振动信号非平稳特征的深层提取技术及远程诊断服务系统的研究
|
摘要
机械设备自身的复杂结构,故障的发生和发展、设备工况的变化导致其振动信号往往具有非稳态的复杂特性,振动信号的非平稳特征提取与表示关系到故障诊断的可靠性和准确性。时频分析是信号非平稳特性分析的有力工具,但随着设备新旧故障的不断复杂化,使得传统的时频分析理论及方法难以完全适用于某些有用信息的捕捉和深入分析,因而有关的研究和应用也受到了很大的限制。该文在综述了国内外故障诊断研究现状的基础上,分析了机械振动信号的非平稳特征及其深层次信息提取的意义;提出了在常规时频分析结果的基础上进行更深层次的二次处理,以提取表征信号非平稳特性的瞬时物理量的多种新方法,并以故障齿轮和故障轴承的大量振动信号作为分析实例对这些方法进行了一一验证;在理论上初步设计了电子商务环境下的远程测试诊断服务系统方案,并对系统实现中的关键技术和核心模块进行了详细的论述和探讨。
本文主要从以下四个方面对非平稳特征的深层次提取技术进行了深入、细致的研究和探讨:
1)基于小波变换的非平稳特征提取技术。对小波变换的理论方法、工程理解及小波基、小波变换尺度的选择进行了系统地分析,介绍了基于复解析小波变换的瞬时幅值及瞬时相位的提取技术,在此基础上引入了基于小波脊线的瞬时频率估计方法,提出了小波尺度—能量与小波时间—能量相关谱的特征表示法并用于表示轴承外圈、滚珠和内圈的故障特征。
2)基于小波包分解与解调技术的非平稳特征提取技术。利用小波包分解技术对振动信号进行降噪处理,并选择特殊频段进行小波包重构,有效捕捉和分离了处于信号不同频段的故障特征分量。在此基础上,对传统Hilbert瞬时频率估计方法加以改进,并引入Teager能量算子解调方法,给出了适合多分量调幅调频信号的瞬时频率特征或调制特征的提取方法。
3)基于联合时频分析的非平稳特征提取技术。阐述了各种线性时频变换和非线性时频变换的时频聚集性和交叉干扰问题,主要对自适应信号分解法和径向高斯核时频分布这两种时频分析方法进行了深入的研究与探讨。在传统自适应信号分解的基础上的提出了三种振动信号处理新技术:基于时频曲面拟合的信号分解法,可有效用于非平稳信号的噪声抑制;对Chirplet基函数加以改进,引入了二次非线性调频参数,提高传统Chirplet自适应信号分解法的时频特征刻画能力;在时频二维平面上采用振动冲击信号模型对时频滤波后的信号分量进行逼近,提纯瞬态冲击分量。在基于径向高斯核时频分布的基础上,引入了时域分段方法提高了其时频分布质量,利用图像处理技术提取出时频图像的脊线特征之后,引入Hough变换自动检测出时频脊线的特征参数,再根据时频脊线与瞬时频率曲线的对应关系来确定多分量时变信号的瞬时频率函数表达式;继而采用基于瞬时频率曲线的时频滤波方法对被分析信号进行滤波后重构,达到了从多分量复杂信号中分离特征分量的目的。
4)基于经验模态分解(EMD)的非平稳特征提取技术。在介绍EMD基本理论的基础上,结合实际应用中遇到的问题,系统论述了EMD中存在的端点效应,本征模函数的筛分迭代停止的标准和模态混叠这三大问题,并针对这些问题提出了相应的解决方法。简要概述了EMD端点效应产生的原因和目前提出的一些有效的抑制方法,并对这些方法的优缺点进行了分析比较。重点针对EMD中出现的模态混叠现象,提出了两种解决方案。一种是EMD与小波包分解相结合的方法;第二种是加减掩膜信号法。仿真实验和应用实践证明这两种方法都能较好地消除EMD中的模态混叠现象,可有效提高EMD的分解能力。
我们研究振动信号的非平稳特征深层提取技术的最终目的是将它们应用到远程测试诊断服务系统中,解决工程实际中的具体问题,将科学技术转化为生产力。因此,本文在上述理论方法的研究基础上,将电子商务理念与远程测试诊断相结合,提出了电子商务环境下的个性化远程测试诊断服务系统的结构模型,系统研究了远程测试诊断服务系统中的虚拟仪器、数据传输、神经网络学习、智能诊断、系统安全等关键技术,开发了服务系统的各种功能模块并在汽车齿轮箱和轴承等典型性设备的远程故障诊断实验中得到了成功应用。本文的研究工作为非平稳信号特征提取和故障诊断开辟了新的途径,其成果对于我们今后的理论研究和开发实践具有承上启下的作用,对工业生产具有相当的实用价值。
The vibration signals of mechanical equipment often represent nonstationarities due to occurrence and variance of fault, influence of nonnormal working condition and inherent nonlinearity of equipment. Nonstationary feature representation and extraction is the most crucial and difficult problem for reliability and accuracy in mechanical fault diagnosis. Though the time-frequency representation is a powerful tool in the study of nonstationary and nonlinear signal, the traditional time-frequency analysis theories and methods are not always suitable to describe the nonstationarities with the increasing complex of new fault,. In this dissertation, based upon a summary of the present worldwide research status, the nonstationary feature of machine vibration signal is defined and the significance of its deep-seated extraction is analyzed. It is indicated that the nonstationary feature of machine vibration signal mainly focus in some main physical quantities, such as the instantaneous frequency, phase ,amplitude, energy , modulated information etc., all of which can be extracted only with further processing the analyzed result from traditional time-frequency distribution, and then can be used for diagnosis. Taking bearing and gearbox being as the researched objects, some practical techniques for deeply extracting nonstationary feature based on traditional time-frequency analysis, is proposed and developed.
In the aspects of the nonstationary feature in-depth extraction, the main contributions of this dissertation as follows:
(1) Nonstationary feature extraction techniques based on Wavelet Transform (WT) are studied. The theory and engineering significance of WT are introduced. The selection problem of wavelet basis and transforming scale is analyzed in detail. With complex analyzed wavelet transform, the instantaneous amplitude and phase can be computed, and then the instantaneous frequency can be extracted with the wavelet ridges method. The feature representations of wavelet scale-energy and time-energy correlation are proposed, and are used to represent the fault features of bearing roll, inner race and outer race successfully.
(2) Nonstationary feature representation and extraction method based upon the Wavelet Packet Decomposition (WPD) and demodulation techniques are studied. With WPD, the vibration signal can be denoised, and some component in special frequency band is selected to reconstruct. With the decomposition and reconstruction of wavelet packet, the monocomponents with fault feature in different frequency band would be captured and separated out. Based upon this, Teager energy operator demodulation method and improved Hilbert instantaneous frequency estimation method are introduced into WPD to be a syncretic technique fitting for extracting noncomponent instantaneous frequency and modulation information from multicomponent complicated signals. This syncretic method is applied into the gearbox fault diagnosis and achieves good effects.
(3) Nonstationary feature extraction based on the joint Time Frequency (TF) analysis is studied. The conceptions of linear and nonlinear TF transformation, their properties and cross-term interference problem are summarized and analyzed systematically. On the basis of traditional self-adaptive TF analysis, three new techniques for fault signal processing are proposed. One is the improved Chirplet self-adaptive TF representation, in which an additional parameter referred to as curvature parameter is introduced in the traditional Chirplet elementary function to match the time varying linear or non-linear components. The performance comparison between the modified version of adaptive TF representation and the other TF representation, verifies that the modified version has the high TF resolution and no cross-term interference; the other is the TF decomposition based on curved surface fitting, which has been applied successfully into nonstationary signal denoising, another is the transient impulse signal extraction based on self-adaptive TF decomposition, which uses the vibration impulse signal model to approximate the signal filtered out in TF plane, in order to determine the parameters of impulse signal model. On the basis of self-adaptive kernel function TF representation, the precise IF extraction method using TF ridges is proposed. In this method, by segmenting in time domain with the way of adding time windows, the whole domain is cut into many sub-segments and the whole Redial Gaussian Kernel time-frequency Distribute(RGKD) can be obtained by concatenating the sub-segment RGKD, and then, the whole RGKD is regarded as an image, the ridges of the image are extracted using digital image processing method including smoothing operation, 2-D Laplacian arithmetic operator and thinning, finally, the principle of detecting the lines of Hough transform is used. Based on the instantaneous frequency extracted result, the TF filtering method based on instantaneous frequency is developed, by which the special monocomponent is separated from the original signal.
