:1－23.
[206]褚福磊,彭志科著.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.
[207]W.R. Bennett. Statistics of regenerative digital transmission [J]. The Bell system technicaljournal,1958,37:1501－1542.
[208]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,2007.
[209]W.A. Gardner, L. Franks. Characterization of cyclostationary random signal processes [J].IEEE transactions on information theory,1975,21:4－14.
[210]W.A. Gardner. Stationarizable random processes [J]. IEEE transactions on information theory,1978,24:8－22.
[211]W.A. Gardner. Spectral correlation of modulated signals: part I-analog modulation [J]. IEEEtransactions on communication,1987,35:584－594.
[212]W.A. Gardner. Signal interception: a unifying theoretical framework for feature detection [J].IEEE transactions on communication,1988,8:897－906.
[213]W.A. Gardner. Exploitation of spectral redundancy in cyclostationary signals [J]. IEEE signalprocessing magazine,1991,8:14－36.
[214]C.M. Spooner, W.A. Gardner. The cumulant theory of cyclostationary time-series: part Ⅱ:development and applications [J]. IEEE transactions on signal processing,1994,42:3409－3429.
[215]W.A. Gardner. Introduction to random processing with applications to signals and systemsSecond edition [M]. New York: McGraw-Hill,1990.
[216]W.A. Gardner. Statistical spectral analysis: a non-probabilistic theory [M]. Englewood Cliffs,NJ: Prentice-Hall,1987.
[217]A.V. Dandawate, G.B. Giannakis. Polyspectral analysis of nonstationary signals: Systemidentification, classification and ambiguity functions [C]. In Processing Int. Signal ProcessingWorkshop Higher Order Statistics,1991:147－150.
[218]G.B. Giannakis., A.V. Dandawate. Polyspectral analysis of non-stationary signals: Bases,consistency and HOS-WV [C]. In Processing Int. Signal Processing Workshop Higher OrderStatistics,1991:167－170.
[219]A.V. Dandawate, G.B. Giannakis. Ergodicity results for non-stationary signals: cumulants,ambiguity functions and wavelets [C]. In processing25thAnnual Conference InformationScience, System,1991:976－983.
[220]A.V. Dandawate, G.B. Giannakis. Nonparametric polyspectral analysis of AM signals andprocesses with missing observations [C]. In processing26thAnnual Conference InformationScience, System,1992:603－605.
[221]A.V. Dandawate, G.B. Giannakis. Cyclic-cumulant based identification of almost periodicallytime-varying systems: Parametric methods [C]. In Processing Int. Conference, ASSP,1992:229－232.
[222]G.B. Giannakis. Digital signal processing handbook: Cyclostationary signal analysis [M]. CRCPress&IEEE Press,1998.
[223]C. Capdessus, M. Sidahmed, J.L. Lacoume. Cyclostationary processes: application in gearfaults early diagnosis [J]. Mechanical systems and signal processing,2000,14:371－385.
[224]C. Capdessus, M. Sidahmed, J.L. Lacoume. Cyclostationary processes: application in gearfaults early diagnosis [J]. Mechanical systems and signal processing,2000,14:371－385.
[225]J. Antoni. Cyclostationarity by examples [J]. Mechanical systems and signal processing,2009,23:987－1036.
[226]J. Antoni, D. Hanson, R.B. Randall. Combining cyclostationarity and the frequency domainsubspace technique for operational modal analysis [J]. Key engineering materials,2007,347:473－478.
[227]J. Antoni, R. Boustany. Advanced rolling element bearing diagnostics based on cyclic spectralanalysis [J]. Key engineering materials,2007,347:265－270.
[228]R.B. Randall, J. Antoni, S. Chobsaard. The relationship between spectral correlation andenvelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals[J]. Mechanical systems and signal processing,2001,15:945－962.
[229]J. Antoni, F. Bonnardot, A. Raad, M. EI Badaoui. Cyclostationary modelling of rotatingmachine vibration signals [J]. Mechanical systems and signal processing,2004,18:1285－1314.
[230]J. Antoni. Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions [J].Journal of sound and vibration,2007,497－529.
[231]J. Antoni. Cyclic spectral analysis in practice [J]. Mechanical systems and signal processing,2007,21:597－630.
[232]R. Boustany, J. Antoni. A subspace method for the blind extraction of a cyclostationary source:Application to rolling element bearing diagnostics [J]. Mechanical systems and signalprocessing,2005,19:1245－1259.
[233]R.B. Randall, J. Antoni, S. Chobsaard. The relationship between spectral correlation andenvelope analysis for cyclostationary machine signals, application to ball bearing diagnostics[J]. Mechanical systems and signal processing,2001,19:945－962.
[234]J. Antoni, R.B. Randall. On the use of the cyclic power spectrum in rolling element bearingsdiagnostics [J]. Journal of sound and vibration,2005,281:463－468.
[235]李力,屈梁生.循环统计量方法在滚动轴承故障诊断中的应用[J].振动、测试与诊断,2003,23:116－119.
[236]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,2002,36:943－946.
[237]李力,屈梁生.循环域解调方法在滚动轴承故障诊断中的应用[J].轴承,2003,10:33－36.
[238]李力,屈梁生.谱相关特性在机械信号特征提取中的应用研究[J].中国机械工程,2006,2:334－338.
[239]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.
[240]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海交通大学学报,2002,36:43－46.
[241]姜鸣,陈进,秦恺.时变调幅信号的循环平稳特征讨论和应用[J].上海交通大学学报,2001,35:1798－1801.
[242]姜鸣,陈进,秦恺,汪慰军.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,2001,14:424－428.
[243]F. Zhou, Jin Chen, J. He, et al. Cyclic statistics based neural network for early fault diagnosisof rolling element bearings [J]. Lecture notes in computer science,2004,3174:595－600.
[244]周福昌,陈进等.循环平稳信号处理在机械设备故障诊断中的应用综述[J].振动与冲击,2006,25:148－152.
[245]周福昌,陈进等.基于小波分析与循环平稳度分析的滚动轴承早期故障诊断方法[J].振动与冲击,2006,25:91－93.
[246]Guo Bi, Jin Chen, et al. Application of degree of cyclostationarity in rolling element bearingdiagnosis [J]. Key engineering materials,2005,293-294:347－354.
[247]Guo Bi, Jin Chen, et al. Application of slice spectral correlation density to gear defect detection[J]. Proc. IMechE Part C: J. Mechanical engineering science,2006,220:1385－1392.
[248]毕果,陈进等.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,2006,19:388－393.
