基于独立分量分析的壳体结构振源数目估计方法

在振源数目未知的条件下,仅利用多通道观测混合信号准确分离源信息是信号源盲分离的技术难题之一。为了揭示观测振动混合信号的复杂组成结构,以及为振源信息盲分离提供可靠的先验信息,提出一种基于独立分量分析和聚类评估技术的信源数目估计方法。通过仿真试验与壳体结构试验台典型机械振动信号分析,定量比较基于信息理论的源数估计方法AIC/MDL与提出方法的性能,研究结果表明所提出方法可从振动调制混合信号中准确可靠地估计信源数目,具有更好的工程应用性能。     

采用盲源分离的旋转机械振动仿真研究

盲源分离作为一种基于主向量分析的信号处理方法,目的是通过假设源信号之间的统计独立性,由一组观测信号恢复出源信号。研究了盲信号分离理论,尤其是盲卷积分离,采用基于交叉残余误差RCTE控制准则的盲分离算法,并针对旋转机械振动信号的特点生成仿真信号,将该方法应用于旋转机械振动信号瞬态成分与噪声卷积混合问题,仿真试验结果表明了该算法的有效性。     

信号源识别的相干函数法

实际工程中所采集的多个信号往往不满足独立性，而且独立信号源的个数也常常是未知的，针对此问题，提出一种基于相干函数分析的振动信号源识别方法。该方法可用于独立、非独立以及未知独立的信号源识别。对于检测到的振动信号，用虚相干函数中的虚输入矩阵确定信号源独立个数，并以此判断信号源是否独立。对于非独立信号源，提出一种优先级排序的滤波器法。在进行优先级排序后，用重相干函数检测是否有重要信号源被遗漏，然后分别用常相干函数和偏相干函数对独立信号源和非独立信号源进行识别。随机信号的仿真试验说明，基于相干函数分析的振动信号源识别方法对信号源的识别具有满意的效果。     

卷积混合机械非平稳振动信号的二阶盲分离方法

针对机械振动信号具有非平稳和卷积混合的特性,文中将基于二阶统计量的盲源分离方法推广至卷积混合的模型,提出在信号子空间的频域中对机械振动信号的盲解卷积方法.仿真和实测数据实验结果表明,此方法充分考虑信号的非平稳以及卷积混合特性,能较好地实现机械振动信号的盲分离.与传统盲源分离算法比较,该方法更适合于机械振动信号的分析.     

基于EMD-SVD-BIC的机械振动源数估计方法

提出了一种复合EMD-SVD-BIC(经验模态分解,empirical mode decomposition,简称EMD;奇异值分解,singular value decomposition,简称SVD;贝叶斯信息准则,bayesian information criterion,简称BIC)的机械振动源信号数量估计方法,解决卷积混合的机械振动源在观测数小于振动源数情况下的源数估计问题。应用EMD方法获得信号的本征模函数,对两观测信号的本征模函数复合矩阵的相关矩阵进行奇异值分解,获得反映源数信息的特征值分布;再采用BIC信息准则,判断源信号的数目。仿真和试验结果表明,该方法可以在观测数小于振动源数的情况下正确获取信号源数,为机械振动故障诊断中的振动源分析及其源信号的正确分离提供了方法保障。     

基于EMD-SVD-BIC的机械动源数估计方法

提出了一种复合EMD-SVD-BIC(经验模态分解,empirical mode decomposition,简称EMD;奇异值分解,singular value decomposition,简称SVD;贝叶斯信息准则,bayesian information criterion,简称BIC)的机械振动源信号数量估计方法,解决卷积混合的机械振动源在观测数小于振动源数情况下的源数估计问题.应用EMD方法获得信号的本征模函数,对两观测信号的本征模函数复合矩阵的相关矩阵进行奇异值分解,获得反映源数信息的特征值分布;再采用BIC信息准则,判断源信号的数目.仿真和试验结果表明,该方法可以在观测数小于振动源数的情况下正确获取信号源数,为机械振动故障诊断中的振动源分析及其源信号的正确分离提供了方法保障.     

基于PCI数据采集卡的旋转机械阶比跟踪算法与实现

介绍了一种用于旋转机械振动信号分析的阶比跟踪方法，以及基于PCI数据采集卡的具体实现方法。由于旋转机械振动信号的振动频率和轴的转动频率有一定的比值关系，所以，采用时域同步滤波可消除与回转频率无关的噪声干扰，提取与转速直接相关的周期信号。     

基于多重分形的旋转机械振动故障检测方法

为获取更加准确的旋转机械振动故障检测结果，提出基于多重分形的旋转机械振动故障检测方法。通过建立旋转机械振动信号采集模型，获取振动信号，采用小波域维纳滤波算法对振动信号去噪处理。分析不同条件下的振动数据，同时引入多重分形方法提取旋转机械振动信号故障特征，通过核模糊C均值聚类算法区分正常信号和故障信号，最终实现旋转机械振动故障检测。实验结果表明，所提方法进行旋转机械振动故障检测率较高，漏检率较低，检测时间较短，可以快速准确地完成旋转机械振动故障检测。     

一种应用重采样技术的整周期采样方法

介绍了一种应用重采样技术的旋转机械振动信号的整周期采样方法。通过三次样条插值，从定时采样的数据中获得整周期采样的数据，经过实验，证明是可行的。     

理想的计量标准仪器名符其实的高精度直流信号源

理想的计量标准仪器名符其实的高精度直流信号源上海工业自动化仪表研究所西派埃自动化技术工程公司刘慰严市场上直流信号源品种繁多，各有千秋。但要找到一种真正高精度、高稳定的直流信号源且价格适中，却甚为不易。本文报导的这则信息，也许对您很有帮助。众所周知，一...     

