线调频小波路径追踪算法和FrFT相结合的升降速齿轮故障诊断方法

针对升降速阶段齿轮振动信号的非平稳特性,提出线调频小波路径追踪算法和分数阶傅里叶变换(Fractional Fouriertransform,FrFT)相结合的齿轮故障诊断方法。该方法采用线调频小波路径追踪算法获得升、降速阶段齿轮振动信号所包含的能量最大信号分量的瞬时频率,并通过对该瞬时频率时频曲线的观察,获得该瞬时频率近似于线性上升或下降的时间范围,提取该时间范围的齿轮振动信号段,用FrFT对所提取的振动信号段进行处理,得到齿轮振动信号段的FrFT频谱图,从FrFT频谱图存在的调制现象来判断齿轮故障。其中FrFT的最佳阶次可由瞬时频率的调频系数计算得到。由于噪声与Chirplet原子的相关性很小,使得线调频小波路径追踪方法对噪声不敏感;另一方面,选择合适的分数阶,信号的FrFT将具有很好的信噪分量效果,因此该方法可用于处理升降速阶段的低信噪比齿轮振动信号。仿真分析和应用实例验证了该方法的有效性和良好的抗噪性。     

Signal Separation and Instantaneous Frequency Estimation Based on Multi-scale Chirplet Sparse Signal Decomposition

An approach based an multi-scale chirplet sparse signal de-composition is proposed to separate the multi-component polynomial phase signals, and estimate their instantaneous frequencies. In this paper, we have generated a family of multi-scale chirplet functions which provide good local correlations of chirps over shorter time inter-val. At every decomposition stage, we build the so-called family of chirplets and our idea is to use a structured algorithm which exploits information in the family to chain chirplets together adaptively as to form the polynomial phase signal component whose correlation with the current residue signal is largest. Simultaneously, the polynomial instantaneous frequency is estimated by connecting the linear frequen-cy of the chirplet fixations adopted in the current separation. Simula-tion experiment demonstrated that this method can separate the com-ponents of the multi-component polynomial phase signals effectively even in the low signal-to-noise ratio condition, and estimate its in-stantaneous frequency accurately.     

基于参数自适应时频分布的瞬时频率估计

为了跟踪信号在不同时刻的频率变化情况，需要估计其瞬时频率。本文分析了瞬时频率与非平稳信号的时频分布之间的关系，提出了一种采用自适应信号子空间分解的参数自适应时频分布（PAD），以及基于PAD峰值检测的瞬时频率估计方法。数值仿真和对实测信号的瞬时频率估计实验结果表明，该方法对于调频类信号的估计性能优于其他常用的瞬时频率估计法，且抗噪声干扰能力强，为时变频率非平稳信号的瞬时频率估计提供了新的手段。     

局域均值分解法在轴承故障诊断中的应用

局域均值分解法将复杂的多分量信号分解为若干个乘积函数(PF)的线性组合,每个PF分量由1个包络信号和1个调频信号相乘得到,包络信号就是该PF的瞬时幅值,而PF的瞬时频率可以由纯调频信号求出。进一步将所有PF分量的瞬时频率和瞬时幅值相组合,即可得到原始信号的时频分布。通过对故障轴承信号的分析表明,该方法能清晰地提取轴承故障特征。     

Research on iterated Hilbert transform and its application in mechanical fault diagnosis

Multicomponent AM–FM demodulation is an available method for machinery fault vibration signal analysis, so a new method for mechanical fault diagnosis based on iterated Hilbert transform (IHT) is proposed. The principle of computing the asymptotically exact multicomponent sinusoidal model for an arbitrary signal by iterating Hilbert transform is introduced, and some properties of IHT are analyzed. Theoretical analysis for the generic two-component signal shows that there are limitations in the direct estimation of instantaneous frequencies via the phase signals of the previously obtained model. Therefore, a smoothed instantaneous frequency estimation (SIFE) method based on difference operator and zero-phase digital low-pass filtering is proposed, and then the accuracy and validity of this method have been proved by the simulation results. The analysis results of the mechanical fault signals show that the weak features of these signals can be efficiently extracted with the proposed approach.     

Non-stationary signal analysis based on general parameterized time–frequency transform and its application in the feature extraction of a rotary machine

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time–frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.     