[249]毕果,陈进等.谱相关密度切片分析在滚动轴承故障诊断中的应用[J].振动与冲击,2006,25:280－282.
[250]何俊.循环平稳和解调频技术在故障诊断中的研究和应用[D].上海：上海交通大学,2007.
[251]Jun He, Jin Chen, et al. Frequency-demodulated analysis based on cyclostationarity for faultdetection in gears [J]. Key engineering materials,2005,293-294:87－94.
[252]何俊,陈进等.循环自相关函数及其切片的解调原理分析[J].振动工程学报,2004,17:359－362.
[253]周宇.基于循环平稳信号二维平面表示的滚动轴承早期故障诊断方法研究[D].上海：上海交通大学,2012.
[254]Yu Zhou, Jin Chen, et al. Application of the horizontal slice of cyclic bispectrum in rollingelement bearings diagnosis [J]. Mechanical systems and signal processing,2012,26:229－243.
[255]Yu Zhou, Jin Chen, et al. Wigner-Ville distribution based on cyclic spectral density and theapplication in rolling element bearings diagnosis [J]. Proc. IMechE Part C: J. Mechanicalengineering science,2011,225:2831－2847.
[256]周宇,陈进等.基于循环双谱的滚动轴承故障诊断[J].振动与冲击,2012,31:78－81.
[257]周宇,陈进等.基于循环双谱二次切片分析的滚动轴承故障诊断研究[J].振动与冲击,2012,31:24－28.
[258]明阳.基于循环平稳和盲源分离的滚动轴承故障特征提取方法研究[D].上海：上海交通大学,2013.
[259]Yang Ming, Jin Chen, G. Dong. Weak fault feature extraction of rolling bearing based on cyclicWiener filter and envelope spectrum [J]. Mechanical systems and signal processing,2011,25:1773－1785.
[260]Yang Ming, Jin Chen, G. Dong. Application of convolved blind separation based onsecond-order cyclic statistics in rolling bearing feature extraction [J]. Journal of vibration andcontrol,2012,1－17.
[261]A.V. Dandawate. Exploiting cyclostationarity and higher-order statistics in signal processing
[D]. Charlottesville, Virginia: University of Virginia,1993.
[262]J.W. Lund. Spring and damping coefficients for the tilting-pad journal bearing [J]. ASLETransactions,1964,7:342－352.
[263]N. Feng, E. Hahn, R.B. Randall. Using transient analysis software to simulate vibration signalsdue to rolling element bearing defects [C]. in Proceedings of the3rdAustralian Congress onApplied Mechanics,2002,689－694.
[264]National Instruments. http://www.ni.com [DB/OL].2014.
[265]B&K. http://www.bksv.com [DB/OL].2014.
[266]柏林.网络化虚拟仪器核心技术及其典型案例的研究[D].重庆：重庆大学,2004.
[267]Bently Nevada. http://www.ge-mcs.com/en/bently-nevada.html [DB/OL].2014.
[268]尹爱军等著.秦氏模型-基于智能虚拟控件的仪器（第二版）[M].北京：科学出版社,2008.
[269]秦树人.岩石模型：机械系统大型测试仪器库[J].机械工程学报,2007,43:68－74.
[270]秦毅.一体化小波分析仪的研究[D].重庆：重庆大学,2008.
[271]刘文艺.风电机组振动监测与故障诊断研究[D].重庆：重庆大学,2010.
[272]董绍江.基于优化支持向量机的空间滚动轴承寿命预测方法研究[D].重庆：重庆大学,2012.
[273]陈仁祥.振动谱表征空间滚动轴承寿命状态方法研究[D].重庆：重庆大学,2012.
[274]API Standard670: Machinery protection systems,4thedition [S].2000.
[275]ISO10816-1: Mechanical vibration-Evaluation of machine vibration by measurements onnon-rotating parts Part1: General guidelines [S].1995.
[2]钟群鹏,张峥,有移亮.我国安全生产（含安全制造）的科学发展若干问题[J].机械工程学报,2007,43:7－18.
[3]王国彪,何正嘉,陈雪峰,赖一楠.机械故障诊断基础研究“何去何从”[J].机械工程学报,2013,49:63－72.
[4]中华人民共和国科学技术部.国家“十一五”科学技术发展规划[Z].2006.
[5]国家自然科学基金委员会工程与材料科学部.机械工程学科发展战略报告（2011-2020年）
[M].北京：科学出版社,2010.
[6]屈梁生,何正嘉著.机械故障诊断学[M].上海：上海科学技术出版社,1986.
[7]陈晖.基于多源信息融合的大型水压机故障诊断与状态评估研究[D].长沙：中南大学,2012.
[8]张键著.机械故障诊断技术[M].北京：机械工业出版社,2008.
[9]马伦,康建设,赵春宇,刘凯东.面向生产过程的CBM策略效益评估方法研究[J].计算机应用研究,2013,30:436－439.
[10]李莹,孟凡光.发电设备状态检修的收益分析.水利天地,2005,2:22
[11]范世东.船舶视情维修与检验的发展及实施对策[J].中国修船,2001,3:37－39.
[12] H.N. Aghjagan. Lubeoil analysis expert system [C]. Canadian maintenance engineeringconference, Toronto,1989:45－50.
[13]王英.设备状态维修系统结构与决策模型研究[D].哈尔滨：哈尔滨工业大学,2007.
[14]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.
[15] R.B. Randall. Vibration-based condition monitoring: industrial, aerospace and automotiveapplications [M]. John Wiley&Sons Ltd,2011.
[16] V. Leemans, M.F. Destain, B. Kilundu, P. Dehombreux. Evaluation of the performance ofinfrared thermography for on-line condition monitoring of rotating machines [J]. Engineering,2011,3:1030－1039.
[17] C.K. Tana, P. Irving, D. Mba. A comparative experimental study on the diagnostic andprognostic capabilities of acoustics emission [J]. Mechanical system and signal processing,2007,21:208－233.
[18]陈向民.基于形态分量分析和线性调频小波路径追踪的机械故障诊断方法研究[D].长沙：湖南大学,2013.
[19]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,2006.
[20]苏文胜.滚动轴承振动信号处理及特征提取方法研究[D].大连：大连理工大学,2010.
[21] P.G. Wheeler. Bearing analysis equipment keeps downtime down [J]. Plant Engineering,1968,25:87－89.
[22] R. Martin. Detection of ball bearing malfunctions [J]. Instruments and control systems,1970,12:79－82.
[23] D.R. Harting. Demodulated resonance analysis system. U.S. Patent3842,1974,663.
