基于DEMD和对称差分能量算子解调的滚动轴承故障诊断

针对机械故障振动信号多为调制信号的特点,为了更好地提取多分量调幅调频信号的幅值和频率信息,提出了基于微分的经验模式分解(DEMD)与对称差分能量算子相结合的解调方法。利用DEMD算法将原始振动信号进行分解,得到若干个单分量信号;对每一个单分量信号进行三点对称差分能量算子解调,得到各单分量信号的瞬时幅值和瞬时频率,并计算出包络谱。将该方法应用于仿真信号和滚动轴承故障信号的诊断,实验结果表明,该方法能有效地提取机械故障信号的故障特征,实现旋转机械故障诊断。     

局部均值分解方法在调制信号处理中的应用

为了提取多分量调制信号的调制信息,研究了一种信号分析方法——局部均值分解(local mean decomposition,简称LMD)方法。LMD方法首先将一个多分量的调制信号自适应地分解成若干个具有一定物理意义的PF(product function)分量,其中每个PF分量为一个包络信号和一个纯调频信号的乘积,然后求出每个PF分量的瞬时幅值与瞬时频率,从而获得原信号完整的调制信息。本文用LMD方法对仿真信号以及齿轮故障振动信号进行了分析,结果表明该方法能有效地提取出信号的调制信息。     

基于局部均值分解和奇异值差分谱的滚动轴承故障诊断研究

为了从复杂的轴承振动信号中提取微弱的故障信息,提出了一种基于局部均值分解(local mean decomposition,LMD)和奇异值差分谱的轴承故障诊断方法。首先通过LMD将非平稳的原始轴承故障信号分解为若干个PF(product function)分量,由于背景噪声的影响,难以从PF分量准确得到故障频率,对PF分量进行Hankel矩阵重构和奇异值分解,相应的得到奇异值差分谱,根据奇异值差分谱理论对某个PF分量进行消噪和重构,然后再求重构后PF分量的包络谱,便能准确地得到故障频率。仿真分析和滚动轴承内圈故障实例很好地验证了提出的改进方法的有效性。     

基于LMD的能量算子解调机械故障诊断方法

为了提取多分量调幅调频信号的幅值和频率信息,提出了基于局部均值分解(local mean decomposition,简称LMD)的能量算子解调机械故障诊断方法.该方法先利用LMD将机械调制信号分解成若干个乘积函数(production function,简称PF)分量,然后对每一个PF分量进行能量算子解调,获得信号的幅值和频率信息进行故障诊断.利用该方法对仿真信号以及轴承和齿轮故障振动信号进行实验研究的结果表明,基于LMD的能量算子解调方法能够有效地提取机械故障振动信号特征.     

全矢积频谱在滚动轴承故障诊断中的应用

局部均值分解(LMD)可将采集的时域信号分解为多个单分量信号(PF),全矢谱(FVS)技术可将双通道信息相互融合,防止单通道信息不完整。在此基础上,借鉴边际谱的思想,提出了一种新的解决方式—积频谱(FAS):采集滚动轴承的同源双通道振动信号,用LMD对同源双通道的振动信号进行处理,得到双通道各个分量的瞬时幅值和调频信号,并对调频信号进行计算得到各个分量的瞬时频率,由此可求出各通道LMD的时频分布;对时频分布进行频率上的积分后,再通过傅立叶变换求出各通道的积频谱;并通过信息融合,将得到的全矢积频谱和单通道积频谱进行对比。选择有外圈故障的滚动轴承进行试验,试验结果表明,该方法是有效的。     

基于局域均值分解的滚动轴承故障诊断方法

论述了一种新的自适应时频分析方法--局域均值分解的基本原理和算法,并将该方法引入到滚动轴承故障诊断中,提出了一种基于局域均值分解的滚动轴承故障诊断方法, 该方法先将一个故障信号自适应地分解成若干个具有一定物理意义的生产函数分量,然后求出每个PF分量的瞬时幅值和瞬时频率,从而获得故障信号的特征信息.试验结果验证了该方法的有效性.     

