基于局部投影算法的齿轮故障声信号特征提取

局部投影算法采用延时坐标将时间序列进行相重构，在高维的相空间上采用局部投影的方法将相空间分解成正交的子空间，通过子空间中吸引子特性的不同来分离时序中的背景信号和弱特征信号分量。提出将局部投影算法用于设备故障声信号的降噪，通过齿轮故障信号的特征提取实验证实该方法用于识别设备故障的有效性。     

基于自相关降噪和ELMD的轴承故障诊断方法

为了提取在故障轴承振动信号中被强噪声淹没的微弱冲击特征信号,提出一种基于总体局部均值分解和自相关降噪的轴承故障诊断方法。首先,应用自相关函数对轴承故障信号进行降噪;其次,对降噪后的信号进行ELMD分解,并得到一系列的乘积分量;最后,利用共振解调技术对各个PF分量进行包络分析,进而发现轴承故障频率。试验结果表明:将自相关降噪和ELMD分解方法结合用于实测轴承故障特征提取中,不仅可以降低信噪比,而且可以有效地提取轴承故障的特征频率。     

基于小波降噪的共振解调技术在齿轮箱故障诊断中的应用

提出一种运用基于小波降噪的共振解调技术对齿轮齿面接触故障进行诊断的方法。该方法利用小波降噪算法对传感器采集的混合信号进行降噪;采用共振解调技术对降噪后的故障源信号进行频谱分析,成功地提取了齿轮齿面直接接触的故障特征。     

基于三维谱峭图算法的共振解调技术研究及应用

针对滚动轴承出现故障时振动信号表现出的周期冲击性特征,以寻求表征故障信号特征的最优频带为目的,提出了一种利用二分法思想从带宽和中心频率两个角度进行优化搜索的三维谱峭图算法。将该方法应用于共振解调技术带通滤波器参数的确定,形成基于三维谱峭图算法的共振解调技术。为了验证该技术的有效性,在铁路货车轮对跑合实验台上进行了轮对故障轴承的振动测试。采用基于三维谱峭图算法的共振解调技术进行故障诊断分析,并与基于快速谱峭图的共振解调技术进行了对比。结果表明,基于三维谱峭图算法的共振解调技术能够更好地诊断轴承故障。最后,通过对时间复杂度的求解,证明了三维谱峭图算法具有较高的执行效率,在工程应用方面具有一定的参考价值。     

加权相空间重构降噪算法及其在设备故障诊断中的应用

加权相空间重构降噪算法在相空间重构与分解的基础上,将一维的时间序列延拓到高维的相空间,使得一维时序中不易识别的特征在高维相空间变为容易识别的吸引子,通过区分吸引子在高维空间的不同的属性与特征,采用汉宁加权窗将高维信号投影到一维,使得信号的本质特征得到充分体现.根据机械设备发生故I荤其振动信号中往往具有非线性、非平稳性的特点,提出将加权相空间重构降噪算法用于设备故障信号的降噪,并采用数值仿真试验及齿轮故障诊断对此算法进行了分析与验证,结果表明该算法对此类信号具有良好的降噪效果.     

基于奇异值分解和局域均值分解的滚动轴承故障特征提取方法

针对随机噪声干扰滚动轴承故障特征信号提取这一问题,提出一种基于奇异值分解(Singular value decomposition,SVD)滤波降噪与局域均值分解(Local mean decomposition,LMD)相结合的故障特征提取方法。该方法首先对原始振动信号在相空间重构Hankel矩阵并利用SVD方法进行降噪处理,再对降噪后的信号进行LMD分解,将多分量的调制信号分解成一系列生产函数(Product function,PF)之和,最后结合共振解调技术对PF分量进行包络谱分析提取故障特征频率。通过数值仿真和实际轴承故障数据的分析对比,表明该方法提高了LMD的分解能力,可有效辨别出滚动轴承实测信号的典型故障,提高滚动轴承故障的诊断效果。     