[24] D.R. Harting. Incipient failure detection by demodulated resonance analysis [J].Instrumentation Technology,1977,9:59－63.
[25] R.B. Randall，J. Antoni. Rolling element bearing diagnostics－A tutorial [J]. Mechanicalsystem and signal processing,2011,25:485－520.
[26] N. Sawalhi, R.B. Randall. Semi-automated bearing diagnostics-three case studies [J]. inProceedings of the Comadem Conference, Faro, Portugal, June,2007.
[27] K.R. Fyfe, E.D.S. Munck. Analysis of computed order tracking [J]. Mechanical system andsignal processing,1997,11:187－205.
[28] K.M. Bossley, R.J. Mckendrick. Hybrid computed order tracking [J]. Mechanical system andsignal processing,1999,13:627－641.
[29] K.S. Wang, P.S. Heyns. Application of computed order tracking, Vold-Kalman filtering andEMD in rotating machine vibration [J]. Mechanical system and signal processing,2011,25:416－430.
[30]郭瑜,秦树人,梁玉前.时频分析阶比跟踪技术[J].重庆大学学报（自然科学版）,2002,25:17－20.
[31]郭瑜,秦树人.旋转机械非稳定信号的伪转速跟踪阶比分析[J].振动与冲击,2004,23:61－64.
[32]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,2004,40:50－54.
[33]郭瑜,秦树人,汤宝平.基于分段重叠零相位滤波的阶比跟踪滤波法[J].振动工程学报,2003,16:399－403.
[34]刘小峰,柏林.角域AR谱技术在齿轮故障诊断中的应用[J].振动工程学报,2010,23:113－118.
[35] Xiaofeng Liu, Lin Bo, Chang Peng. Application of order cyclostationary demodulation todamage detection in a direct-driven wind turbine bearing [J]. Measurement science andtechnology,2014,25:1－8.
[36]陈向民,于德介,罗洁思.基于线调频小波路径追踪阶比循环平稳解调的齿轮故障诊断[J].机械工程学报,2012,48:95－101.
[37]陈向民,于德介等.基于线调频小波路径追踪阶比循能量解调的齿轮轮齿裂纹故障诊断[J].中国机械工程,2011,22:2598－2603.
[38] Z. Feng, M. Liang, F.L. Chu. Recent advances in time-frequency analysis methods formachinery fault diagnosis: a review with application examples [J]. Mechanical systems andsignal processing,2013,38:165－205.
[39] M.S. Kay, S.L. Marple. Spectrum analysis-a modern perspective [J]. Proceedings of IEEE,1981,69:1380－1419.
[40]李健宝,彭涛.基于AR模型和KFDA的滚动轴承故障诊断[J].华中科技大学学报,2009,37:213－216.
[41] D. Ho. Bearing diagnostics and self-adaptive noise cancellation [D]. New South Wales: TheUniversity of New South Wales,2000.
[42] D. Ho, R.B. Randall. Effects of time delay, order of fir filter and convergence factor onself-adaptive noise cancellation [C]. ICSV5, Adelaide,1997.
[43] J. Antoni, R.B. Randall. Optimisation of SANC for separating gear and bearing signals [C]. in:Proceedings of the Comadem Conference, Manchester,2001,89－96.
[44] G.K. Chaturvedi, D.W. Thomas. Bearing fault detection using adaptive noise cancelling [J].Journal of sound and vibration,1982,104:280－289.
[45] C.C. Tan, B. Dawson. An adaptive noise cancellation approach for condition monitoring ofgearbox bearings [C]. in: Proceedings of the International Tribology Conference, Melbourne,1987.
[46] P.D. McFadden. A revised model for the extraction of periodic waveforms by time domainaveraging [J]. Mechanical system and signal processing,1987,1:83－95.
[47] N. Sawalhi, R.B. Randall. Localised fault diagnosis in rolling element bearings in gearboxes[C]. in: Proceedings of the Fifth International Conference on Condition monitoring andMachinery Failure Prevention Technologies, Edinburgh,2008,15－18.
[48] S. Braun. The synchronous (time domain) average revisited [J]. Mechanical system and signalprocessing,2011,25:1087－1102.
[49]沈国际,陶利民,徐永成.时域同步平均的相位误差累积效应研究[J].振动工程学报,2007,20:335－339.
[50]邵毅敏,周晓君,欧家福.变周期信号的时域同步平均新算法[J].振动工程学报,2009,22:497－502.
[51]吴雪明,于德介,陈向民.基于自适应线调频基原子分解的时域同步平均方法及其在变转速齿轮故障诊断中的应用[J].中国机械工程,2012,23:1025－1211.
[52] N. Sawalhi, R.B. Randall, H. Endo. The enhancement of fault detection and diagnosis inrolling element bearings using minimum entropy deconvolution combined with spectralkurtosis [J]. Mechanical system and signal processing,2007,21:2616－2633.
[53]王宏超,陈进,董广明.基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报,2013,49:88－94.
[54]李峰,张英堂,王宁等.基于小波和奇异谱降噪理论的轴承故障诊断研究[J].轴承,2003,7:30－33.
[55]王太勇,王正英,胥永刚等.基于SVD降噪的经验模式分解及其工程应用[J].振动与冲击,2005,24:96－98.
[56]章立军,徐金梧等.自适应多尺度形态学分析及其在滚动轴承故障诊断中应用[J].北京科技大学学报,2008,30:441－445.
[57]唐英,孙巧.滚动轴承振动信号的小波奇异性故障检测研究[J].振动工程学报,2002,15:111－113.
[58]李富才,何正嘉,陈进.小波域相关滤波法及其早期故障预示应用[J].振动工程学报,2005,18:145－148.
[59] D.L. Donoho. De-noising by soft-thresholding [J]. IEEE Transaction on information theory,1995,41:613－627.
[60]藏玉萍,张德江,王维正.小波分层阈值降噪法及其在发动机振动信号分析中的应用[J].振动与冲击,2009,28:57－60.
[61] D. Dyer, R.M. Stewart. Detection of rolling element bearing damage by statistical vibrationanalysis [J]. ASME Transactions-Journal of mechanical design,1977,100:229－235.
[62] J. Antoni. The spectral kurtosis: a useful tool for characterizing nonstationary signals [J].Mechanical system and signal processing,2006,20:282－307.
[63] J. Antoni, R.B. Randall. The spectral kurtosis: application to the vibratory surveillance anddiagnostics of rotating machines [J]. Mechanical systems and signal processing,2006,2:308－331.
[64] J. Antoni. Fast computation of the kurtogram for the detection of transient faults [J].Mechanical systems and signal processing,2007,21:108－124.