基于微分局部均值分解的旋转机械故障诊断方法

提出一种基于微分局部均值分解(Differential local mean decomposition,DLMD)的旋转机械故障诊断方法。该方法在局部均值分解(Local mean decomposition,LMD)过程中融入微分和积分运算。对原始信号进行k阶微分,然后对微分后信号进行LMD分解,对分解得到的各乘积函数(Production function,PF)分量循环进行一次积分和一阶LMD分解,直至循环k次,得到m个PF分量和残余分量,将所有PF分量的瞬时幅值和瞬时频率组合,便可以得到原始信号完整时频分布。将该方法应用于旋转机械故障诊断研究中,通过仿真和试验进行分析研究,结果表明,基于微分局部均值分解的旋转机械故障诊断方法能够有效地抑制虚假干扰频率,提高旋转机械故障诊断准确性。     

基于LMD包络谱熵及SVM的天然气管道微小泄漏孔径识别

针对管道泄漏信号的非平稳特征以及管道泄漏孔径大小难以识别的问题,提出一种基于局域均值分解包络谱熵及支持向量机的识别方法。该方法对管道泄漏信号进行局域均值分解,得到若干个瞬时频率具有物理意义的乘积函数(production Function, PF)分量;计算各PF分量的峭度值并据此选出包含主要泄漏信息的分量作为主PF分量,对这些分量进一步采用小波包分解能量法进行分析并重构;再对重构后的主PF分量进行希尔伯特变换求取包络谱,结合信息熵的概念提出包络谱熵并计算熵值;将归一化包络谱熵作为泄漏信号特征输入支持向量机分类器中,用以区分不同的泄漏孔径,完成对泄漏孔径的识别。通过试验采集大量的管道泄漏信号进行处理及分析,试验结果表明该方法能有效识别不同泄漏孔径类别。     

基于局部均值分解的阶次跟踪分析及其在齿轮故障诊断中的应用

针对齿轮升降速过程中故障振动信号为多分量的调制信号以及故障特征频率随转速变化的特点,将局部均值分解（LMD）与阶次跟踪分析相结合,提出了一种新的齿轮故障诊断方法。首先采用阶次重采样将齿轮的时域振动信号转换为角域平稳信号,然后对角域信号进行LMD分解,得到若干个乘积函数（PF）分量,最后对各个PF分量的瞬时幅值进行频谱分析来提取齿轮的故障特征。通过对齿轮齿根裂纹故障试验振动信号的分析可知,该方法能有效地提取齿轮故障特征。     

基于线调频小波路径追踪阶比能量解调的齿轮轮齿裂纹故障诊断

针对变速下齿轮裂纹故障信号微弱,难以提取这一特点,提出了基于线调频小波路径追踪的阶比能量解调算法,并将其应用于变速下的齿轮裂纹故障诊断。该方法先采用线调频小波路径追踪算法提取齿轮的啮合频率分量,由此得到转速信号;然后利用转速信号对原始信号进行等角度采样得到角域平稳信号;接着对角域平稳信号进行带通滤波和角域平均运算以消除干扰噪声的影响;最后使用能量算子解调求取瞬时频率和瞬时幅值,根据瞬时频率和瞬时幅值进行故障诊断。应用实例表明,该方法能有效地提取变速下的齿轮裂纹故障。     

用最大重叠离散小波包变换的Hilbert谱时频分析

在介绍基于最大重叠离散小波包变换(Maximal Overlap Discrete Wavelet Packet Transform,简称MODW-PT)的Hilbert谱方法的基础上,将基于MODWPT的Hilbert谱应用于非平稳信号的分析.采用MODWPT可将多分量的复杂信号分解为若干个瞬时频率和瞬时幅值都具有经典物理意义的分量之和,求出各个单分量信号的瞬时频率和瞬时幅值,再进行组合得到原始复杂信号完整的时频分布.对基于MODWPT和基于经验模态分解(Empiri-cal Mode Decomposition,简称EMD)的Hilbert谱,在不同类型非平稳信号下的时频分析效果进行了比较和分析,结果表明了基于MODWPT的Hilbert谱分析方法的有效性.     