(4) Nonstationary feature extraction base on Empirical Mode Decomposition (EMD) is studied. Based on the basal principle of empirical mode decomposition (EMD), three existing problems from EMD, that is, end effects, the select of sifting stop guide and mode mixing, are analyzed. For these limits of conventional EMD, some improved methods are proposed for the precision and correctness of EMD, combining the problems occurring in practical applications. Two novel solutions to mode mixing problems are presented in detail. One is expending best wavelet packet decomposition (WPD) into EMD, that is, applying WPD based on Shannon entropy into the selected (IMF) in which the mixing mode exists. The second novel solution to the problem of mode mixing is the masking signal method. The experiment results verify that these two methods both are benefit to the improvement of EMD decomposition ability.
Our final intention is applying all these nonstationary feature extraction techniques to solve the practical engineering problems, transforming the science technique to productivity. A number of practical signal analysis examples indicate that synthetically applying above feature extraction methods, the exact diagnosis result can be attained. As the carrier of these extraction techniques, the remote measurement and diagnosis service system proposed in the dissertation realizes the amalgamation of E-commerce, information network and industry intranet. Its overall architecture are researched and designed, and some key techniques including virtual instrument、DataSocket remote data transferring、neural network study and diagnosis、network security technique etc. are introduced in detail.
The methods of nonstationary signal feature extraction developed in this dissertation take on the important reference value for mechanical equipment fault diagnosis. The remote measurement and diagnosis service system in E-commerce environment can cut the time of collecting fault, improve the efficiency of diagnosis system, and favor accumulating data and sharing resources. All the studies are practical and realistic, and full of innovation. The research productions are beneficial for theory study and development practice in the nearly future.
本文主要从以下四个方面对非平稳特征的深层次提取技术进行了深入、细致的研究和探讨:
1)基于小波变换的非平稳特征提取技术。对小波变换的理论方法、工程理解及小波基、小波变换尺度的选择进行了系统地分析,介绍了基于复解析小波变换的瞬时幅值及瞬时相位的提取技术,在此基础上引入了基于小波脊线的瞬时频率估计方法,提出了小波尺度—能量与小波时间—能量相关谱的特征表示法并用于表示轴承外圈、滚珠和内圈的故障特征。
2)基于小波包分解与解调技术的非平稳特征提取技术。利用小波包分解技术对振动信号进行降噪处理,并选择特殊频段进行小波包重构,有效捕捉和分离了处于信号不同频段的故障特征分量。在此基础上,对传统Hilbert瞬时频率估计方法加以改进,并引入Teager能量算子解调方法,给出了适合多分量调幅调频信号的瞬时频率特征或调制特征的提取方法。
3)基于联合时频分析的非平稳特征提取技术。阐述了各种线性时频变换和非线性时频变换的时频聚集性和交叉干扰问题,主要对自适应信号分解法和径向高斯核时频分布这两种时频分析方法进行了深入的研究与探讨。在传统自适应信号分解的基础上的提出了三种振动信号处理新技术:基于时频曲面拟合的信号分解法,可有效用于非平稳信号的噪声抑制;对Chirplet基函数加以改进,引入了二次非线性调频参数,提高传统Chirplet自适应信号分解法的时频特征刻画能力;在时频二维平面上采用振动冲击信号模型对时频滤波后的信号分量进行逼近,提纯瞬态冲击分量。在基于径向高斯核时频分布的基础上,引入了时域分段方法提高了其时频分布质量,利用图像处理技术提取出时频图像的脊线特征之后,引入Hough变换自动检测出时频脊线的特征参数,再根据时频脊线与瞬时频率曲线的对应关系来确定多分量时变信号的瞬时频率函数表达式;继而采用基于瞬时频率曲线的时频滤波方法对被分析信号进行滤波后重构,达到了从多分量复杂信号中分离特征分量的目的。
4)基于经验模态分解(EMD)的非平稳特征提取技术。在介绍EMD基本理论的基础上,结合实际应用中遇到的问题,系统论述了EMD中存在的端点效应,本征模函数的筛分迭代停止的标准和模态混叠这三大问题,并针对这些问题提出了相应的解决方法。简要概述了EMD端点效应产生的原因和目前提出的一些有效的抑制方法,并对这些方法的优缺点进行了分析比较。重点针对EMD中出现的模态混叠现象,提出了两种解决方案。一种是EMD与小波包分解相结合的方法;第二种是加减掩膜信号法。仿真实验和应用实践证明这两种方法都能较好地消除EMD中的模态混叠现象,可有效提高EMD的分解能力。
我们研究振动信号的非平稳特征深层提取技术的最终目的是将它们应用到远程测试诊断服务系统中,解决工程实际中的具体问题,将科学技术转化为生产力。因此,本文在上述理论方法的研究基础上,将电子商务理念与远程测试诊断相结合,提出了电子商务环境下的个性化远程测试诊断服务系统的结构模型,系统研究了远程测试诊断服务系统中的虚拟仪器、数据传输、神经网络学习、智能诊断、系统安全等关键技术,开发了服务系统的各种功能模块并在汽车齿轮箱和轴承等典型性设备的远程故障诊断实验中得到了成功应用。本文的研究工作为非平稳信号特征提取和故障诊断开辟了新的途径,其成果对于我们今后的理论研究和开发实践具有承上启下的作用,对工业生产具有相当的实用价值。
The vibration signals of mechanical equipment often represent nonstationarities due to occurrence and variance of fault, influence of nonnormal working condition and inherent nonlinearity of equipment. Nonstationary feature representation and extraction is the most crucial and difficult problem for reliability and accuracy in mechanical fault diagnosis. Though the time-frequency representation is a powerful tool in the study of nonstationary and nonlinear signal, the traditional time-frequency analysis theories and methods are not always suitable to describe the nonstationarities with the increasing complex of new fault,. In this dissertation, based upon a summary of the present worldwide research status, the nonstationary feature of machine vibration signal is defined and the significance of its deep-seated extraction is analyzed. It is indicated that the nonstationary feature of machine vibration signal mainly focus in some main physical quantities, such as the instantaneous frequency, phase ,amplitude, energy , modulated information etc., all of which can be extracted only with further processing the analyzed result from traditional time-frequency distribution, and then can be used for diagnosis. Taking bearing and gearbox being as the researched objects, some practical techniques for deeply extracting nonstationary feature based on traditional time-frequency analysis, is proposed and developed.
In the aspects of the nonstationary feature in-depth extraction, the main contributions of this dissertation as follows:
(1) Nonstationary feature extraction techniques based on Wavelet Transform (WT) are studied. The theory and engineering significance of WT are introduced. The selection problem of wavelet basis and transforming scale is analyzed in detail. With complex analyzed wavelet transform, the instantaneous amplitude and phase can be computed, and then the instantaneous frequency can be extracted with the wavelet ridges method. The feature representations of wavelet scale-energy and time-energy correlation are proposed, and are used to represent the fault features of bearing roll, inner race and outer race successfully.
(2) Nonstationary feature representation and extraction method based upon the Wavelet Packet Decomposition (WPD) and demodulation techniques are studied. With WPD, the vibration signal can be denoised, and some component in special frequency band is selected to reconstruct. With the decomposition and reconstruction of wavelet packet, the monocomponents with fault feature in different frequency band would be captured and separated out. Based upon this, Teager energy operator demodulation method and improved Hilbert instantaneous frequency estimation method are introduced into WPD to be a syncretic technique fitting for extracting noncomponent instantaneous frequency and modulation information from multicomponent complicated signals. This syncretic method is applied into the gearbox fault diagnosis and achieves good effects.