[65] N. Sawalhi, R.B. Randall. Spectral kurtosis optimization for rolling element bearings [C]. In:Proceedings of the Eighth International Conference on Vibrations in Rotating Machinery,Sydney, August28-31,2005,839－842.
[66] Y.G. Lei, Jin Lin, Zhengjia He, Yanyang Zi. Application of an improved kurtogram method forfault diagnosis of rolling element bearings [J]. Mechanical system and signal processing,2011,25:1738－1749.
[67] J. Chen, Y. Zi, Z. He and J. Yuan. Improved spectral kurtosis with adaptive redundantmultiwavelet packet and its application for rotating machinery fault detection [J]. Measurementscience and technology,2012,23:1－15.
[68]蔡艳平,李艾华等.基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J].振动与冲击,2011,30:167－172.
[69]苏文胜,王奉涛等. EMD降噪与谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29:18－21.
[70]胡爱军,马万里,唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J].中国电机工程学报,2012,32:106－111.
[71]彭畅,柏林,谢小亮基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法[J].振动与冲击,2012,31:143－146.
[72]张志刚,石晓辉等.基于改进EMD和谱峭度法滚动轴承故障特征提取[J].振动、测试与诊断,2013,33:478－482.
[73]刘亭伟,郭瑜等.基于谱峭度的滚动轴承故障包络阶比跟踪分析[J].振动与冲击,2012,31:149－153.
[74]周智,朱永生等.基于MMSE和谱峭度的滚动轴承故障诊断方法[J].振动与冲击,2013,32:73－77.
[75]石林锁,沈金伟等.基于AR模型和谱峭度法的滚动轴承故障诊断[J].振动与冲击,2011,30:257－260.
[76]从飞云,陈进,董广明.基于谱峭度和AR模型的滚动轴承故障诊断[J].振动、测试与诊断,2012,32:538－541.
[77]石林锁,张亚洲,米文鹏.基于WVD的谱峭度法在轴承故障诊断中的应用[J].振动与冲击,2011,31:27－31.
[78]胡清忠,褚福磊.基于Hilbert解调的滚动轴承故障诊断分析[J].振动与冲击,2010,29:242－244.
[79]蔡艳平,李艾华等. Hilbert解调技术在柴油机失火故障诊断中的应用[J].机械科学与技术,2009,10:1349－1352.
[80]张帆,丁康.广义检波解调分析的三种算法及其局限性研究[J].振动工程学报,2002,2:123－124.
[81]鞠萍华,秦树人,赵玲.基于LMD的能量算子解调方法及其在故障特征信号提取中的应用[J].振动与冲击,2011,30:1－4.
[82]樊新海,何嘉武等.基于Teager能量算子的解调方法[J].装甲兵工程学院学报,2009,23:60－63.
[83]张文义,于德介,陈向民.基于信号共振稀疏分解与能量算子解调的轴承故障诊断方法[J].中国电机工程学报,2013,33:111－118.
[84]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海交通大学学报,2002,36:799－802.
[85]胥永刚,何正嘉,王太勇.基于经验模式分解的包络解调技术及其应用[J].西安交通大学学报,2004,38:1169－1172.
[86]刘小峰,彭永金,李慧.谐波小波解调法在齿轮箱故障诊断中的应用[J].重庆大学学报,2011,34:15－20.
[87]杨建刚,蔡霆等.汽轮机轴承动力学特性三维流固耦合分析模型.中国电机工程学报,2004,8:147－151.
[88]蔡林.流体动压滑动轴承静特性研究[D].哈尔滨：哈尔滨工程大学,2012.
[89]成大先著.机械设计手册-轴承[M].北京：化学工业出版社,2005.
[90]温诗铸等.摩擦学原理[M].北京：清华大学出版社,2005.
[91] A. Sommerfeld. Zur hydrodynamische theorie derschmiermittelreibung [J]. Zeit Math. Phys.,1904,50:97－155.
[92]张直明著.滑动轴承的流体动力润滑[M].北京：高等教育出版社,1982.
[93] B. Jakobsson, L. Floberg. Finite journal bearing, considering vaporization [J]. ChalmersTekniska Hogskolas-Handlingar,1957,190:1－116.
[94] L. Floberg. Boundary conditions of cavitation regions in journal bearings [J]. American societyof lubrication engineers-Transactions,1961,4:282－286.
[95] H.G. Elrod, M.L. Adams. A computer program for cavitation and starvation problems [C].Leeds-lyon
[96]王丽丽.高速滑动轴承的界面滑移及空穴机理研究[D].济南：山东大学,2012.
[97] A. Kumar, J.F. Booker. A finite element cavitation algorithm [J]. ASME Journal of tribology,1991,113:276－286.
[98] A.K. Singhal, H.Y. Li, et al. Mathmatical basis and validation of the full cavitation model [C].ASME FEDSM’01, New Orleans, Louisiana,2001.
[99] P.J. Zwart, A.G. Gerber, T. Belamri. A two-phase flow model for predicting cavitationdynamics [C]. In fifth international conference on multiphase flow, Yokohama, Japan,2004.
[100]G.H. Schnerr, J. Sauer. Physical and numerical modeling of unsteady cavitation dynamics [C].In fourth international conference on multiphase flow, New Orleans, USA,2001.
[101]J.D. Knight, P. Ghadimi. Analysis and observation of cavities in a journal bearing consideringflow continuity [J]. Tribology Transactions,2001,44:88－96.
[102]L. San Andres, O. de Santigo. Dynamic response of squeeze film dampers operating withbubbly mixtures [J]. ASME Journal of gas turbines and power,2002,408－415.
[103]M.L. Sun, Z.M. Zhang, et al. Experimental study of cavitation in an oscillatory oil squeeze film[J]. Tribology Transactions,2008,51:341－350.
[104]徐华,姜歌东等.四瓦可倾瓦径向滑动轴承动力特性的试验研究[J].摩擦学学报,2001,21:385－389.
[105]C. Lu, X. Ai, J. Li. Analysis and research on spiral oil wedge hydrodynamic bearing for precisemachine tool spindles [J]. Int. J. Mach. Tools Manufact.,1998,38:197－203.
[106]张文明,孟光,陈迪.微转子系统径向气体轴承特性[J].机械工程学报,2008,44:25－33.
[107]D.L. Cabrera, N.H. Woolley, D.R. Allanson. Film pressure distribution in water-lubricatedrubber journal bearings. Proc. IMechE. Part J: Engineering Tribology,2003,219:125－132.
