Blind separation of vibration components: Principles and demonstrations

Blind source separation is the issue of recovering the various independent sources exciting a system given only the measurements of the outputs of that system. It has recently become the focus of intensive research work due to its high potential in many applications. However when it comes to vibration signals, blind source separation faces a number of difficulties which seriously hinder its feasibility. In this paper we first draw a list of these difficulties. We then propose to somehow lessen the ambitions of BSS to more realistic goals, and in particular to separate vibration signal into contributions of (1) periodic, (2) random stationary, and (3) random nonstationary sources. Although not ideal, such a separation can provide substantial information in many practical cases of interest. Towards this aim we propose two robust separation techniques based on the short-time Fourier transform. They are perfectly suited to handle convolutive mixtures of sources and they lead to fast and efficient implementation.     

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.     

基于VbMoICA的机械故障盲源分离研究

用独立分量分析(ICA)分解和表示数据时,假设整个数据分布完全可以用一个坐标系来描述。然而,当观测数据是由许多自相似的、非高斯的流形组成时,则硬是用一个单独的、全局的表示是不合适的,这样会产生一个次优的表示。针对ICA在盲源分离中的不足,在变分贝叶斯理论的基础上提出了一种基于变分贝叶斯混合独立分量分析的机械故障源盲分离方法。该方法是考虑到源信号来自于多个坐标系,然后在多个坐标系下建立独立分量分析混合模型对观测信号进行学习分离。实验结果表明,本文提出的方法是非常有效的。     

一种新的盲信号源数估计方法研究

传统源数估计方法要求传感器数大于或等于源数,而盲信号很难满足这个条件,为此,提出了一种新的源数估计方法。该方法在传感器数与源数关系不明确的情况下,仅根据观测信号的功率谱密度函数的比值即可对源数作出估计。通过理论分析、仿真和实验,证明了该方法的有效性。     

基于非负矩阵分解的盲信号源数估计

为满足盲源分离算法对振源信号数量的苛刻要求,提出了一种基于非负矩阵分解的源数估计方法。该方法在传感器数大于或等于源数时,无论源信号是否相关均能准确估计源数;在传感器数小于源数时,能估计源数的下界。理论分析、仿真和工程实验证明了该方法的有效性和可行性。     

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 .     

Segmentation of digitized histological sections for quantification of the muscularized vasculature in the mouse hind limb

Immunohistochemical tissue staining enhances microvasculature characteristics, including the smooth muscle in the medial layer of the vessel walls that is responsible for regulation of blood flow. The vasculature can be imaged in a comprehensive fashion using whole‐slide scanning. However, since each such image potentially contains hundreds of small vessels, manual vessel delineation and quantification is not practically feasible. In this work, we present a fully automatic segmentation and vasculature quantification algorithm for whole‐slide images. We evaluated its performance on tissue samples drawn from the hind limbs of wild‐type mice, stained for smooth muscle using 3,3'‐Diaminobenzidine (DAB) immunostain. The algorithm was designed to be robust to vessel fragmentation due to staining irregularity, and artefactual staining of nonvessel objects. Colour deconvolution was used to isolate the DAB stain for detection of vessel wall fragments. Complete vessels were reconstructed from the fragments by joining endpoints of topological skeletons. Automatic measures of vessel density, perimeter, wall area and local wall thickness were taken. The segmentation algorithm was validated against manual measures, resulting in a Dice similarity coefficient of 89%. The relationships observed between these measures were as expected from a biological standpoint, providing further reinforcement of the accuracy of this system. This system provides a fully automated and accurate means of measuring the arteriolar and venular morphology of vascular smooth muscle.     

Detection of food fraud of meat products from the different brands by application of histological methods

In Sohag City, 400 samples were collected from different food markets of different meat products from two companies with high and low prices (e.g., minced meat, kofta sausage, beef burger, and luncheon meat) for determining food fraud. Light, fluorescence, and scanning electron microscopy (SEM) were used to examine the samples. “Special histochemical stains” permit the microscopic examination of different cell types, structures, and/or microorganisms. Histological examination revealed variant tissue types, besides skeletal muscles. Nuchal ligaments, bones, hyaline cartilages, white fibrocartilages, large and medium arteries, cardiac muscles, tendons, and collagenous connective tissues comprised the capsule of a parenchymatous organ. Additionally, a crystal of food additives was recognized using light microscopy and SEM. SEM allows the visualization of bacterial contamination. Using different microscopic anatomy techniques is an efficient methodology for qualitative evaluations of various meat products. No difference in quality was observed between low- and high-priced meat products.