基于角域AR模型滤波的滚动轴承故障诊断

在齿轮噪源存在的变转速滚动轴承故障诊断过程中,因混合信号中转频分量相对较小,使得基于时频表达的阶比跟踪技术受到限制。虽然基于故障特征频率的角域重采样能提取轴承的故障特征,但这种算法不能确定故障位置,而且可能会出现误判。针对这一问题,提出了基于角域自回归(auto regressive,简称AR)模型滤波的处理方法。该方法利用线调频小波路径追踪算法从降采样处理的混合信号中提取齿轮瞬时啮合频率趋势线并估计转速,根据估计转速信息对原混合信号进行等角度重采样,获得了角域信号。利用角域信号中齿轮啮合振动成分具有周期性的特点,使用AR模型对其滤波,并且对滤波后信号进行包络阶比分析,完成故障判断。通过处理仿真信号和实验信号,验证了该方法不仅能有效地去除齿轮噪声,并且可以判断轴承故障位置。     

高速列车转向架区气动噪声分离研究

提出了一种基于快速路径优化的自适应短时傅里叶变换时频分析方法，并将该方法用于行星齿轮箱的故障诊断。该时频分析方法通过使用快速路径优化获得瞬时频率变化规律，在短时傅里叶变换过程中自适应的改变时窗长度，从而获得更恰当的时频分辨率。针对行星齿轮箱运行状态不稳定的特点，通过使用笔者提出的时频分析方法可以有效地提取出行星齿轮箱的转速信息，利用参考转速对故障信号角度域重采样和阶次分析，从而实现变转速情况下的行星齿轮箱故障诊断。仿真分析表明，与传统短时傅里叶变换相比基于快速路径优化的自适应短时傅里叶变换得到的时频分布能量更加集中；试验分析证明了基于快速路径优化的自适应短时傅里叶变换方法在行星齿轮箱故障诊断中的有效性。     

基于小波脊提取的扭转振动测试方法研究

针对工程中转子的扭转振动提取对硬件要求严格的情况,提出了一种基于小波脊提取的扭转振动测试方法。首先,通过小波变换获得齿信号的时频信息;其次,对时频信息进行脊提取得到齿信号的瞬时频率,获得转子的瞬时转速;然后,对瞬时转速进行参数估计获得最终的扭转振动参数;最后,通过仿真与试验分别对所提出的方法进行了验证。结果表明,与传统的扭转振动测试方法相比,所提出的方法不但具有较高的精度,且对硬件的要求相对较低,所需采样频率和储存空间远远小于传统的测齿法。     

基于LMD EMD故障诊断分析及其研究

经验模态分解(EMD)及局域均值分解(LMD)都是转子故障诊断领域时频分析的有效方法。EMD为非平稳信号进行有意义的Hilbert变换起到了桥梁的作用,但是却会因此而产生了不能解释的负频率。而LMD将一个复杂的多分量信号分解为若干个瞬时频率有物理意义的乘积函数(Production Function PF),并且其局部均值函数与包络估计函数都是采用平滑处理的方法形成的避免了EMD方法中的过包络与欠包络现象。实验通过采用LMD与EMD方法对两类常见的转子故障信号的分析比对,得出LMD在高频和频率变化波动大的故障信号中比EMD效果更佳明显。     

瞬时频率估计的齿轮箱升降速信号阶次跟踪

提出了基于瞬时频率估计的齿轮箱升降速信号阶次跟踪的新方法。首先对振动信号进行经验模态分解得到信号的固有模态函数，再求各个固有模态函数的Hilbert变换，得到信号的瞬时频率，从而直接从振动信号得到参考轴的转速信号，然后根据参考轴的转速信号对时域振动信号进行等角度重采样，最后对重采样信号进行阶次分析。通过仿真信号和对齿轮磨损故障实验信号的分析，表明该方法能有效地诊断齿轮的故障。     

基于连续小波变换的瞬时频率提取在光电编码器速度测量中的应用

提出一种新的光电编码器速度测量方法,根据编码器角度测量误差信号中高频分量的渐进特性,通过对角度误差信号进行时频分析,提取编码器的旋转速度。利用功能强大的连续小波变换非平稳信号分析工具,提取角度误差分量的特征。基于连续小波变换,实现了一种称为迭代算法的小波脊提取方法,用于瞬时频率估计。实验结果表明,经过适当的时频分析处理后,角度误差的高频分量可以有效地用于光电编码器的速度测量。该方法能够有效减弱噪声和干扰对测量精度的影响。     

基于广义S变换奇异值分解的料位回波检测与校正

非平稳及多奇异点的调频料位测量雷达回波中包含虚假回波及噪声,影响料位回波信号检测,导致料位测量精度不高.本文提出了一种基于广义S变换和奇异值分解的料位回波检测与校正方法.首先,将料位变化视作低速运动目标,将料位回波信号与雷达发射信号进行混频解调,并根据回波信号的频率分布特点对广义S变换窗口的变化趋势进行调节.之后对其变换所得到的二维时频系数矩阵利用奇异值分解方法重构系数矩阵,并对其进行广义S逆变换,得到校正后的回波信号.实验结果表明:该方法能够准确检测料位回波信号,在抑制噪声的同时能最大限度保留信号的细节特征,减少虚假回波干扰.料位测量误差不超过4.01％,测量精度可达到0.40％F.S.     