[108]屈梁生著.机械故障的全息诊断原理[M].北京：科学出版社,2007.
[109]贺光明.滑动轴承油膜振荡及其在线消除研究[D].太原：太原理工大学,2011.
[110]A. Muszynska. Whirl and whip-rotor/bearing stability problems [J]. Journal of sound andvibration,1986,110:443－462.
[111]A. Muszynska. Stability of whirl and whip in rotor/bearing systems [J]. Journal of sound andvibration,1988,127:49－64.
[112]A. Zanarini, A. Cavallini. Experiencing rotor and fluid film bearing dynamics [J]. inProceedings of Quinta giornata di studio Ettore Funaioli,1998,23－38.
[113]J.C. Deepak, S.T. Noah. Experiment verification of sub-critical whirl bifurcation of a rotorsupported on a fluid-film bearing [J]. Transactions of ASME Journal of tribology,1998,120:605－609.
[114]S. Li, Q. Xu, X. Zhang. Nonlinear dynamic behaviors of rotor labyrinth seal system [J].Nonlinear Dynamics,2007,47:321－329.
[115]马辉,李辉等.两种不同载荷形式下转子系统油膜失稳的数值研究[J].振动工程学报,2013,26:105－111.
[116]张彦梅,陆启韶,张思进.一种非稳态油膜力模型下转子系统的碰摩分岔分析[J].振动工程学报,2002,15:68－72.
[117]马金奎.滑动轴承非线性轴心轨迹的瞬态与周期特性研究[D].济南：山东大学,2010.
[118]江志农,李艳妮.旋转机械轴心轨迹特征提取技术研究[J].振动、测试与诊断,2007,27：98－101.
[119]许飞云,钟秉林,黄仁.轴心轨迹自动识别及其在旋机诊断中的应用[J].振动、测试与诊断,2009,29：141－144.
[120]刘桂珍,于影等.转子系统非稳态油膜力的非线性分析[J].机床与液压,2013,41:31－34.
[121]王朝晖,张来斌,万波.基于轴心轨迹的滑动轴承运行稳定性综合评价方法[J].煤炭学报,2007,32:1328－1333.
[122]马金奎,路长厚,陈淑江.瞬变载荷作用下滑动轴承轴心轨迹计算仿真[J].振动、测试与诊断,2010,30:6－10.
[123]祝晓燕,常宏飞等.基于SVM与轴心轨迹不变矩的转子状态识别方法[J].汽轮机技术,2012,54:469－472.
[124]李舜酩.谐波小波包方法及其对转子亚频轴心轨迹的提取[J].机械工程学报,2004,40:133－137.
[125]张文斌,周晓军等.基于谐波窗方法的转子轴心轨迹提纯[J].振动与冲击,2009,28:74－77.
[126]陈仁祥,汤宝平,吕中亮. EEMD滤波的转子轴心轨迹提纯方法[J].重庆大学学报,2012,35:15－20.
[127]Don Southwick. Using full spectrum plots [J]. Orbit,1993,14:19－21.
[128]Don Southwick. Using full spectrum plots part2[J]. Orbit,1994,15:11－15.
[129]Fengqi Wu, G. Meng. Compound rub malfunctions feature extraction based on full spectrumcascade analysis and SVM [J]. Mechanical systems and signal processing,2006,20:2007－2021.
[130]N. Bachschmid, P. Pennacchi, A. Vania. Diagnostic significance of orbit shape analysis and itsapplication to improve machine fault detection [J]. Journal of Brazilian Society of MechanicalSciences and Engineering,2004,26:200－208.
[131]T.H. Patel, A.K. Darpe. Vibration response of a cracked rotor in presence of rotor-stator rub [J].Journal of sound and vibration,2008,317:841－865.
[132]吴峰崎,孟光,荆建平.基于加速度信号全谱分析的转子碰摩故障特征提取实验研究[J].振动与冲击,2006,25:44－47.
[133]吴峰崎,孟光.转子故障加速度信号的全谱分析[C].第十二届全国信号处理学术年会,苏州,2005,21:613－616.
[134]吴凝,丁康.全频谱进动分析原理及在故障诊断中的应用[C].2008年全国振动工程及应用学术会议暨第十一届全国设备故障诊断学术会议论文集,西宁,2008,27:170－172.
[135]屈梁生,邱海,徐光华.全息动平衡技术：原理与实践[J].中国机械工程,1988,9:60－63.
[136]刘石,屈梁生.全息谱技术在轴系现场动平衡方法中的应用[J].热能动力工程,2009,24:24－30.
[137]刘石,屈梁生.全息谱技术在现场动平衡前故障诊断中的应用[J].振动、测试与诊断,2004,24:269－274.
[138]徐宾刚,屈梁生.非对称转子的全息动平衡技术[J].西安交通大学学报,2000,34:50－54.
[139]刘淑莲,李强,郑水英.基于全息谱分析的非线性转子系统不平衡量识别[J].机械工程学报,2010,46:62－67.
[140]李宏坤,周帅等.基于经验模式分解的全息谱故障识别方法[J].振动、测试与诊断,2011,31:20－22.
[141]刘然,许宝杰.基于EMD和全息谱的设备故障诊断方法研究[J].机械科学与技术,2011,30:1922－1926.
[142]汤宝平,陈建波,章国稳.基于相位差校正法的全息谱研究[J].振动与冲击,2009,28:99－102.
[143]魏玉果,汤宝平等.基于阶比跟踪的全息谱技术[J].重庆大学学报（自然科学版）,2007,30:10－12.
[144]梅检民,陈祥龙等.基于稳健全息阶比谱提取微弱故障特征[J].振动与冲击,2012,31:49－52.
[145]刘石,屈梁生.回转机械故障诊断中的三维全息谱技术[J].西安交通大学学报,2004,38:899－903.
[146]王琇峰,屈梁生,廖与禾.三维全息谱在诊断负荷诱发的转子热弯曲与标高故障中的运用[J].热能动力工程,2008,23:462－467.
[147]史东峰,屈梁生.三维全息谱分解在回转机械诊断中的应用研究[J].西安交通大学学报,1998,32:94－97.
[148]韩捷,石来德著.全矢谱技术及其工程应用[M].北京：机械工业出版社,2008.
[149]韩捷,石来德.旋转机械的全信息能量谱分析方法研究[J].机械强度,2003,25:364－368.
[150]韩捷,董辛旻等.旋转机械同源数据全信息倒频谱分析与应用[J].机械强度,2005,27:452－455.