(3) Nonstationary feature extraction based on the joint Time Frequency (TF) analysis is studied. The conceptions of linear and nonlinear TF transformation, their properties and cross-term interference problem are summarized and analyzed systematically. On the basis of traditional self-adaptive TF analysis, three new techniques for fault signal processing are proposed. One is the improved Chirplet self-adaptive TF representation, in which an additional parameter referred to as curvature parameter is introduced in the traditional Chirplet elementary function to match the time varying linear or non-linear components. The performance comparison between the modified version of adaptive TF representation and the other TF representation, verifies that the modified version has the high TF resolution and no cross-term interference; the other is the TF decomposition based on curved surface fitting, which has been applied successfully into nonstationary signal denoising, another is the transient impulse signal extraction based on self-adaptive TF decomposition, which uses the vibration impulse signal model to approximate the signal filtered out in TF plane, in order to determine the parameters of impulse signal model. On the basis of self-adaptive kernel function TF representation, the precise IF extraction method using TF ridges is proposed. In this method, by segmenting in time domain with the way of adding time windows, the whole domain is cut into many sub-segments and the whole Redial Gaussian Kernel time-frequency Distribute(RGKD) can be obtained by concatenating the sub-segment RGKD, and then, the whole RGKD is regarded as an image, the ridges of the image are extracted using digital image processing method including smoothing operation, 2-D Laplacian arithmetic operator and thinning, finally, the principle of detecting the lines of Hough transform is used. Based on the instantaneous frequency extracted result, the TF filtering method based on instantaneous frequency is developed, by which the special monocomponent is separated from the original signal.
(4) Nonstationary feature extraction base on Empirical Mode Decomposition (EMD) is studied. Based on the basal principle of empirical mode decomposition (EMD), three existing problems from EMD, that is, end effects, the select of sifting stop guide and mode mixing, are analyzed. For these limits of conventional EMD, some improved methods are proposed for the precision and correctness of EMD, combining the problems occurring in practical applications. Two novel solutions to mode mixing problems are presented in detail. One is expending best wavelet packet decomposition (WPD) into EMD, that is, applying WPD based on Shannon entropy into the selected (IMF) in which the mixing mode exists. The second novel solution to the problem of mode mixing is the masking signal method. The experiment results verify that these two methods both are benefit to the improvement of EMD decomposition ability.
Our final intention is applying all these nonstationary feature extraction techniques to solve the practical engineering problems, transforming the science technique to productivity. A number of practical signal analysis examples indicate that synthetically applying above feature extraction methods, the exact diagnosis result can be attained. As the carrier of these extraction techniques, the remote measurement and diagnosis service system proposed in the dissertation realizes the amalgamation of E-commerce, information network and industry intranet. Its overall architecture are researched and designed, and some key techniques including virtual instrument、DataSocket remote data transferring、neural network study and diagnosis、network security technique etc. are introduced in detail.
The methods of nonstationary signal feature extraction developed in this dissertation take on the important reference value for mechanical equipment fault diagnosis. The remote measurement and diagnosis service system in E-commerce environment can cut the time of collecting fault, improve the efficiency of diagnosis system, and favor accumulating data and sharing resources. All the studies are practical and realistic, and full of innovation. The research productions are beneficial for theory study and development practice in the nearly future.
引文
[1] 孙惠国,张瑞林.设备状态监测与故障诊断.河南:河南科学技术出版社,1993
[2] 屈梁生,何正嘉.机械故障诊断学.上海:上海科学技术出版社,1986
[3] 廖伯瑜.机械故障诊断基础.北京:冶金工业出版社,1991
[4] 钟秉林,黄仁.机械故障诊断学.北京:机械工业出版社,1997
[5] 何正嘉,孟庆丰,赵纪元.非平稳机械动态信号的时频分析,动态分析与测试技术,1992(3):5~11
[6] 秦树人.虚拟仪器及其最新发展.振动、测试与诊断.2000,28(6):123~129
[7] 柏 林 , 秦 树人 , 汤 宝 平 . 基 于 虚 拟 仪 器 的 预 测 维 修 系 统 的 研发 . 中国机械工程2003,Vol.14(16):1400~1404
[8] 柏林 , 汤宝平 , 王晓峰 , 秦树人 . 基于虚拟仪器的预测维修系统 . 设备管理与维修.2001,7:27~29
[9] Al-Dhaher, A.H.G. Applied virtual instrumentation [Book Reviews].Instrumentation & Measurement Magazine, IEEE , 2001,4(1):59~59
[10] Wang, J.-L;Liu, J.-Y. Virtual integrated test and virtual instrument technology. Beijing Huagong Daxue Xuebao(Ziran Kexueban)/Journal of Beijing University of Chemical Technology. 2001,28(2):84~88
[11] 何 岭 松 、 王 峻 峰 . 基 于 因 特 网 的 设 备 故 障 远 程 协 作 诊 断 技 术 . 中 国 机 械 工程,1999,10(3):34~36.
[12] Brooks R A. In telligence Without Representation.Artificial Intelligence,1991(47): 139~160
[13] Spoelder, H.J.W. Virtual instrumentation and virtual environments. Instrumentation & Measurement Magazine, IEEE.1999,2(3):14~19
[14] 刘德勇、朱明富 . 基于 Internet 的远程协作故障诊断系统技术 . 现代电子技术 2001(12):23~27.
[15] 王文利、段宝岩等. 基于网络的机械设备远程监测与故障诊断. 制造业自动化, 21(4):32~33.
[16] 鲍文博,徐玉秀,等. 低频动力机械基础非线性振动特性. 东北大学学报,2001,22(1):5~7
[17] 杨文平.基于现代非线性理论的复杂机械故障诊断与预测和电磁轴承控制系统的研究:[博士后出站报告].西安:西北工业大学,2002
[18] 寇惠,原培新. 故障诊断的振动信号处理. 北京:冶金工业出版社,1989
[19] 张贤达.现代信号处理(第二版),北京:清华大学出版社,2002
[20] 卢文详,杜润生.机械工程测试·信息·信号分析. 武汉:华中理工大学出版社,1999
[21] 钟佑明 , 秦树人 , 汤宝平 . 傅立叶变换与小波变换的统一数学模型 . 机械工程学报.2002,38(11):136~141
[22] 朱洪俊,秦树人,彭丽玲.小波变换对突变信号峰值奇异点的精确检测.机械工程学报.2002,38(12):10~15
[23] 钟佑明,汤宝平,秦树人.基于直接算法的虚拟式小波变换信号分析仪.重庆大学学报,2002,25(3):10~14
[24] 纪跃波,秦树人,汤宝平.基于多分辨分析的时频分析.振动与冲击.2002,21(1):12~15
[25] 朱洪俊,秦树人,曹怡.心电信号中 R 波的小波探测法.重庆大学学报.2002,25(5):108~111
[26] 丁康.平稳和非平稳振动信号的若干处理方法及发展.振动工程学报,2003,16(1):1~10
[27] Djurovic, I. Stankovic, L. A virtual instrument for time-frequency analysis. IEEE Transactions on Instrumentation and Measurement 1999,48(6):1086 ~1092
[28] He Zhengjia, Meng Qingfeng,Zhao Jiyuan, et al. Time-frequency analysis and diagnosis for nonstationary dynamic signal of machinery. International Journal of Plant Engineering and Management, 1996, 1(1):40~47
[29] 卢奂采.虚拟仪器技术的发展及现状.自动化仪表.2001,22(11):1~3
[30] 应怀樵,刘进明,沈松.DASP 软件虚拟仪器库系统的设计开发和应用.计算机自动测量与控制.2001,9(21):25~29
[31] 郭瑜,秦树人,梁玉前.时频分析阶比跟踪技术.重庆大学学报.2002,25(5):17~20
[32] 柏林,秦树人.XML 技术与虚拟仪器的融合.电子与信息学报.2003,25(增刊):366~369
[33] 季忠,秦树人,彭丽玲.脑电信号的现代分析方法.重庆大学学报.2002,25(10):108~112
[34] Goldberg, Harold. What is virtual instrumentation. IEEE Instrumentation and Measurement Magazine.2000,3(4):10~13
[35] Chung-Ping Young; Devaney, M.J. Shyh-Chyang Wang. Universal serial bus enhances virtual instrument-based distributed power monitoring. IEEE Transactions on Instrumentation and Measurement , 2001, 50(6):1692 ~ 1697
[36] Tondre, H., Bernal, A., Valle, J. VLSTA: virtual instrument standard test architecture. Aerospace and Electronic Systems Magazine, IEEE.1995, 10(3): 19 ~28
[37] 黄磊.基于 Internet 的远程机械故障诊断技术研究.计算机自动测量与控制,2000,8(4 ):13~ 15
[38] 史挺进,缪思恩,邱忠字等.分布式网络化状态监测与故障诊断系统. 发电设备, 1999, (2):22~26.
[39] 周东华,王桂珍. 故障诊断技术综述. 化工自动化及仪表 1998.25(1):58～62.
[40] 徐敏等. 设备故障诊断手册.西安:西安交通大学出版社,1998
[41] Yan, T. Ota, J. etc. Concept design of remote fault diagnosis system for autonomous mobile robots.IEEE International Conference on Intelligent Robots and Systems,2000,(2):931~ 936
[42] C .L Churms. etc. The remote control of nuclear microprobes over the Internet. Nuclear instruments and methods in physics research, 1999, (158):124 ~128.
[43] Caldwell, Nicholas H.M. Remote instrument diagnosis on the Internet. IEEE Intelligent Systems & Their Applications,1998,(13):70 ~76.
[44] 梅宏斌等.滚动轴承振动监测与诊断理论·方法·系统.北京:机械工业出版社,1996
[45] 王肇琪,付勤毅.滚动轴承故障的振动监测方法.有色矿山,1999(1):36 ~40
[46] Dyer, R. M. Stewart. Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis. Transactions of the ASME, Journal of Mechanical Design,1978,100(4):229~235
[47] T. Koizumi, M. Kiso, R. Taniguchi. Preventive Maintenance for roller and Journal Bearings of introduction Motor Based on the Diagnostic Signature Analysis. Transactions of the ASME. Journal of Vibration, Acoustics, Stress and Reliability in Design, 1986, 108(1):26 ~31
[48] R .B. Randall. Analysis Techniques for Diagnosis of Gear and Bearing Faults. Proceedings of CSMDT 86.Shenyang China,1986:387 ~393
[49] 李润方,王建军.齿轮系统动力学.北京:科学出版社,1996
[50] 钱济国,吴森,齿轮故障的振动与分析.振动、测试、诊断,1990, 10 (1):36 ~41
[51] 丁康,朱小勇等,齿轮箱典型故障振动特征与诊断策略,振动与冲击,2001,20(3):7~12
[52] Brennan,M.J,Chen,M.H,Reynolds olds,A.G, Use of vibration measurements to detect local tooth defects in gears,Sound and vibration,1997
[53] Huang,K.J and Liu,T.S,Dynamic analysis of a spur gear by the dynamic stiff ness method Sound and vibration,2000,234(2):311~329
[54] 崔锦泰著.小波分析导论. 程正兴译. 西安:西安交通大学出版社,1995
[55] 刘贵忠,邸双亮.小波分析及其应用.西安:西安电子科技大学出版社,992
[56] 陈涛,梁生.小波分析的工程理解及其在机械诊断中的应用.动态分析与测试技术,1996,4(3):0~16
[57] Newland DE. Wavelet analysis of vibration, Part 1: Theory. Journal of Vibration and Acoustics,1994,116(8.):417~457
[58] Bruce A, Donoho D, Gao Hongye. Wavelet analysis. IEEE Spectrum,.1996, 8:26~35
[59] O.Rioul and M.Vetterli. Wavelets and Signal Processing. IEEE Signal Processing Magazine, 1991,8(4): 1207~1213
[60] 陈泽鑫.小波基函数在故障诊断中的最佳选择.机械科学与技术,005(2):25~28
[61] 周春健. 基于小波变换的旋转机械故障诊断[硕士论文]. 南京航空航天大学硕士学位论文. 2004
[62] 朱洪俊,王忠,秦树人.小波变换对瞬态信号特征信息的精确提取.机械工程学报 2005年 12 月
[63] 张志禹,汪文秉,顾家柳. 小波域内瞬时频率提取方法研究.西安交通大学学报,2001 年 8月
[64] 张征平,任震,黄雯莹,管霖,杨楚明,何建军.基于小波脊线的电动机转子故障检测新方法.中国电机工程学报,2003 年 1 月
[65] J.M.Aller, T.G.Habetler, R.G.Harley, R.M. Tallam and S.B.Lee. “Sensorless speed measurement of AC machines using Analytic Wavelet Transform,” IEEE Trans on Industry Applications, 2002,38(5):1344~1350
[66] A.W. Rihaczek. Hilbert transforms and the complex representation of real signals. Proc.IEEE, 1966,54(3):34~435
[67] J.M.Aller, T.G.Habetler, R.G.Harley, R.M. Tallam and S.B.Lee. “Sensorless speed measurement of AC machines using Analytic Wavelet Transform,” IEEE Trans on Industry Applications, 2002,38(5):1344~1350
[68] 何晓霞,沈玉娣,张西宁.连续小波变换在滚动轴承故障诊断中的应用,机械科学与技术,2001,20(4):571~573
[69] 陈保家,李 力,张 园.尺度—小波能量谱在内燃机故障诊断中的应用.内燃机学报,2006,24(3):284~287
[70] 江 涌.小波变换能量谱在滚动轴承故障诊断中的应用 轴承 2005,(07): 31~33
[71] 刘忠祥,邱阿瑞,刘玉.小波变换在滚动轴承故障诊断中的应用. 机械工程师,2002, (4):44~45
[72] 傅勤毅,章易程,应力军,李国顺. 滚动轴承故障特征的小波提取方法.机械工程学报,2001,02:64~68.
[73] 程军圣,于德介,邓乾旺,杨宇,张邦基.时间-小波能量谱在滚动轴承故障诊断中的应用.振动与冲击,2004,32(02): 34~36
[74] Chui C K. An Introduction to wavelets. Boston: Academic Press,1992
[75] Chui C K. Wavelets: A tutorial in theory and application. Boston: Academic Press,1992
[76] I. Cohen, S. Raz and D. Malah, “Orthonormal shift invariant wavelet packet decomposition and representation”, Sig.Proc., 1997,57(3): 251~270.
[77] 杨守志,正兴.混合正交小波基的构造.西安交通大学学报,2000,4(1):97~99
[78] 程正兴.小波分析算法与应用.西安交通大学出版,1998
[79] 刘世元,杜润生,杨叔子.小波包改进算法及其在柴油机振动诊断中的应用.内燃机学报2000,18(1):11~15
[80] 王海清,宋执环,李平.改进小波包分频算法及在故障检测中的应用.浙江大学学报(工学版),2001,35(3):307~311
[81] 赵建林,代月明,张冬梅,朱习军.基于小波包的机械故障信号消噪处理.煤矿机械 ,2005,(09) :141~142
[82] 丁锋,何正嘉,张新运.小波包分析在滚动轴承信号消噪处理中的应用.西安工业学院学报 2006,26(1): 8~11
[83] 赵华,刘荣林,于艳红.小波包分析在电机信号消噪处理中的应用.中国民航学院学报 ,2004,22(2): 13~16
[84] 万军 , 蒋世祥 , 蔡智勇 . 旋转机械振动信号的小波包分解及故障检测 . 汽轮机技术 ,2002,44(2): 95~97
[85] 卢学军,缪思恩,隋明霖,顾晃.旋转机械振动信号的小波包分解及故障检测.仪器仪表学报,2002,23(5) : 484~487
[86] 杨文平,陈国定,石博强等.基于小波包时域重构的汽车发动机燃烧状态分析.机械科学与技术,2002,21(2):23~26
[87] 马晓建,陈瑞琪,吴文英,周保堂,贺世正.机械故障诊断中常用解调方法的比较及应用。东华大学学报(自然科学版).2001,27(5):105~108
[88] 郑晓溪,丁康,江利旗.希尔伯特变换解调分析在故障诊断中应用的局限性研究.汕头大学学报(自然科学版),1999,14(2):50~54
[89] 丁康等. 解调分析在故障诊断中应用的局限性研究, 振动工程学报. 1997,10(1):235~240
[90] 黄大吉,赵进平,苏纪兰.希尔伯特—黄变换的端点延拓.2003,25(1):10~11
[91] 丁康等. 解调分析的一种优化方法. 振动工程学报,1998,11(2):235~240
[92] I. Pitas and A.N. Venetsanopoulos. Adaptive filters based on order statistics. IEEE transactions on signal Processing, 1991,39(2):518~522
[93] Akira T., Mitsuhiko M. Adaptive L-filters Based on Fuzzy Rules. ISCAS 1995, 1995, 2(5):961~964
[94] A. Potamianos and P. Maragos, A comparison of the energy operate and Hilbert transform approach to the signal and speech demodulation. Sig.Proc., 1994, (37):50~148
[95] J. F. Kaiser, Some useful properties of Teager’s energy operators. Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, 1993, (3): 140~158.