Adaptive chirplet transform for sensitive and accurate monitoring of pulsed gas metal arc welding process

Condition monitoring of welding processes have received considerable attention in recent years. The method proposed in this paper provides a novel and a better method for analysis of the weld joint strength, i.e., the adaptive chirplet transform. The presence of the nonlinearities in the various sensor outputs of the monitoring systems of the welding procedure demands a more precise signal processing method for a more accurate analysis of the weld joint strength. The adaptive chirplet method has been used here which produced much better results than the other statistical signal processing methods like the wavelet transform technique due to a better time–frequency resolution of the same. In nonlinear feature extraction, wavelet transform technique was first used to detect the weld joint strength using current as a sensor output during the welding. Then the similar procedure was followed using the adaptive chirplet analysis technique which not only showed better differencing capacity between various signals but also provided better time–frequency resolution for the experimental cases where the wavelet method could not predict the weld joint strength correctly. A thorough laboratory study shows that the diagnostic method proposed in this paper is much more accurate, has high sensitivity with respect to faults, and also has better diagnostic resolution.     

基于改进型峰值搜索法的变速箱振动阶比分析

基于传统内燃机汽车发动机引起振动噪声阶次特征明显的特点，运用短时傅里叶变换（short-time Fourier transform，简称STFT）进行转速估计，结合阶次追踪法，对汽车加速工况变速器振动信号进行阶次分析。首先，利用STFT对加速工况变速箱振动信号进行时频分析；其次，利用改进型峰值搜索法提取特征阶次所对应的瞬时频率值，进一步计算得到发动机转速信号表达式；然后，根据发动机转速信号表达式对振动信号在角域重采样，进行阶次分析；最后，利用本研究方法对变速箱加速过程振动信号进行阶次分析，并与商用软件LMS.Test.lab分析结果进行对比。结果表明，本研究方法无需布置转速传感器即可对变速箱振动信号进行阶次分析，为整车振动噪声试验分析提供参考。     

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。     

基于时频脊线的瞬时频率特征提取

根据时频脊线所在的位置信息与信号瞬时频率的对应关系,提出根据时频脊线提取多成分瞬时频率的新方法。该方法是,先通过加窗处理对一维时间信号进行时域分段,对每个时段信号采用自适应核函数时频分析,再把每个时段上求得的时频分布拼接成信号整体时频谱;然后将时频分布图转换成直观的二维图像,进行平滑处理后采用二维Laplacian算子提取时频图像的脊线并作细化处理;采用Hough变换自动检测时频脊线的特征参数,特别对正弦调频信号和线性调频信号的时频脊线检测问题进行论述。仿真试验和齿轮故障诊断的工程实践证明了该方法的可行性和有效性。     

基于多项式Chirplet变换和变分模态分解的变转速工况轴向柱塞泵故障诊断

针对变转速工况轴向柱塞泵故障诊断时故障特征提取困难的问题,提出了基于多项式Chirplet变换和变分模态分解的诊断方法。首先使用多项式Chirplet变换估计瞬时频率;然后基于估计的瞬时频率重采样,将时域非平稳信号转化为角域平稳信号;最后对角域信号进行变分模态分解。根据峭度对所得的本征模态函数分量进行重构并作包络阶次谱分析,判断轴向柱塞泵中轴承的故障类型。实验结果表明,该方法有效提取了变转速工况轴向柱塞泵轴承的故障特征。     

基于小波变换mallat算法的雷达回波去噪

为了准确识别雷达回波信号中的目标信号,将小波变换引入雷达回波信号处理,对回波信号进行分解、重构,并通过滤波器设计,去除了雷达信号中的噪声。采用Matlab软件进行系统仿真,结果表明,采用小波变换mallat算法对探地雷达回波信号进行目标识别具有较好的时频分辨率,且可抑制杂波,去除噪声,能很好地识别目标信号,具有很好的应用前景。     

IMPROVED METHOD FOR HILBERT INSTANTANEOUS FREQUENCY ESTIMATION

In the mechanical fault detection and diagnosis field, it is more and more important to analyze the instantaneous frequency (IF) character of complex vibration signal. The improved IF estimation method is put forward aiming at the shortage of traditional Hilbert transform. It is based on Hilbert transform in wavelet domain. With the help of relationship between the real part and the imaginary part obtained from the complex coefficient of continuous wavelet transform or the analytical signal reconstructed in wavelet packet decomposition, the instantaneous phase function of the subcomponent is extracted. In order to improve the precise of IF estimated out, some means such as Linear regression, adaptive filtering, resampling are applied into the instantaneous phase obtained, then, the central differencing operator is used to get desired IF. Simulation results with synthetic and gearbox fault signals are included to illustrate the proposed method.