[151]巩晓赟,韩捷等.全矢小波包-包络分析方法及其在齿轮故障诊断中的应用[J].振动与冲击,2012,31:92－95.
[152]李凌均,韩捷等.矢双谱分析及其在机械故障诊断中的应用[J].机械工程学报,2012,31:92－95.
[153]马辉,李辉等.转子系统二阶油膜失稳故障仿真及试验研究[J].机械工程学报,2013,49:24－29.
[154]马辉,李辉等.两种转子-轴承系统模型的失稳故障仿真[J].中国工程机械学报,2013,11:340－346.
[155]H. Ma, et al. Time-frequency features of two types of coupled rub-impact faults in rotorsystems [J]. Journal of sound and vibration,2009,321:1109－1128.
[156]C. Lee, Y. Han. The directional wigner distribution and its application [J]. Journal of sound andvibration,1998,216:585－600.
[157]C. Lee, Y. Han. Directional wigner distribution for order analysis in rotating/reciprocatingmachines [J]. Mechanical systems and signal processing,1999,13:723－737.
[158]王丽雅,韩捷,陈磊.基于信息融合的短时矢功率谱分析方法[J].机械科学与技术,2007,26:437－440.
[159]腾山邦久著.声发射（AE）技术的运用[M].北京：冶金工业出版社,1996.
[160]A. Albers, W. Burger, et al. Monitoring lubrication regimes in sliding bearings-using acousticemission analysis [J]. Practical oil analysis magazine,2006, May:8－12.
[161]王晓伟,刘占生等.基于声发射的可倾瓦径向滑动轴承碰摩故障诊断[J].中国电机工程学报,2009,29:64－69.
[162]吴峰崎,孟光,荆建平.基于声信号三维谱分析的转子复合碰摩故障特征提取[J].振动与冲击,2005,24:79－84.
[163]秦萍,傅和平等.基于声发射监测的静载荷滑动轴承接触摩擦故障诊断的实验研究[J].摩擦学学报,2006,26:585－589.
[164]朱益军,李录平等.滑动轴承润滑状态与声发射信号特征关系研究[J].汽轮机技术,2011,53:50－55.
[165]T. Kim，H. White. On more robust estimation of skewness and kurtosis [J]. Finance ResearchLetters,2003,1:65－70.
[166]K. Pearson. Das Fehlergesetz und seine Verallgemeinerungen durch Fechner und Pearson [J]. ARejoinder. Biometrika,4:169－212.
[167]D. Ruppert. What is kurtosis?: An influence function approach [J]. The American StatisticalAssociation,1987,41:1－5.
[168]R.F. Dwyer. Detection of non-Gaussian signals by frequency domain kurtosis estimation [C]. in:Proceedings of the International Conference on Acoustics, Speech, and Signal Processing,Boston,1983,607－610.
[169]R.F. Dwyer. A technique for improving detection and estimation of signals contaminated byunder ice noise [J]. Journal of the Acoustical Society of America,1983,74:124－130.
[170]R.F. Dwyer. Use of the kurtosis statistics in the frequency domain as an aid in detecting randomsignals [J]. IEEE Journal of Oceanic Engineering,1984,9:85－92.
[171]C. Otonnello, S. Pagnan. Modified frequency domain kurtosis for signal processing [J].Electronics Letters,1994,30:1117－1118.
[172]S. Pagnan, et al.. Filtering of randomly occurring signals by kurtosis in frequency domain [C].Proceedings of the12thInternational Conference on Pattern Recognition,1994,3:131－133.
[173]V. Capdevielle, C. Servière, J.L. Lacoume. Blind separation of wide-band sources: applicationto rotating machine signals [C]. Proceedings of the Eighth European Signal ProcessingConference,1996,3:2085－2088.
[174]刘亭伟.基于谱峭度的齿轮箱故障特征提取[D].昆明：昆明理工大学,2011.
[175]J.J.A. Moors. The meaning of the kurtosis: Darlington reexamined [J]. The AmericanStatistician,1986,40:283－284.
[176]R.B. Darlington. Is kurtosis really ‘peakedness’?[J]. The American Statistician,1970,24:19－22.
[177]J.J.A. Moors. A quantile alternative for kurtosis [J]. The Statistician,1988,37:25－32.
[178]R.V. Hogg. More light on the kurtosis and related statistics [J]. Journal of the AmericanStatistical Association,1972,67:422－424.
[179]R.V. Hogg. Adaptive robust procedures: a partial review and some suggestions for futureapplications and theory [J]. Journal of the American Statistical Association,1974,69:909－923.
[180]E.L. Crow, M.M. Siddiqui. Robust estimation of location [J]. Journal of the AmericanStatistical Association,1967,62:353－389.
[181]Seeded fault test data [OL]. Bearing Data Center, Case Western Reserve University,2012,http://csegroups.case.edu/bearingdatacenter/home.
[182]高西全，丁玉美，阔永红著.数字信号处理：原理、实现及应用[M].北京：电子工业出版社，2006.
[183]L. Shi, R.B. Randall, J. Antoni. Rolling element bearing fault detection using improvedenvelope analysis [C]. In: Proceedings of the Eighth International Conference on Vibrations inRotating Machinery, Swansea, Wales, September7-9,2004,301－311.
[184]N. Sawalhi, R.B. Randall. Spectral kurtosis optimization for rolling element bearings [C]. In:Proceedings of the Eighth International Conference on Vibrations in Rotating Machinery,Sydney, August28-31,2005,839－842.
[185]M.G. Frei, I. Osorio. Intrinsic time-scale decomposition: time-frequency-energy analysis andreal-time filtering of non-stationary signals [J]. Proceedings of the royal society A,2007,463:321－342.
[186]秦毅.一体化小波分析仪的研究[D].重庆：重庆大学,2008.
[187]周瑞.基于第二代小波的机械故障信号处理方法研究[D].哈尔滨：哈尔滨工业大学,2009.
[188]S.-F. Lei and R.P. Hamernik. Wavelet transform and frequency domain kurtosis: application toassessment of hearing hazard from noise exposure [C]. Applications of signal processing toaudio and acoustics, IEEE ASSP Workshop on,1995,19－21.
[189]S.-F. Lei, W.A. Ahroon and R.P. Hamernik. The application of frequency and time domainkurtosis to the assessment of hazardous noise exposures [J]. The journal of the acousticalsociety of America,1994,96:1435－1444.
[190]J. Lin and M.J. Zuo. Gearbox fault diagnosis using adaptive wavelet filter [J]. Mechanicalsystems and signal processing,2003,17:1259－1269.
[191]石林锁.滚动轴承故障检测的改进包络分析法[J].轴承,2006,2:36－39.