[96] Choi B K, Kim D W. Bounded deviation joint path algorithms for piecewise cubic polynomial trajectories. IEEE Transactions on Systems, Man and Cybernetics,1990, 20 (3) : 725~733
[97] 丁康,陈健林,苏向荣.平稳和非平稳振动信号的若干处理方法及发展.振动工程学报,2003,16(1):1~6
[98] 张贤达,保铮.非平稳信号分析与处理.北京:国防工业出版社,1998
[99] Qian S, Chen D. Joint Time-frequency Analysis: Methods and Application. New York: Prentice Hall,1996
[100] Qian S, Chen D. Joint time-frequency analysis. IEEE Signal Processing Mag.,1999, 16(5):52~67
[101] 马世伟.非平稳信号的参数自适应时频表示及其应用的研究[博士论文].上海:上海大学,2000
[102] Stephane M. and Zhifeng Z. Adaptive Time-Frequency Decomposition with Matching Pursuits. Trans on Signal Processing, 1993, 41(11):3397~3415.
[103] Shiwei M., Guanghua C. Deng Jiamei . Adaptive Time - Frequency Distribution Based on Linear Chirp Modulated Gaussian Function. Journal of Shanghai University, 2000,4 (1) :31~37.
[104] Shie Qian and Dapang Chen, Decomposition of the Wigner-Ville Distribution and Time-Frequency Distribution Series. IEEE Trans on Signal Processing, 1994, 42(10):2205~2209.
[105] 魏茂刚、阮成礼.时频分布级数方法的改进.电子科技大学学报,2004,33(1):49~50
[106] Lancaster P., Salkauskas K. Surfaces Generated by Moving Least Squares Methods. Mathematics of Computation, 1981, 37 (5): 141~158.
[107] Jones, D.L. Baraniuk, R.G.. A Simple Scheme for Adapting Time-Frequency Representation, Signal Processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1994,42(12):3530~3535
[108] Qian Shie, Chen Dapang, Yin Qinye. Adaptive Chirplet Based Signal Approximation. IEEE Transon Acoustics Speech and Signal processing, 1998, 3 (5) : 1781~1784
[109] 邹虹,保铮.一种有效的基于 Chirplet 自适应信号分解算法.电子学报, 2001, 29 (4): 515~517
[110] Yin Qinye, Qian Shie, Feng Aigang. A Fast Refinement for Adaptive Gaussian Chirplet Decomposition. IEEE Transon Signal Processing, 2002, 50 (6) : 1298~1306
[111] 田光明,基于能量峰区域时频滤波的信号估计,信号处理,2004,20(3):263~265
[112] 倪振华.振动力学.西安:西安交通大学出版社.1989.
[113] Baraniuk R G, Jones D L. Signal-dependent time-frequency analysis using a radically Gaussian kernel. Signal Processing, 1993: 263~284
[114] Van den Elsen P A,Antoine Maintz JB , Evertjan D P,etal. Image fusion using geometricalfeatures. Visualization in Biomedical Computing,1992 ,1808:172~185
[115] Carmona R A, Hwang W L, Torresani B. Multiridge detection and time - frequency reconstruction. IEEE Transactions Signal Processing, 1999, 47 (2): 2480~2492.
[116] Ruan Q Q. Digital image processing. Beijing: Publishing House of Electronics Industry, 2001: 425~426.
[117] Xu L,Oja E.Randomized Hough transform: basic mechanism, algorithms, and computational complexities. CVGIP: Image Understanding,1993,57(2):131~154.
[118] 邹长春等一类正弦曲线的 hough 变换快速 计算机工程与应用:2002.4(1):10~13
[119] G.F.Boudreaux-Bartels and T.W.Parks. Time-varying filtering and signal estimation using Wigner distribution synthesis techniques. IEEE Transactions on Acoustics Speech and Signal processing, 1986,34(6):442~451
[120] B.E.A.Saleh and N.S.Subotic. Time-variant filtering of signals in the mixed time-frequency domain. IEEE Transactions on Acoustics Speech and Signal processing, 1985, 33(6):479~485
[121] 杨世锡,胡劲松吴,昭同等.旋转机械振动信号给予 EMD 的 Hilbert 和小波变换时频分析比较.中国电机工程学报 2003,23(6):102~107.
[122] Ch Loh. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses. Bulletin of the Semological Society of America, 2001,91:1339~1357
[123] Huang N E, Shen Zheng, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for non linear and non-stationary time series analysis, Proc. R. Soc. London. Ser. A,1998, 454(1971):903~995
[124] 钟佑明 , 秦树人 , 汤宝平 .Hilbert-Huang 变换中的理论研究 , 振动与冲击 ,2002 , 21(4):13~17
[125] Huang N E , Wu M L, Long S R, et al.. A confidence limit for the empirical mode decomposition and Hilbert spectrum analysis. Proc. R. Soc. London. Ser. A, 2003,459(2037):2317~2345.
[126] Loh C H. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses.Bulletin of the Seismological Society of America, 2001(91):1339~1357
[127] Schlurmann T. Spectral Analysis of Nonlinear water Waves Based on the Hilbert-Huang transformation. Journal of Offshore Mechanics and Arctic Engineering Transactions of the ASME , 2002. 124(1) : 22~27.
[128] 黄大吉,赵进平,苏纪兰.希尔伯特一黄变换的端点延拓.海洋学报,2003,1:1~11
[129] Zhao Jinping. Improvement of Mirror Extending in Empirical Mode Decomposition Method and the Technology for Eliminating Frequency Mixing. HIGH TECHNOLOGY LETTERS,2002,8(3):40~47
[130] Zhao Jin-ping, Huang Da-ji. Mirror Extending and Circular Spline Function For Empirical Mode Decomposition Method. Journal of Zhejiang University(Science),2001,2(3):247~252
[131] 牛玉广,陈彭,宋之平.整周期采样信号的局部频谱细化.动态分析与测试技术,1996,3: 21~25
[132] 刘慧婷,陈家兴等.基于多项式拟合算法的 EMD 端点问题的处理.计算机工程与应用,2004.16: 84~86
[133] 邓拥军,王伟,钱成春等.EMD 方法及 Hilbert 变换中边界问题的处理.科学通报,2001,46(3):257~263
[134] 胡劲松,吴昭同,严拱标.提高旋转机械振动信号整周期采样精度的一种方法.浙江大学学报(工学版),2002,36( 3):273~274
[135] 赵进平.异常事件对 EMD 方法的影响及其解决方法研究.青岛海洋大学学报,2001,31( 6 ): 805~814
[136] 王秉仁.希尔伯特变换在机械故障诊断中的应用.水利电力机械,1998,1:43~45
[137] Boashash. Estimating and Interpreting the Instantaneous Frequency. Part1:Fundamentals,Proceedings of the IEEE, 1992,80(4):520~538
[138] 孟宗,戴桂平,刘彬.基于 EMD 时频分析方法的性能研究.传感技术学报,2006(04): 25~27
[139] Ryan Deering, James F.Kaiser. The use of a masking signal to improve empirical mode decomposition. ICASSP 2005 IEEE. 485~488.