基于盲源分离技术的故障特征信号分离方法

信号采集过程中，传感器测量到的信号是实际振动信号在此测量方向的投影值，由于其他不相干振源的影响，测量信号由多个振动信号成分组成。在分析多振源信号混合模型的基础上，采用盲源分离技术分离不同的振源信号，讨论分离结果的广义初等相等性质的影响，研究估计振源数目的方法和选取测量信号的方法，利用二阶特征矩阵联合近似对角化算法，从测量信号中分离故障特征源信号。该算法可减小信号采集不当造成的影响，有效提高特征信号的提取。     

多振源卷积混合的时域盲源分离算法

在机械多源振动传播和卷积混合模型的基础上,提出一种基于时域的多振源卷积混合信号的盲源分离算法.该算法以独立性为评判准则,采用反向学习和合理简化滤波器系数的方式,进行滤波器系数的学习,进而实现基于时域的多振源卷积混合信号的分离.仿真试验和多机振动源试验结果表明,该算法对于多源卷积混合信号具有很好的分离效果,可应用于机械设备多激振源卷积混合情况下机械振动源信号的有效分离.     

Blind source separation of internal combustion engine piston slap from other measured vibration signals

Internal combustion engines have several vibration sources, such as combustion, fuel injection, piston slap and valve operation. For machine condition monitoring or design improvement purposes, it is necessary to separate the vibration signals caused by different sources and then analyse each of them individually. However, traditional frequency analysis techniques are not very useful due to overlap of the different sources over a wide frequency range. This paper attempts to separate the vibration sources, especially piston slap, by using blind source separation techniques with the intention of revealing the potential of the new technique for solving mechanical vibration problems. The BSS method and the Blind least mean square algorithm using Gray's variable norm as a measure of non-Gaussianity of the sources is briefly described and separation results for both simulated and measured data are presented and discussed.     

A blind deconvolution separation of multiple sources, with application to bearing diagnostics

A method is presented, geared to separating signals from different sources which are convoluted and mixed by the mechanical systems before being measured. The method is based on an automatically operating blind deconvolution separation method, with Kurtosis of the separated signals as the measure to be maximised. The application described involves bearings diagnostics, whereas with many classical diagnostic methods, Kurtosis is traditionally one of the accepted criteria for fault detection. The method is tested on simulation and experimental cases. Results show that separation is possible even when measurements are distanced from the vibration exciting sources of the faulty bearing. Furthermore, the method eliminates the effect of structural resonances, which often causes severe problems in classical diagnostic methods.     

混合信号振声源分离与多机组故障诊断

针对机房设备混合信号难以提取有用信息,提出了多参数的振声诊断方法。应用最小互信息梯度下降的盲分离算法,通过展开边缘熵和修正四阶累积量估计值的方法改善算法性能,在故障源数量未知且可能大于传感器数量的情况下,根据信息源之间的独立性测度关系依次提取最显著的特征值。仿真结果证明,改进算法估计误差减小且算法可靠。在诊断实例中,首先,分离机房内的混合噪声信号以确定主要故障来源;然后,采集故障源的振动信号进行非线性盲分离,提取热泵机组压缩机不对中、齿轮啮合不良和碰磨的故障特征;最后,根据分离的振源信号特征识别故障类型,建立基于盲源分离算法的大空间设备群的振声诊断方法。     

强脉冲干扰非线性混叠转子故障信号分离

由于旋转机械在运行过程中,传感器测得的振动信号是各振源的混叠信号且含有很强的噪声,常规的信号处理方法难以分离混叠信号,对设备的状态监测和故障诊断造成了很大的困难。针对这一问题,介绍了盲源分离基本原理和方法,指出源分离算法在脉冲噪声环境下失效。针对强脉冲噪声环境下的混叠振动信号,首先,通过中值滤波降噪方法对振动信号进行降噪;然后,通过盲源分离算法对降噪后的信号分离;最后,利用该方法对实测混叠转子振动信号成功实现了降噪和故障信号分离。仿真结果验证了提出方法的有效性。     

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.     

基于最大熵条件下机械噪声盲源分离方法的研究

盲源分离方法是近年来出现的一种先进的信号处理方法。本文基于对最大熵(ME)的原理,推导了基于自然梯度法的盲信号处理的计算方法,并通过实例说明本方法对噪声源分离是可能的,为机械设备的故障诊断和监察提供了一个新的方法。     

基于阶比滤波的单通道缸盖振动信号盲源分离

传统盲源分离方法要求传感器观测信号数目不小于源信号数目,且在源信号平稳、相互独立的前提下,才能得到较为准确的分离信号,但对于发动机缸盖振动非平稳信号,由于激励源较多,这些条件不易满足。为实现缸盖振动信号盲源分离,提出了基于阶比滤波的单通道缸盖振动信号盲源分离方法。利用燃爆激励信号频率随转频变化的先验信息,通过阶比滤波得到阶比分量,将阶比分量和单通道信号组成多维观测信号,通过快速独立成分分析方法得到了缸盖振动非平稳信号的分离信号。仿真和应用研究证明了该方法的有效性。     

Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods .