基于贝叶斯理论的机械故障源盲分离

由于噪声的干扰,噪声环境下机械源信号盲分离方法往往分离效果很差.针对这一问题,提出了一种基于变分贝叶斯理独立分量分析的盲分离方法.与传统的分离方法相比,具有以下独特特点:即不需要将未知噪声看成一种独立源,也不需要进行消噪预处理,可直接对噪声干扰的机械源信号进行有效分离.实验结果表明,提出的方法优于传统的机械源分离方法,分离误差大幅度降低.     

基于CVA-ICA的机械故障源动态盲分离方法

动态盲源分离问题是多故障源盲分离的一个热点。传统的机械故障源分离方法要求满足统计特征保持稳定,且混合系统保持不变等假设,而忽略了时序信息。针对此不足,结合规范变量分析(Canonical variate analysis,CVA)和独立分量分析(Independent component analysis,ICA),提出一种基于CVA-ICA的机械多故障源动态盲分离方法。该方法的基本思想是将源信号看成状态空间的状态变量,观测信号看成状态空间的输出变量,从而将动态混合盲源分离问题转化为状态空间盲源分离问题,利用规范变量分析作为降维工具来构造状态空间,再利用传统的ICA算法对规范的观测信号进行盲源分离。仿真研究表明,在处理动态混合的盲分离中,提出的方法明显优于静态ICA方法,取得了满意的分离效果。将该方法应用到滚动轴承内圈和滚动体的故障盲分离中,试验结果进一步验证了该方法的有效性。     

故障源信号的频域盲分离及其应用研究

盲源分离可以对相互统计独立的信号源经线性组合而产生的一组混合信号进行分离,从中得到各个独立的源信息.针对混合信号相位差对独立分量分析算法性能的影响问题,提出一种故障源信号的频域盲分离方法.该方法利用频谱的线性叠加性和无相位性,先将工程实测信号转换到频域,再对得到的信号频谱进行盲源分离.仿真实例和涡流传感器失效检测的成功应用表明,根据信号结构选择预处理方法十分重要,正确的预处理可以大大提高独立分量分析提取故障源特征的有效性.     

基于量子遗传的机械故障非线性盲源分离方法研究

针对传统的机械故障非线性盲分离方法的不足,即将非线性盲源分离中分离矩阵和非线性去混合函数的参数分开来优化,这样容易顾此失彼,学习效率低。将量子遗传引入到机械故障非线性盲分离中,提出一种基于量子遗传的机械故障非线性盲源分离方法(简称QGA-NBSS方法),该方法能同时对分离矩阵和非线性去混合函数的参数进行优化,获得全局最优解并加快了算法的全局收敛性,克服了传统的机械故障源的非线性盲源分离方法的不足。仿真和实验结果验证了提出的方法的有效性。     

基于局域均值分解的机械故障欠定盲源分离方法研究

结合局域均值分解(Local mean decomposition,LMD)和盲源分离各自的特点,提出一种基于局域均值分解的欠定盲源分离方法.该方法利用LMD对观测信号进行分解,得到一系列的生产函数分量,将所得到的生产函数(Production functions,PF)分量和原观测信号组成新的观测信号.对构成的新观测信号进行白化处理和联合近似对角化,得到源信号的估计.该方法能有效解决传统的盲源分离方法要求源信号满足非高斯、平稳和相互独立的假设,且要求观测信号数多于源数的不足等问题.仿真结果表明,所提出的方法是有效的,在处理非平稳信号混合的欠定盲分离方面,比传统时频域的盲源分离方法得到了更好的分离效果.将提出的方法应用到滚动轴承的混合故障分离中,试验结果进一步验证该方法的有效性.     

基于EEMD的单通道盲源分离在轴承故障诊断中的应用

针对一维观测矩阵的极度欠定盲分离模型,结合盲源分离和总体经验模式分解的优点,利用总体经验模式分解将单通道信号转化为固有模态矩阵,重组观测矩阵,再通过近似联合对角化实现信号的盲分离。数据仿真说明该方法能提取低信噪比下的轴承故障信息。实验中,对2种不同故障的轴承进行故障诊断,从而进一步证明了该方法的有效性。     

基于时间序列变分贝叶斯理论的信号盲源分离

研究信号盲源分离中源信号和混合矩阵估计问题.独立分量分析盲源分离的不足之处在于不能估计混合矩阵和源信号的能量及顺序;变分独立因子分析盲源分离的不足之处在于依赖参数初值.将一般变分贝叶斯理论用于时间序列,推导出时间序列的变分贝叶斯期望极大算法.将此算法用于信号盲源分离,同时将传感器噪声逆方差的分布取为Wishart分布,得到了理论上更合理的后验分布参数更新规则.仿真数据和实际语音信号盲源分离结果表明这种方法可以比较准确地估计混合矩阵和源信号,在一定程度上弥补了独立分量分析和变分独立因子分析盲源分离的不足.     

基于源数估计的机械源信号盲分离方法研究

传统的机械源分离方法往往事先假设源信号的个数已知,否则无法进行机械源信号分离。然而,在实际中源信号的个数往往是是未知的。针对此不足,结合变分贝叶斯独立变量分析和自相关测定,提出一种机械源数的最佳估计方法,它是通过比较不同模型的信度来确定出信源的个数。仿真和实验结果表明该方法是有效的,并具有很好的鲁棒性。     

基于分数Fourier变换的机械故障源盲分离方法的研究

结合分数Fourier变换和盲源分离,提出了一种基于分数Fourier变换的机械故障源盲分离方法,试验结果表明,该方法能有效地分离具有不同的分数Fourier变换的机械源信号。     

基于源数估计的旋转机械源盲分离

在传统的源数估计方法基础上引入聚类分析思想，提出了一种基于奇异值分解的聚类不相关源数估计新方法。应用该方法，可以估计系统中独立源数的上界，并籍此获得足够维数的传感观测信号，保证盲源分离方法在实际应用中的正确实施。仿真以实验的结果证明了所提出的整体解决方案的可行性和有效性。     

盲源分离技术用于机械故障诊断的研究初探

目前对现场机械设备进行故障诊断主要是利用振动信号 ,虽然振动故障诊断技术已经十分完善 ,却有着其本身的缺陷。而声音信号恰能弥补这一点。作者试图利用声信号进行机械故障诊断 ,称之为声频故障诊断技术。并首次利用信号处理中的盲源分离技术进行多信号源的分离 ,从而达到单独对现场中某台设备进行诊断的目的。     

Constrained independent component analysis and its application to machine fault diagnosis

For machine fault diagnosis the signals from working machine are always numerous, even uncountable, but there contains only a little useful information. Hence how to find out the useful signal from numerous signals, including noises, that is, how to only extract the desired fault signal is very attractive. This paper shows that the constrained independent component analysis (cICA) can solely extract desired faulty signal using some prior mechanical information. The methods of creating reference of cICA for machine diagnostics are discussed, and the effectiveness of the method is successfully verified by simulations and experiments.     

基于最小互信息准则的盲源分离在齿轮箱故障诊断中的应用

提出一种利用盲源分离技术对齿轮箱混合故障进行诊断的方法。该方法以最小互信息量为准则,采用自然梯度的自适应算法求解统计独立源信号的估计值,并根据分离信号的频谱成功地提取了混合故障的特征信息,有效地诊断出齿轮箱所处的故障状态。     

Source separation of diesel engine vibration based on the empirical mode decomposition and independent component analysis

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis(ICA).However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition(EMD) and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.     

Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic

Under the only hypothesis of independent sources, blind source separation (BSS) consists of recovering these sources from several observed mixtures of them. As it extracts the contributions of the sources independently of the propagation medium, this approach is usually used when it is too difficult to modelise the transfer from the sources to the sensors. In that way, BSS is a promising tool for non-destructive machine condition monitoring by vibration analysis. Principal component analysis (PCA) is applied as a first step in the separation procedure to filter out the noise and whiten the observations. The crucial point in PCA and BSS methods remains that the observations are generally assumed to be noise-free or corrupted with spatially white noises. However, vibration signals issued from electro-mechanical systems as rotating machine vibration may be severely corrupted with spatially correlated noises and therefore the signal subspace will not be correctly estimated with PCA.This paper extends a robust-to-noise technique earlier developed for the separation of rotating machine signals. It exploited spectral matrices of delayed observations to eliminate the noise influence. In this paper, we focus on the modulated sources and prove that the proposed PCA is available to denoise such sources as well as sinusoidal ones. Finally, performance of the algorithm is investigated with experimental vibration data issued from a complex electro-mechanical system.     

基于奇异值分解的欠定盲信号分离新方法及应用

提出一种利用相空间重构和奇异值分解实现信号升维,从而对欠定信号进行盲分离的新方法。选择合理的时间延迟和嵌入维数对信号进行相空间重构而得到吸引子轨迹矩阵,对该矩阵进行奇异值分解,并根据不同信号的奇异值分布特性选择合适的奇异值进行逆变换,从而可以得到源信号的新的线性组合,实现了信号升维。随后对新混合信号与原混合信号之间的关系进行讨论,分析二者之间的相关性,证明了该方法的合理性。利用该方法首先分析几种常见信号如正弦信号、调频信号、调幅信号等的奇异值分布特性,研究这些信号与白噪声混合时的欠定盲分离,并将其用于实测齿轮故障信号的盲分离,研究表明该方法能够识别齿轮系统的典型故障,取得了较好效果。     

Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis

Air-borne acoustic based condition monitoring is a promising technique because of its intrusive nature and the rich information contained within the acoustic signals including all sources. However, the back ground noise contamination, interferences and the number of Internal Combustion Engine ICE vibro-acoustic sources preclude the extraction of condition information using this technique. Therefore, lower energy events; such as fuel injection, are buried within higher energy events and/or corrupted by background noise.     

Improved independent component analysis based modal identification of higher damping structures

Structural modal parameter identification under ambient excitation has strong engineering value and theoretical significance. As the most popular tool for solving Blind Source Separation (BSS) problems, Independent Component Analysis (ICA) is able to directly extract the time-domain modal parameters, including frequencies, damping ratios and modal shapes. ICA, however, has a fatal flaw of failing to identify structures with higher damping. To overcome the flaw above, the paper proposes a new method named “ICA + IDT”. Firstly, free vibration response of a structure is obtained from structural outputs under ambient excitation. Inverse damping transfer (IDT) is employed to turn a highly damped signal into a low damping response signal without changing of frequencies and mode shapes. Then, structural modal parameters are extracted from the low damping response signal by ICA. Finally, the identified damping ratios are adjusted to eliminate the impact of IDT. To verify the effectiveness and applicability of IDT + ICA proposed herein, two numerical simulations—mass-spring model and simply supported concrete beam—and an experiment model of three-story steel frame are built, and the analysis results reveal that presented method can identify structures with higher damping effectively.