Instantaneous Frequency Estimation for Motion Echo Signal of Projectile in Bore Based on Polynomial Chirplet Transform

Microwave interferometer is one of the devices for measuring the movement travel–varying or time-varying velocity of projectile in bore. Microwave interferometer first obtains the Doppler echo signal including the motion information of the projectile in bore, then the velocity is measured based on instantaneous frequency estimation (IFE) of the processed and transformed signal. The parametric time-frequency analysis method can make spectral energy of nonlinear frequency modulation (FM) signal concentrate at some range in the new transform domain. As the motion echo signal of projectile in bore (MSPB) is a nonlinear FM signal, it could be described by polynomial chirplet, one of polynomial FM signal modes, which is used to construct transform kernel for the signal. In this paper, Polynomial chirplet transform (PCT) method is proposed to analyze the simulation and experiment echo signals of projectile in bore. The estimation error and Renyi entropy are used to measure quantify of the time-frequency distribution. Compared with short-time Fourier transform (STFT) method and Wigner-Ville distribution (WVD) method, our results show that the PCT method has most powerful anti-interference performance and highest accuracy of instantaneous frequency estimation for the simulation signal, and lowest Renyi entropy of the instantaneous frequency estimation for the experiment signal. In general, the PCT method has powerful anti-interference performance and high time-frequency concentration and accuracy of instantaneous frequency estimation for the motion echo signal of projectile in bore.     

基于相位特征的多波束测深声呐多回波检测方法研究

现代多波束测深声呐在检测海底地形的同时往往也有检测水中目标的需求,常见的多回波检测方法基于回波幅度设置检测门限,其对波束内幅度相当的回波检测是有效的,但是当多目标反向散射能力强弱导致的回波幅度悬殊时,基于幅度门限方法难以奏效。针对此问题,提出了一种基于瞬时频率方差及谱特征联合加权的方法,在利用回波幅度的基础上进一步利用了相位信息。首先利用回波信号相位特性求得回波信号的瞬时频率方差,其次对信号的谱特征进行分析求取回波信号的等效带宽,最后利用所得的等效带宽数值与瞬时频率方差数值极低甚至近似为0的特性,联合对回波幅度进行加权,凸显被强目标信号淹没的弱目标回波信号,便于强弱目标的同时检测,提高对弱目标的检测能力。计算机仿真结果显示经过联合加权后,强弱目标的相对幅度提升了近30%,并且检测能力得以有效提高。通过外场试验数据处理结果可以发现,经过瞬时频率方差及谱特征加权处理的目标回波检测能力得到明显的改善。     

一种Hilbert变换法在非线性系统分析中的应用

Hilbert变换是信号处理领域常用工具之一,将Hilbert变换法改进并应用到信号分解和非线性系统振动分析中。振动信号的多谐波性,使得Hilbert变换法提取信号的瞬时频率和瞬时相角可以通过滤波的方法分离成快变和慢变两部分,从而提取系统的振动分量;通过迭代计算,依次获得振动信号中所有谐波分量;将非线性振动方程用瞬态幅值相关的瞬态参数表示,从而求解系统的频响关系曲线方程。通过相应的数值模拟计算,验证了改进的Hilbert变换法在非线性振动分析的有 效性。     

基于非对称滤波的解调频法的原理研究

从调频信号边频分量的幅值相位特征出发，分析了平方解调法不能对调频信号进行解调的原因。分析过程中发现由于调频信号栽波频率左右两侧的边频分量的相位具有一种特殊的对称性，和调幅信号完全不同，使得常用的解调方法不再适用于调频信号。针对上述问题，从打破调频信号边频分量的特殊对称性出发，利用非对称滤波法（滤波器的中心频率远离载波频率）对调频信号进行滤波，用常规解调法就可以进行解调。通过对调频信号边频相位的非对称特性的分析和研究，为寻找更好的解调频方法提供了新的思路。     

The envelope order spectrum based on generalized demodulation time–frequency analysis and its application to gear fault diagnosis