[140] G. Rilling, P. Goncalves, “On empirical mode decomposition and its algorithms,”in IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03,2003
[141] S.Hahn, Hilbert Transforms in signal Processing , Artech House, Boston, 1996
[142] 贾民平,许飞云,钟秉林等.远程分布式汽轮机组故障诊断网络系统的研究. 中国电机工程学报,1998,18(2):60~63
[143] 张金玉.基于网络的远程诊断与处理支持中心的研究[博士学位论文].西安:西安交通大学
[144] 李东晓,史铁林,杨叔子.分布式监测诊断系统的设计思想及实现.华中理工大学学报,1997,25(11):31~32
[145] Engelmann U, Schroter A, Baur U etc. A three-generation model for teleradiology. IEEE Trans on Information Technology in Biomedicine, 1998,2(1).20～25
[146] 何志伟,王承.基于虚拟仪器的网络化自动测试系统的构架及实现.电子技术应用.2001,27(11):58~59
[147] 卢奂采.虚拟仪器技术的发展及现状.自动化仪表.2001,22(11):1~3
[148] 严骏,赵立强,凌海凤.基于 DataSocket 的工程机械液压系统远程故障监测与诊断系统研究.机床与液压,2006,19: 48~49
[149] 陈新宇,周锋,王丽华,荀东升.设备远程实时监测系统的研究.电气传动,2005 ,35 (2): 130~132
[150] 孙 欣, 丁香乾, 石 硕.BP 神经网络在柴油机涡轮增压系统故障诊断中的应用.计算机应用研究,2006(5):202~203
[151] 胡 婧,杨曙年.基于 BP 神经网络的滚动轴承故障诊断研究.机械与电子,2006(4):9~10
[152] S.T.Kent and J.Lin. ”Privacy Enhancement for Internet Electronic Mail:Part II: Centificate-Based Key Management” rfc 1114, 1989
[153] M.Ogiwara. “A Method for Generating Cryptographically strong Primes”, The Trans. Of IEEICE, 1990(73):985~994
[154] R.C Merk. “Secrecy, Authentication and Public Key System”, Ann arbor:UMI Research Press,1982.
[155] J.Hastad. “On Using RSA With Lower Exponent in Public Key Network”, Proceeding CRYPTO’s 85 New York: Springer-Verlag,1986.
[2] 屈梁生,何正嘉.机械故障诊断学.上海:上海科学技术出版社,1986
[3] 廖伯瑜.机械故障诊断基础.北京:冶金工业出版社,1991
[4] 钟秉林,黄仁.机械故障诊断学.北京:机械工业出版社,1997
[5] 何正嘉,孟庆丰,赵纪元.非平稳机械动态信号的时频分析,动态分析与测试技术,1992(3):5~11
[6] 秦树人.虚拟仪器及其最新发展.振动、测试与诊断.2000,28(6):123~129
[7] 柏 林 , 秦 树人 , 汤 宝 平 . 基 于 虚 拟 仪 器 的 预 测 维 修 系 统 的 研发 . 中国机械工程2003,Vol.14(16):1400~1404
[8] 柏林 , 汤宝平 , 王晓峰 , 秦树人 . 基于虚拟仪器的预测维修系统 . 设备管理与维修.2001,7:27~29
[9] Al-Dhaher, A.H.G. Applied virtual instrumentation [Book Reviews].Instrumentation & Measurement Magazine, IEEE , 2001,4(1):59~59
[10] Wang, J.-L;Liu, J.-Y. Virtual integrated test and virtual instrument technology. Beijing Huagong Daxue Xuebao(Ziran Kexueban)/Journal of Beijing University of Chemical Technology. 2001,28(2):84~88
[11] 何 岭 松 、 王 峻 峰 . 基 于 因 特 网 的 设 备 故 障 远 程 协 作 诊 断 技 术 . 中 国 机 械 工程,1999,10(3):34~36.
[12] Brooks R A. In telligence Without Representation.Artificial Intelligence,1991(47): 139~160
[13] Spoelder, H.J.W. Virtual instrumentation and virtual environments. Instrumentation & Measurement Magazine, IEEE.1999,2(3):14~19
[14] 刘德勇、朱明富 . 基于 Internet 的远程协作故障诊断系统技术 . 现代电子技术 2001(12):23~27.
[15] 王文利、段宝岩等. 基于网络的机械设备远程监测与故障诊断. 制造业自动化, 21(4):32~33.
[16] 鲍文博,徐玉秀,等. 低频动力机械基础非线性振动特性. 东北大学学报,2001,22(1):5~7
[17] 杨文平.基于现代非线性理论的复杂机械故障诊断与预测和电磁轴承控制系统的研究:[博士后出站报告].西安:西北工业大学,2002
[18] 寇惠,原培新. 故障诊断的振动信号处理. 北京:冶金工业出版社,1989
[19] 张贤达.现代信号处理(第二版),北京:清华大学出版社,2002
[20] 卢文详,杜润生.机械工程测试·信息·信号分析. 武汉:华中理工大学出版社,1999
[21] 钟佑明 , 秦树人 , 汤宝平 . 傅立叶变换与小波变换的统一数学模型 . 机械工程学报.2002,38(11):136~141
[22] 朱洪俊,秦树人,彭丽玲.小波变换对突变信号峰值奇异点的精确检测.机械工程学报.2002,38(12):10~15
[23] 钟佑明,汤宝平,秦树人.基于直接算法的虚拟式小波变换信号分析仪.重庆大学学报,2002,25(3):10~14
[24] 纪跃波,秦树人,汤宝平.基于多分辨分析的时频分析.振动与冲击.2002,21(1):12~15
[25] 朱洪俊,秦树人,曹怡.心电信号中 R 波的小波探测法.重庆大学学报.2002,25(5):108~111
[26] 丁康.平稳和非平稳振动信号的若干处理方法及发展.振动工程学报,2003,16(1):1~10
[27] Djurovic, I. Stankovic, L. A virtual instrument for time-frequency analysis. IEEE Transactions on Instrumentation and Measurement 1999,48(6):1086 ~1092
[28] He Zhengjia, Meng Qingfeng,Zhao Jiyuan, et al. Time-frequency analysis and diagnosis for nonstationary dynamic signal of machinery. International Journal of Plant Engineering and Management, 1996, 1(1):40~47
[29] 卢奂采.虚拟仪器技术的发展及现状.自动化仪表.2001,22(11):1~3
[30] 应怀樵,刘进明,沈松.DASP 软件虚拟仪器库系统的设计开发和应用.计算机自动测量与控制.2001,9(21):25~29
[31] 郭瑜,秦树人,梁玉前.时频分析阶比跟踪技术.重庆大学学报.2002,25(5):17~20
[32] 柏林,秦树人.XML 技术与虚拟仪器的融合.电子与信息学报.2003,25(增刊):366~369
[33] 季忠,秦树人,彭丽玲.脑电信号的现代分析方法.重庆大学学报.2002,25(10):108~112
[34] Goldberg, Harold. What is virtual instrumentation. IEEE Instrumentation and Measurement Magazine.2000,3(4):10~13
[35] Chung-Ping Young; Devaney, M.J. Shyh-Chyang Wang. Universal serial bus enhances virtual instrument-based distributed power monitoring. IEEE Transactions on Instrumentation and Measurement , 2001, 50(6):1692 ~ 1697
[36] Tondre, H., Bernal, A., Valle, J. VLSTA: virtual instrument standard test architecture. Aerospace and Electronic Systems Magazine, IEEE.1995, 10(3): 19 ~28
[37] 黄磊.基于 Internet 的远程机械故障诊断技术研究.计算机自动测量与控制,2000,8(4 ):13~ 15
[38] 史挺进,缪思恩,邱忠字等.分布式网络化状态监测与故障诊断系统. 发电设备, 1999, (2):22~26.
[39] 周东华,王桂珍. 故障诊断技术综述. 化工自动化及仪表 1998.25(1):58～62.
[40] 徐敏等. 设备故障诊断手册.西安:西安交通大学出版社,1998
[41] Yan, T. Ota, J. etc. Concept design of remote fault diagnosis system for autonomous mobile robots.IEEE International Conference on Intelligent Robots and Systems,2000,(2):931~ 936
[42] C .L Churms. etc. The remote control of nuclear microprobes over the Internet. Nuclear instruments and methods in physics research, 1999, (158):124 ~128.
[43] Caldwell, Nicholas H.M. Remote instrument diagnosis on the Internet. IEEE Intelligent Systems & Their Applications,1998,(13):70 ~76.