基于特征空间降噪的局部保持投影算法及其在轴承故障分类中的应用

实际机械振动信号不可避免受到各种各样的噪声干扰,导致机械状态诊断结果误判等问题。目前的降噪算法主要都是针对时域振动信号,所需计算时间较长、占用存储空间较大。流形学习算法处理对象是样本特征空间数据,提出一种直接对样本特征空间进行奇异值分解降噪方法,再对降噪后的特征空间利用局部保留投影算法进行维数约简,通过1NN算法对设备运行状态进行识别。轴承故障仿真试验表明,与直接对时域信号进行降噪相比,所提方法能有效保证局部保持投影算法的降维效果,同时加快计算速度及减少所占存储空间。     

高速线材札机同步齿轮箱故障监测与诊断研究

提出了共振解调技术的数学模型,求解了该模型的脉冲响应,为该技术在正常低频振动中提取所夹带的微小冲击信号的应用提供了理论依据.同时介绍了采用共振解调技术在高速线材轧机同步齿轮箱抽承和齿轮故障诊中的应用效果,说明共振解调技术在提取齿轮、轴承机械故障的微小冲击的应用上是非常有效的.     

局部投影算法及其在非线性时间序列分析中的应用

引入了非线性时间序列的局部投影消噪算法。该算法将时间序列先进行相重构,在高维的相空间上采用局部投影的方法将相空间分解成正交的子空间,来分离时序中不同的分量。通过Lorenz模型的数值仿真分析,证实了该算法在消除非线性时间序列中随机噪声的效果。此外还讨论了局部投影算法在提取微弱特征信号中的应用。     

滚动轴承早期冲击性故障特征提取的综合算法研究

针对滚动轴承早期微弱冲击性故障信号特征难以提取的问题,提出了共振解调结合小波包系数熵阈值降噪的综合算法,用于准确确定并提取早期微弱冲击性故障引起的共振调制边频带。该算法应用时延相关和小波包系数熵阈值算法实现信号的双重降噪,并依据共振带能量比确定小波包分解的最佳分解尺度和选取熵阈值的最佳阈值,寻求共振带的最优解,然后进行共振解调提取故障信号特征。实验数据分析结果表明了该算法对滚动轴承早期冲击性故障提取的可行性和有效性。     

基于递归定量分析的液压泵故障识别方法

提出了一种局部投影消噪和递归定量分析相结合的轴向柱塞泵故障识别方法。以轴向柱塞泵故障振动信号为研究对象，首先用局部投影消噪方法对振动信号进行消噪；其次对消噪后的振动信号绘制递归图，进而通过递归定量分析对递归图所反映出的系统动力学信息进行特征提取，选择确定率（DET）和递归熵(ENTR)2个特征构成特征向量，构成故障特征样本；然后通过核模糊C均值聚类(KFCMC)方法对训练样本进行聚类,进而依据最小欧氏距离准则对测试样本进行故障识别；最后，将递归定量分析方法和相空间复杂网络定量特征方法进行对比。结果表明，基于递归定量分析的轴向柱塞泵故障识别方法具有更高的故障确诊率。     

基于奇异谱的降噪方法及其在故障诊断技术中的应用

提出一种将振动信号在相空间进行重构,并利用重构吸引子轨道矩阵的奇异谱的特性来提高信噪比的方法。该方法已应用于滚动轴承和齿轮箱的故障诊断中,试验表明该方法能够有效地降低噪声,提高信噪比,突出振动信号的故障特征,从而提高设备故障诊断的准确率。     

基于主流形识别的非线性时间序列降噪方法及其在故障诊断中的应用

提出了一种新的基于主流形识别的非线性时间序列降噪方法。新的降噪方法将一维时间序列重构到高维相空间,利用非线性降维方法找出动力学系统在相空间中具有全域正交坐标系的低维主流形,然后根据主流形反求一维时间序列,进而达到降噪的目的。对洛伦兹信号进行的数值试验证明,与奇异谱分解等现有非线性分析方法相比,基于主流形识别的降噪方法能更加有效地消除混沌时间序列中的高斯白噪声。将该方法应用于带有断齿故障的齿轮箱振动信号的故障分析中,成功地提取出了淹没在带噪信号中的冲击特征。     