[192]W. Sweldens. The lifting scheme: A custom-design construction of biorthogonal wavelets [J].Applied and computational harmonic analysis,1996,3:186－200.
[193]W. Sweldens. The lifting scheme: A construction of second generation wavelets [J]. SIAMjournal of mathematical analysis,1998,29:511－546.
[194]R.L. Claypoole, R.G. Baraniuk, R.D. Nowak. Adaptive wavelet transforms via lifting [C].IEEE International conference on Acoustics, Speech and Signal processing,1998,3:1513－1516.
[195]C.S. Lee, C.K. Lee, K.Y. Yoo. New lifting based structure for undecimated wavelet transform[J]. Electronics Letters,2000,36:1894－1895.
[196]H.K. Jiang, Z. He, C. Duan and P. Chen. Gearbox fault diagnosis using adaptive redundantlifting scheme [J]. Mechanical systems and signal processing,2006,20:1992－2006.
[197]姜洪开,王仲生.第二代小波包构造及发动机微弱损伤识别[J].北京航空航天大学学报,2007,33:777－780.
[198]姜洪开,王仲生,何正嘉.基于自适应提升小波包的故障微弱信号特征早期识别[J].西北工业大学学报,2008,26:99－103.
[199]R. Zhou, W. Bao, N. Li, X. Huang, D.R. Yu. Mechanical equipment fault diagnosis based onredundant second generation wavelet packet transform [J]. Digital signal processing,2010,20:276－288.
[200]N.E. Huang, Z. Shen, S.R. Long, et al. The empirical mode decomposition and the Hilbertspectrum for the nonlinear and non-stationary time series analysis [J]. Proceedings of the RoyalSociety of London, Series A,1988,454:903－995.
[201]Z. Wu, N.E. Huang. Ensemble empirical mode decomposition: a noise-assisted data analysismethod [J]. Advances in adaptive data analysis,2009,1:1－41.
[202]Z.K. Peng, P.W. Tse, F.L. Chu. A comparison study of improved Hilbert-Huang transform andwavelet transform: application to fault diagnosis for rolling bearing [J]. Mechanical systemsand signal processing,2005,19:974－988.
[203]Z.K. Peng, P.W. Tse, F.L. Chu. An improved Hilbert-Huang transform and its application invibration signal analysis [J]. Journal of sound and vibration,2005,286:187－205.
[204]Y. Qin, S. Qin, Y. Mao. Research on iterated Hilbert transform and its application inmechanical fault diagnosis [J]. Mechanical systems and signal processing,2008,22:1967－1980.
[205]N.E. Huang, Z. Wu. A review on Hilbert-Huang transform: method and its applications togeophysical studies [J]. Reviews of Geophysics,2008,46:1－23.
[206]褚福磊,彭志科著.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.
[207]W.R. Bennett. Statistics of regenerative digital transmission [J]. The Bell system technicaljournal,1958,37:1501－1542.
[208]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,2007.
[209]W.A. Gardner, L. Franks. Characterization of cyclostationary random signal processes [J].IEEE transactions on information theory,1975,21:4－14.
[210]W.A. Gardner. Stationarizable random processes [J]. IEEE transactions on information theory,1978,24:8－22.
[211]W.A. Gardner. Spectral correlation of modulated signals: part I-analog modulation [J]. IEEEtransactions on communication,1987,35:584－594.
[212]W.A. Gardner. Signal interception: a unifying theoretical framework for feature detection [J].IEEE transactions on communication,1988,8:897－906.
[213]W.A. Gardner. Exploitation of spectral redundancy in cyclostationary signals [J]. IEEE signalprocessing magazine,1991,8:14－36.
[214]C.M. Spooner, W.A. Gardner. The cumulant theory of cyclostationary time-series: part Ⅱ:development and applications [J]. IEEE transactions on signal processing,1994,42:3409－3429.
[215]W.A. Gardner. Introduction to random processing with applications to signals and systemsSecond edition [M]. New York: McGraw-Hill,1990.
[216]W.A. Gardner. Statistical spectral analysis: a non-probabilistic theory [M]. Englewood Cliffs,NJ: Prentice-Hall,1987.
[217]A.V. Dandawate, G.B. Giannakis. Polyspectral analysis of nonstationary signals: Systemidentification, classification and ambiguity functions [C]. In Processing Int. Signal ProcessingWorkshop Higher Order Statistics,1991:147－150.
[218]G.B. Giannakis., A.V. Dandawate. Polyspectral analysis of non-stationary signals: Bases,consistency and HOS-WV [C]. In Processing Int. Signal Processing Workshop Higher OrderStatistics,1991:167－170.
[219]A.V. Dandawate, G.B. Giannakis. Ergodicity results for non-stationary signals: cumulants,ambiguity functions and wavelets [C]. In processing25thAnnual Conference InformationScience, System,1991:976－983.
[220]A.V. Dandawate, G.B. Giannakis. Nonparametric polyspectral analysis of AM signals andprocesses with missing observations [C]. In processing26thAnnual Conference InformationScience, System,1992:603－605.
[221]A.V. Dandawate, G.B. Giannakis. Cyclic-cumulant based identification of almost periodicallytime-varying systems: Parametric methods [C]. In Processing Int. Conference, ASSP,1992:229－232.
[222]G.B. Giannakis. Digital signal processing handbook: Cyclostationary signal analysis [M]. CRCPress&IEEE Press,1998.
[223]C. Capdessus, M. Sidahmed, J.L. Lacoume. Cyclostationary processes: application in gearfaults early diagnosis [J]. Mechanical systems and signal processing,2000,14:371－385.
[224]C. Capdessus, M. Sidahmed, J.L. Lacoume. Cyclostationary processes: application in gearfaults early diagnosis [J]. Mechanical systems and signal processing,2000,14:371－385.
[225]J. Antoni. Cyclostationarity by examples [J]. Mechanical systems and signal processing,2009,23:987－1036.
[226]J. Antoni, D. Hanson, R.B. Randall. Combining cyclostationarity and the frequency domainsubspace technique for operational modal analysis [J]. Key engineering materials,2007,347:473－478.
[227]J. Antoni, R. Boustany. Advanced rolling element bearing diagnostics based on cyclic spectralanalysis [J]. Key engineering materials,2007,347:265－270.
[228]R.B. Randall, J. Antoni, S. Chobsaard. The relationship between spectral correlation andenvelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals[J]. Mechanical systems and signal processing,2001,15:945－962.