[44] 梅宏斌等.滚动轴承振动监测与诊断理论·方法·系统.北京:机械工业出版社,1996
[45] 王肇琪,付勤毅.滚动轴承故障的振动监测方法.有色矿山,1999(1):36 ~40
[46] Dyer, R. M. Stewart. Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis. Transactions of the ASME, Journal of Mechanical Design,1978,100(4):229~235
[47] T. Koizumi, M. Kiso, R. Taniguchi. Preventive Maintenance for roller and Journal Bearings of introduction Motor Based on the Diagnostic Signature Analysis. Transactions of the ASME. Journal of Vibration, Acoustics, Stress and Reliability in Design, 1986, 108(1):26 ~31
[48] R .B. Randall. Analysis Techniques for Diagnosis of Gear and Bearing Faults. Proceedings of CSMDT 86.Shenyang China,1986:387 ~393
[49] 李润方,王建军.齿轮系统动力学.北京:科学出版社,1996
[50] 钱济国,吴森,齿轮故障的振动与分析.振动、测试、诊断,1990, 10 (1):36 ~41
[51] 丁康,朱小勇等,齿轮箱典型故障振动特征与诊断策略,振动与冲击,2001,20(3):7~12
[52] Brennan,M.J,Chen,M.H,Reynolds olds,A.G, Use of vibration measurements to detect local tooth defects in gears,Sound and vibration,1997
[53] Huang,K.J and Liu,T.S,Dynamic analysis of a spur gear by the dynamic stiff ness method Sound and vibration,2000,234(2):311~329
[54] 崔锦泰著.小波分析导论. 程正兴译. 西安:西安交通大学出版社,1995
[55] 刘贵忠,邸双亮.小波分析及其应用.西安:西安电子科技大学出版社,992
[56] 陈涛,梁生.小波分析的工程理解及其在机械诊断中的应用.动态分析与测试技术,1996,4(3):0~16
[57] Newland DE. Wavelet analysis of vibration, Part 1: Theory. Journal of Vibration and Acoustics,1994,116(8.):417~457
[58] Bruce A, Donoho D, Gao Hongye. Wavelet analysis. IEEE Spectrum,.1996, 8:26~35
[59] O.Rioul and M.Vetterli. Wavelets and Signal Processing. IEEE Signal Processing Magazine, 1991,8(4): 1207~1213
[60] 陈泽鑫.小波基函数在故障诊断中的最佳选择.机械科学与技术,005(2):25~28
[61] 周春健. 基于小波变换的旋转机械故障诊断[硕士论文]. 南京航空航天大学硕士学位论文. 2004
[62] 朱洪俊,王忠,秦树人.小波变换对瞬态信号特征信息的精确提取.机械工程学报 2005年 12 月
[63] 张志禹,汪文秉,顾家柳. 小波域内瞬时频率提取方法研究.西安交通大学学报,2001 年 8月
[64] 张征平,任震,黄雯莹,管霖,杨楚明,何建军.基于小波脊线的电动机转子故障检测新方法.中国电机工程学报,2003 年 1 月
[65] J.M.Aller, T.G.Habetler, R.G.Harley, R.M. Tallam and S.B.Lee. “Sensorless speed measurement of AC machines using Analytic Wavelet Transform,” IEEE Trans on Industry Applications, 2002,38(5):1344~1350
[66] A.W. Rihaczek. Hilbert transforms and the complex representation of real signals. Proc.IEEE, 1966,54(3):34~435
[67] J.M.Aller, T.G.Habetler, R.G.Harley, R.M. Tallam and S.B.Lee. “Sensorless speed measurement of AC machines using Analytic Wavelet Transform,” IEEE Trans on Industry Applications, 2002,38(5):1344~1350
[68] 何晓霞,沈玉娣,张西宁.连续小波变换在滚动轴承故障诊断中的应用,机械科学与技术,2001,20(4):571~573
[69] 陈保家,李 力,张 园.尺度—小波能量谱在内燃机故障诊断中的应用.内燃机学报,2006,24(3):284~287
[70] 江 涌.小波变换能量谱在滚动轴承故障诊断中的应用 轴承 2005,(07): 31~33
[71] 刘忠祥,邱阿瑞,刘玉.小波变换在滚动轴承故障诊断中的应用. 机械工程师,2002, (4):44~45
[72] 傅勤毅,章易程,应力军,李国顺. 滚动轴承故障特征的小波提取方法.机械工程学报,2001,02:64~68.
[73] 程军圣,于德介,邓乾旺,杨宇,张邦基.时间-小波能量谱在滚动轴承故障诊断中的应用.振动与冲击,2004,32(02): 34~36
[74] Chui C K. An Introduction to wavelets. Boston: Academic Press,1992
[75] Chui C K. Wavelets: A tutorial in theory and application. Boston: Academic Press,1992
[76] I. Cohen, S. Raz and D. Malah, “Orthonormal shift invariant wavelet packet decomposition and representation”, Sig.Proc., 1997,57(3): 251~270.
[77] 杨守志,正兴.混合正交小波基的构造.西安交通大学学报,2000,4(1):97~99
[78] 程正兴.小波分析算法与应用.西安交通大学出版,1998
[79] 刘世元,杜润生,杨叔子.小波包改进算法及其在柴油机振动诊断中的应用.内燃机学报2000,18(1):11~15
[80] 王海清,宋执环,李平.改进小波包分频算法及在故障检测中的应用.浙江大学学报(工学版),2001,35(3):307~311
[81] 赵建林,代月明,张冬梅,朱习军.基于小波包的机械故障信号消噪处理.煤矿机械 ,2005,(09) :141~142
[82] 丁锋,何正嘉,张新运.小波包分析在滚动轴承信号消噪处理中的应用.西安工业学院学报 2006,26(1): 8~11
[83] 赵华,刘荣林,于艳红.小波包分析在电机信号消噪处理中的应用.中国民航学院学报 ,2004,22(2): 13~16
[84] 万军 , 蒋世祥 , 蔡智勇 . 旋转机械振动信号的小波包分解及故障检测 . 汽轮机技术 ,2002,44(2): 95~97
[85] 卢学军,缪思恩,隋明霖,顾晃.旋转机械振动信号的小波包分解及故障检测.仪器仪表学报,2002,23(5) : 484~487
[86] 杨文平,陈国定,石博强等.基于小波包时域重构的汽车发动机燃烧状态分析.机械科学与技术,2002,21(2):23~26
[87] 马晓建,陈瑞琪,吴文英,周保堂,贺世正.机械故障诊断中常用解调方法的比较及应用。东华大学学报(自然科学版).2001,27(5):105~108
[88] 郑晓溪,丁康,江利旗.希尔伯特变换解调分析在故障诊断中应用的局限性研究.汕头大学学报(自然科学版),1999,14(2):50~54
[89] 丁康等. 解调分析在故障诊断中应用的局限性研究, 振动工程学报. 1997,10(1):235~240
[90] 黄大吉,赵进平,苏纪兰.希尔伯特—黄变换的端点延拓.2003,25(1):10~11
[91] 丁康等. 解调分析的一种优化方法. 振动工程学报,1998,11(2):235~240
[92] I. Pitas and A.N. Venetsanopoulos. Adaptive filters based on order statistics. IEEE transactions on signal Processing, 1991,39(2):518~522
[93] Akira T., Mitsuhiko M. Adaptive L-filters Based on Fuzzy Rules. ISCAS 1995, 1995, 2(5):961~964
[94] A. Potamianos and P. Maragos, A comparison of the energy operate and Hilbert transform approach to the signal and speech demodulation. Sig.Proc., 1994, (37):50~148
[95] J. F. Kaiser, Some useful properties of Teager’s energy operators. Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, 1993, (3): 140~158.
[96] Choi B K, Kim D W. Bounded deviation joint path algorithms for piecewise cubic polynomial trajectories. IEEE Transactions on Systems, Man and Cybernetics,1990, 20 (3) : 725~733
[97] 丁康,陈健林,苏向荣.平稳和非平稳振动信号的若干处理方法及发展.振动工程学报,2003,16(1):1~6
[98] 张贤达,保铮.非平稳信号分析与处理.北京:国防工业出版社,1998
[99] Qian S, Chen D. Joint Time-frequency Analysis: Methods and Application. New York: Prentice Hall,1996
[100] Qian S, Chen D. Joint time-frequency analysis. IEEE Signal Processing Mag.,1999, 16(5):52~67
[101] 马世伟.非平稳信号的参数自适应时频表示及其应用的研究[博士论文].上海:上海大学,2000
[102] Stephane M. and Zhifeng Z. Adaptive Time-Frequency Decomposition with Matching Pursuits. Trans on Signal Processing, 1993, 41(11):3397~3415.