Weak fault feature extraction of rolling bearing under strong poisson noise and variable speed conditions

Fault feature extraction of the rolling bearing under strong background noise is always a difficult problem in bearing fault diagnosis. At present, most of the research focuses on weak signal extraction under Gaussian white noise and has certain practical significance. However, the noise in engineering is often complex and changeable, Gaussian white noise cannot fully simulate the actual strong background noise. Poisson white noise is a type of typical non-Gaussian noise, which widely exists in complex mechanical impact. It is of great significance to study the weak fault feature extraction of a faulty bearing under this type of noise. At the same time, variable speed conditions occupy most rotating machinery speed conditions. Non-stationary vibration signals make it difficult to extract fault features, and the frequency spectrum ambiguity will occur because of speed fluctuation. To solve the above problems, a method of weak feature extraction of a faulty bearing based on computed order analysis (COA) and adaptive stochastic resonance (SR) is proposed. Firstly, by numerical simulation, the non-stationary fault characteristic signal corrupted with strong Poisson noise is transformed into a stationary signal in the angle domain by COA. Secondly, the influence of the parameters of the pulse arrival rate and noise intensity of Poisson white noise on the optimal SR response in the angle domain are studied, and the influence of the parameters of Poisson white noise on the fault feature extraction is given. Then, adaptive SR method is used to extract and enhance fault feature information. Finally, the effectiveness of this method in weak fault characteristic signal extraction under strong Poisson noise is verified by experiments. Numerical simulation and experimental results verify the effectiveness of the proposed method in bearing fault diagnosis under strong Poisson noise and variable speed conditions.

     

基于多稳态随机共振的轴承微弱故障信号检测

针对双稳态随机共振模型无法有效处理调制信号的缺点,提出了一种以包络信号为输入信号的自适应多稳态级联随机共振(adaptive multi-stable cascaded stochastic resonance,简称AMCSR)信号强化方法。首先,对振动信号进行包络解调,依据包络信号分布特点,选用与信号分布相匹配的多稳态随机共振模型;然后,以故障特征频率的频谱幅值为指标,采用蚁群算法自适应地优化随机共振模型参数;最后,以噪声为强化源和驱动信号,通过级联随机共振方法对包络信号中的故障特征频率进行逐级强化,获得故障特征成分的强化信号。对实测轴承振动信号的验证结果表明,该方法能够增强故障特征频率成分,有效地提取被其他频率成分淹没的微弱故障信号。     

自适应中值滤波器及其应用

将自适应加权中值滤波器应用于滚动轴承故障信号的故障诊断中。当信号中含有多种噪声时，让信号先通过自适应加权中值滤波器，再通过线性带通滤波器，对降噪后的信号进行包络解调处理，可以克服噪声对包络谱分析的影响。通过仿真和试验信号分析可以看出，自适应中值滤波器在机械故障诊断中具有较好的应用前景。使包络谱分析方法得到更广泛的应用。     

Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.     

Feature extraction under bounded noise background and its application in low speed bearing fault diagnosis

We investigated the low speed bearing fault diagnosis under bounded noise background in three typical conditions. These situations included when the frequency of bounded noise was much larger, a little larger, and a little smaller than the driving frequency. Through the investigation of a harmonic signal in these three situations, we found the effect of stochastic resonance method was the best in the first situation, while it was invalid in the third situation. For the third situation, we introduced the vibrational resonance method by adding an auxiliary signal to extract the character frequency successfully. Then we also studied the low speed experimental bearing fault signal under bounded noise background, and it showed the same results as those of the simulation harmonic signal case. The methods and results of this paper might be useful in practical engineering, especially in extracting the weak character signal in the bounded noise background.