[229]J. Antoni, F. Bonnardot, A. Raad, M. EI Badaoui. Cyclostationary modelling of rotatingmachine vibration signals [J]. Mechanical systems and signal processing,2004,18:1285－1314.
[230]J. Antoni. Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions [J].Journal of sound and vibration,2007,497－529.
[231]J. Antoni. Cyclic spectral analysis in practice [J]. Mechanical systems and signal processing,2007,21:597－630.
[232]R. Boustany, J. Antoni. A subspace method for the blind extraction of a cyclostationary source:Application to rolling element bearing diagnostics [J]. Mechanical systems and signalprocessing,2005,19:1245－1259.
[233]R.B. Randall, J. Antoni, S. Chobsaard. The relationship between spectral correlation andenvelope analysis for cyclostationary machine signals, application to ball bearing diagnostics[J]. Mechanical systems and signal processing,2001,19:945－962.
[234]J. Antoni, R.B. Randall. On the use of the cyclic power spectrum in rolling element bearingsdiagnostics [J]. Journal of sound and vibration,2005,281:463－468.
[235]李力,屈梁生.循环统计量方法在滚动轴承故障诊断中的应用[J].振动、测试与诊断,2003,23:116－119.
[236]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,2002,36:943－946.
[237]李力,屈梁生.循环域解调方法在滚动轴承故障诊断中的应用[J].轴承,2003,10:33－36.
[238]李力,屈梁生.谱相关特性在机械信号特征提取中的应用研究[J].中国机械工程,2006,2:334－338.
[239]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.
[240]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海交通大学学报,2002,36:43－46.
[241]姜鸣,陈进,秦恺.时变调幅信号的循环平稳特征讨论和应用[J].上海交通大学学报,2001,35:1798－1801.
[242]姜鸣,陈进,秦恺,汪慰军.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,2001,14:424－428.
[243]F. Zhou, Jin Chen, J. He, et al. Cyclic statistics based neural network for early fault diagnosisof rolling element bearings [J]. Lecture notes in computer science,2004,3174:595－600.
[244]周福昌,陈进等.循环平稳信号处理在机械设备故障诊断中的应用综述[J].振动与冲击,2006,25:148－152.
[245]周福昌,陈进等.基于小波分析与循环平稳度分析的滚动轴承早期故障诊断方法[J].振动与冲击,2006,25:91－93.
[246]Guo Bi, Jin Chen, et al. Application of degree of cyclostationarity in rolling element bearingdiagnosis [J]. Key engineering materials,2005,293-294:347－354.
[247]Guo Bi, Jin Chen, et al. Application of slice spectral correlation density to gear defect detection[J]. Proc. IMechE Part C: J. Mechanical engineering science,2006,220:1385－1392.
[248]毕果,陈进等.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,2006,19:388－393.
[249]毕果,陈进等.谱相关密度切片分析在滚动轴承故障诊断中的应用[J].振动与冲击,2006,25:280－282.
[250]何俊.循环平稳和解调频技术在故障诊断中的研究和应用[D].上海：上海交通大学,2007.
[251]Jun He, Jin Chen, et al. Frequency-demodulated analysis based on cyclostationarity for faultdetection in gears [J]. Key engineering materials,2005,293-294:87－94.
[252]何俊,陈进等.循环自相关函数及其切片的解调原理分析[J].振动工程学报,2004,17:359－362.
[253]周宇.基于循环平稳信号二维平面表示的滚动轴承早期故障诊断方法研究[D].上海：上海交通大学,2012.
[254]Yu Zhou, Jin Chen, et al. Application of the horizontal slice of cyclic bispectrum in rollingelement bearings diagnosis [J]. Mechanical systems and signal processing,2012,26:229－243.
[255]Yu Zhou, Jin Chen, et al. Wigner-Ville distribution based on cyclic spectral density and theapplication in rolling element bearings diagnosis [J]. Proc. IMechE Part C: J. Mechanicalengineering science,2011,225:2831－2847.
[256]周宇,陈进等.基于循环双谱的滚动轴承故障诊断[J].振动与冲击,2012,31:78－81.
[257]周宇,陈进等.基于循环双谱二次切片分析的滚动轴承故障诊断研究[J].振动与冲击,2012,31:24－28.
[258]明阳.基于循环平稳和盲源分离的滚动轴承故障特征提取方法研究[D].上海：上海交通大学,2013.
[259]Yang Ming, Jin Chen, G. Dong. Weak fault feature extraction of rolling bearing based on cyclicWiener filter and envelope spectrum [J]. Mechanical systems and signal processing,2011,25:1773－1785.
[260]Yang Ming, Jin Chen, G. Dong. Application of convolved blind separation based onsecond-order cyclic statistics in rolling bearing feature extraction [J]. Journal of vibration andcontrol,2012,1－17.
[261]A.V. Dandawate. Exploiting cyclostationarity and higher-order statistics in signal processing
[D]. Charlottesville, Virginia: University of Virginia,1993.
[262]J.W. Lund. Spring and damping coefficients for the tilting-pad journal bearing [J]. ASLETransactions,1964,7:342－352.
[263]N. Feng, E. Hahn, R.B. Randall. Using transient analysis software to simulate vibration signalsdue to rolling element bearing defects [C]. in Proceedings of the3rdAustralian Congress onApplied Mechanics,2002,689－694.
[264]National Instruments. http://www.ni.com [DB/OL].2014.
[265]B&K. http://www.bksv.com [DB/OL].2014.
[266]柏林.网络化虚拟仪器核心技术及其典型案例的研究[D].重庆：重庆大学,2004.
[267]Bently Nevada. http://www.ge-mcs.com/en/bently-nevada.html [DB/OL].2014.
[268]尹爱军等著.秦氏模型-基于智能虚拟控件的仪器（第二版）[M].北京：科学出版社,2008.
[269]秦树人.岩石模型：机械系统大型测试仪器库[J].机械工程学报,2007,43:68－74.
[270]秦毅.一体化小波分析仪的研究[D].重庆：重庆大学,2008.
[271]刘文艺.风电机组振动监测与故障诊断研究[D].重庆：重庆大学,2010.
[272]董绍江.基于优化支持向量机的空间滚动轴承寿命预测方法研究[D].重庆：重庆大学,2012.
[273]陈仁祥.振动谱表征空间滚动轴承寿命状态方法研究[D].重庆：重庆大学,2012.
[274]API Standard670: Machinery protection systems,4thedition [S].2000.
[275]ISO10816-1: Mechanical vibration-Evaluation of machine vibration by measurements onnon-rotating parts Part1: General guidelines [S].1995.
© 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 |