[103] Shiwei M., Guanghua C. Deng Jiamei . Adaptive Time - Frequency Distribution Based on Linear Chirp Modulated Gaussian Function. Journal of Shanghai University, 2000,4 (1) :31~37.
[104] Shie Qian and Dapang Chen, Decomposition of the Wigner-Ville Distribution and Time-Frequency Distribution Series. IEEE Trans on Signal Processing, 1994, 42(10):2205~2209.
[105] 魏茂刚、阮成礼.时频分布级数方法的改进.电子科技大学学报,2004,33(1):49~50
[106] Lancaster P., Salkauskas K. Surfaces Generated by Moving Least Squares Methods. Mathematics of Computation, 1981, 37 (5): 141~158.
[107] Jones, D.L. Baraniuk, R.G.. A Simple Scheme for Adapting Time-Frequency Representation, Signal Processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1994,42(12):3530~3535
[108] Qian Shie, Chen Dapang, Yin Qinye. Adaptive Chirplet Based Signal Approximation. IEEE Transon Acoustics Speech and Signal processing, 1998, 3 (5) : 1781~1784
[109] 邹虹,保铮.一种有效的基于 Chirplet 自适应信号分解算法.电子学报, 2001, 29 (4): 515~517
[110] Yin Qinye, Qian Shie, Feng Aigang. A Fast Refinement for Adaptive Gaussian Chirplet Decomposition. IEEE Transon Signal Processing, 2002, 50 (6) : 1298~1306
[111] 田光明,基于能量峰区域时频滤波的信号估计,信号处理,2004,20(3):263~265
[112] 倪振华.振动力学.西安:西安交通大学出版社.1989.
[113] Baraniuk R G, Jones D L. Signal-dependent time-frequency analysis using a radically Gaussian kernel. Signal Processing, 1993: 263~284
[114] Van den Elsen P A,Antoine Maintz JB , Evertjan D P,etal. Image fusion using geometricalfeatures. Visualization in Biomedical Computing,1992 ,1808:172~185
[115] Carmona R A, Hwang W L, Torresani B. Multiridge detection and time - frequency reconstruction. IEEE Transactions Signal Processing, 1999, 47 (2): 2480~2492.
[116] Ruan Q Q. Digital image processing. Beijing: Publishing House of Electronics Industry, 2001: 425~426.
[117] Xu L,Oja E.Randomized Hough transform: basic mechanism, algorithms, and computational complexities. CVGIP: Image Understanding,1993,57(2):131~154.
[118] 邹长春等一类正弦曲线的 hough 变换快速 计算机工程与应用:2002.4(1):10~13
[119] G.F.Boudreaux-Bartels and T.W.Parks. Time-varying filtering and signal estimation using Wigner distribution synthesis techniques. IEEE Transactions on Acoustics Speech and Signal processing, 1986,34(6):442~451
[120] B.E.A.Saleh and N.S.Subotic. Time-variant filtering of signals in the mixed time-frequency domain. IEEE Transactions on Acoustics Speech and Signal processing, 1985, 33(6):479~485
[121] 杨世锡,胡劲松吴,昭同等.旋转机械振动信号给予 EMD 的 Hilbert 和小波变换时频分析比较.中国电机工程学报 2003,23(6):102~107.
[122] Ch Loh. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses. Bulletin of the Semological Society of America, 2001,91:1339~1357
[123] Huang N E, Shen Zheng, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for non linear and non-stationary time series analysis, Proc. R. Soc. London. Ser. A,1998, 454(1971):903~995
[124] 钟佑明 , 秦树人 , 汤宝平 .Hilbert-Huang 变换中的理论研究 , 振动与冲击 ,2002 , 21(4):13~17
[125] Huang N E , Wu M L, Long S R, et al.. A confidence limit for the empirical mode decomposition and Hilbert spectrum analysis. Proc. R. Soc. London. Ser. A, 2003,459(2037):2317~2345.
[126] Loh C H. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses.Bulletin of the Seismological Society of America, 2001(91):1339~1357
[127] Schlurmann T. Spectral Analysis of Nonlinear water Waves Based on the Hilbert-Huang transformation. Journal of Offshore Mechanics and Arctic Engineering Transactions of the ASME , 2002. 124(1) : 22~27.
[128] 黄大吉,赵进平,苏纪兰.希尔伯特一黄变换的端点延拓.海洋学报,2003,1:1~11
[129] Zhao Jinping. Improvement of Mirror Extending in Empirical Mode Decomposition Method and the Technology for Eliminating Frequency Mixing. HIGH TECHNOLOGY LETTERS,2002,8(3):40~47
[130] Zhao Jin-ping, Huang Da-ji. Mirror Extending and Circular Spline Function For Empirical Mode Decomposition Method. Journal of Zhejiang University(Science),2001,2(3):247~252
[131] 牛玉广,陈彭,宋之平.整周期采样信号的局部频谱细化.动态分析与测试技术,1996,3: 21~25
[132] 刘慧婷,陈家兴等.基于多项式拟合算法的 EMD 端点问题的处理.计算机工程与应用,2004.16: 84~86
[133] 邓拥军,王伟,钱成春等.EMD 方法及 Hilbert 变换中边界问题的处理.科学通报,2001,46(3):257~263
[134] 胡劲松,吴昭同,严拱标.提高旋转机械振动信号整周期采样精度的一种方法.浙江大学学报(工学版),2002,36( 3):273~274
[135] 赵进平.异常事件对 EMD 方法的影响及其解决方法研究.青岛海洋大学学报,2001,31( 6 ): 805~814
[136] 王秉仁.希尔伯特变换在机械故障诊断中的应用.水利电力机械,1998,1:43~45
[137] Boashash. Estimating and Interpreting the Instantaneous Frequency. Part1:Fundamentals,Proceedings of the IEEE, 1992,80(4):520~538
[138] 孟宗,戴桂平,刘彬.基于 EMD 时频分析方法的性能研究.传感技术学报,2006(04): 25~27
[139] Ryan Deering, James F.Kaiser. The use of a masking signal to improve empirical mode decomposition. ICASSP 2005 IEEE. 485~488.
[140] G. Rilling, P. Goncalves, “On empirical mode decomposition and its algorithms,”in IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03,2003
[141] S.Hahn, Hilbert Transforms in signal Processing , Artech House, Boston, 1996
[142] 贾民平,许飞云,钟秉林等.远程分布式汽轮机组故障诊断网络系统的研究. 中国电机工程学报,1998,18(2):60~63
[143] 张金玉.基于网络的远程诊断与处理支持中心的研究[博士学位论文].西安:西安交通大学
[144] 李东晓,史铁林,杨叔子.分布式监测诊断系统的设计思想及实现.华中理工大学学报,1997,25(11):31~32
[145] Engelmann U, Schroter A, Baur U etc. A three-generation model for teleradiology. IEEE Trans on Information Technology in Biomedicine, 1998,2(1).20～25
[146] 何志伟,王承.基于虚拟仪器的网络化自动测试系统的构架及实现.电子技术应用.2001,27(11):58~59
[147] 卢奂采.虚拟仪器技术的发展及现状.自动化仪表.2001,22(11):1~3
[148] 严骏,赵立强,凌海凤.基于 DataSocket 的工程机械液压系统远程故障监测与诊断系统研究.机床与液压,2006,19: 48~49
[149] 陈新宇,周锋,王丽华,荀东升.设备远程实时监测系统的研究.电气传动,2005 ,35 (2): 130~132
[150] 孙 欣, 丁香乾, 石 硕.BP 神经网络在柴油机涡轮增压系统故障诊断中的应用.计算机应用研究,2006(5):202~203
[151] 胡 婧,杨曙年.基于 BP 神经网络的滚动轴承故障诊断研究.机械与电子,2006(4):9~10
[152] S.T.Kent and J.Lin. ”Privacy Enhancement for Internet Electronic Mail:Part II: Centificate-Based Key Management” rfc 1114, 1989
[153] M.Ogiwara. “A Method for Generating Cryptographically strong Primes”, The Trans. Of IEEICE, 1990(73):985~994
[154] R.C Merk. “Secrecy, Authentication and Public Key System”, Ann arbor:UMI Research Press,1982.
[155] J.Hastad. “On Using RSA With Lower Exponent in Public Key Network”, Proceeding CRYPTO’s 85 New York: Springer-Verlag,1986.
© 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 |