The generalized demodulation time–frequency analysis is a novel signal processing method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM–FM) signals as it can decompose a multi-component signal into a set of single-component signals whose instantaneous frequencies own physical meaning. While fault occurs in gear, the vibration signals measured from gearbox would exactly display AM–FM characteristics. Therefore, targeting the modulation feature of gear vibration signal in run-ups and run-downs, a fault diagnosis method in which generalized demodulation time–frequency analysis and envelope order spectrum technique are combined is put forward and applied to the transient analysis of gear vibration signal. Firstly the multi-component vibration signal of gear is decomposed into some mono-component signals using the generalized demodulation time–frequency analysis approach; secondly the envelope analysis is performed to each single-component signal; thirdly each envelope signal is re-sampled in angle domain; finally the spectrum analysis is applied to each re-sampled signal and the corresponding envelope order spectrum can be obtained. Furthermore, the gear working condition can be identified according to the envelope order spectrum. The analysis results from the simulation and experimental signals show that the proposed algorithm was effective in gear fault diagnosis.     

A vibration analysis method based on hybrid techniques and its application to rotating machinery

Vibration-based condition monitoring and fault diagnosis technique is a most effective approach to maintain the safe and reliable operation of rotating machinery. Unfortunately, the vibration signal always exhibits non-linear and non-stationary characteristics, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, a vibration analysis method based on hybrid techniques is proposed in this paper. Firstly, the raw signals are decomposed into a few product functions (PFs) using local mean decomposition (LMD), and meanwhile instantaneous frequency and instantaneous amplitude also are obtained. Subsequently, Fourier transform is performed on the derived PFs, and then, according to the spectra features, the useful PFs are selected to reconstruct the purified vibration signals. Lastly, several different fault features are fused to illustrate the operating state of the machinery. The experimental results show that the proposed method can accurately extract machine fault features, which proves that the combined application of LMD and other signal processing techniques is a successful scheme for the machine vibration analysis.     

Instantaneous frequency of an arbitrary signal

This paper defines the non-negative pointwise instantaneous frequency (pIF) and pointwise instantaneous amplitude (pIA) of an arbitrary time signal to be the circular frequency and radius of curvature of the signal’s instantaneous trajectory on the complex plane consisting of the signal and its conjugate part from the Hilbert transform. One analytical and three computational methods are derived to prove and validate this concept. The analytical method is derived based on the definition of pIF and circle fitting. A five-point frequency tracking method is developed to eliminate the incapability of the original four-point Teager–Kaiser algorithm (TKA) for obtaining pIF of signals with moving averages. A three-point conjugate-pair decomposition (CPD) method is derived based on circle fitting using a pair of conjugate harmonic functions for frequency tracking. Moreover, the Hilbert–Huang transform (HHT) uses the empirical mode decomposition (EMD) to sift a signal’s instantaneous dynamic component from its sectional moving average (sMA) as the first intrinsic mode function, and then Hilbert transform is used to compute the first IMF’s frequency and amplitude as the sectional instantaneous frequency (sIF) and sectional instantaneous amplitude (sIA). Because finite difference is used in the five-point TKA, its accuracy is easily destroyed by noise. On the other hand, because CPD uses a constant and a pair of windowed regular harmonics to fit data points and estimate pIF and pIA, noise filtering is an implicit capability of CPD and its accuracy increases with the number of processed data points. Numerical simulations confirm that pIF and pIA are non-negative and physically meaningful and can be used for frequency tracking and accurate characterization of complex signals. However, sIF and sIA from HHT are more useful for system identification because the IMFs sifted by EMD often correspond to actual vibration modes.     

变分模态分解在转子故障诊断中的应用

针对转子故障诊断问题,提出一种基于变分模态分解(variational mode decomposition,简称VMD)的信号处理方法。该方法在获取分解分量的过程中通过迭代搜寻变分模型最优解来确定每个分量的频率中心及带宽,从而能够自适应地实现信号的频域剖分及各分量的有效分离,对各单分量信号进行希尔伯特变换,即可得到瞬时的频率和幅值信息。对仿真信号和典型转子故障信号进行VMD方法和经验模态分解(empirical mode decomposition,简称EMD)方法的分析比较,以验证所提方法的有效性。仿真信号的分解结果表明,变分模态能够准确分离出信号中的固有模态分量且不存在模态混叠;转子故障实验信号的分析结果表明,所提方法能够有效提取出明显的故障特征,从而准确诊断出转子存在的故障。