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 semianechoic chamber demonstrate the effectiveness of the presented methods.     

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.     

A subspace method for the blind extraction of a cyclostationary source: Application to rolling element bearing diagnostics

The need for blindly separating mixtures of signals arises in many signal processing applications. A class of solutions to this problem was recently proposed by the so-called blind source separation (BSS) techniques which rely on the sole knowledge of the number of independent sources present in the mixture. This paper deals with the case where the number of sources is unknown and statistical independence may not apply, but where there is only one signal of interest (SOI) to be separated, which is cyclostationary. It proposes a blind extraction method using a subspace decomposition of the observations via their cyclic statistics. This method is first developed for instantaneous mixtures and is then extended to the convolutive case in the frequency-domain where it does not suffer from the permutation problem as does classical BSS. Experiments on industrial data are finally performed and illustrate the high performance of the proposed method.     

大跨径桥梁实时动态挠度信号的分离

由于经验模式分解(empirical mode decomposition,简称EMD)将非线性非平稳信号分解成为一系列线性、平稳的本征模函数(intrinsic mode function,简称IMF)信号,针对单通道大跨径桥梁挠度信号分离问题,结合盲源分离和经验模式分解各自优点,提出基于经验模式分解的盲源分离方法。利用奇异值分解(singular value decomposition,简称SVD)估计信号源数目,根据源信号数目将单通道挠度信号和其本征模函数重组为多通道输入信号,应用独立分量分析（independent component analysis,简称ICA）理论中的快速独立分量分析（fast independent component analysis,简称FastICA）算法对输入信号进行分解,实现桥梁挠度信号各分量的分离。仿真研究表明,该方法能较好地解决ICA模型源数估计和单通道挠度信号盲源分离难题。     

用盲源分离技术创建独立的虚拟传感器

在振动与声测量中，由于结构对振动的传播作用以及声传播过程中散射与混响效应的存在，传感器(如加速度计或麦克风)所测得的信号往往是多个源的混合。盲源分离作为一种强有力的冗余取消工具，可以正确恢复独立源信号的波形。不过在具体实施中，所有的盲源分离算法都依赖于一个基本假设，即传感观测信号数必须大于或等于系统中的独立源数，而实际机器中独立源的数目往往未知。为此首先提出一种基于奇异值分解的聚类不相关源数估计新方法，估计一个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，从而共同构建一个能获取无法直接观测的独立源波形的虚拟传感观测系统。实验结果表明了该系统潜在的实用意义。     

Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition

This study reports a joint wavelet decomposition and Fourier transform approach to the separation of periodic mechanical source signals from single-channel signal mixture. With this method, the signal mixture is first decomposed to certain wavelet scales. The resulting wavelet coefficients are then Fourier transformed to extract the information pertinent to each signal source from these scales. Next, the number of signal sources is determined and the wavelet coefficients for each signal are constructed in all scale levels. Finally the source signals can be reconstructed using these wavelet coefficients. Since this method does not require the number of sources to be known a priori, it is particularly suitable for mechanical fault signal separation as the number of source signals varies with time and is unpredictable. It is also important to point out that the number of sources is determined without the commonly adopted sequential extraction/learning process and hence the proposed method can be used for on-line fault detection due to the reduced computing burden. The application of this method has been demonstrated using mixed bearing data containing both inner and outer race fault signals.     

Novel cyclostationarity-based blind source separation algorithm using second order statistical properties: Theory and application to the bearing defect diagnosis

Among signal processing techniques, blind source separation (BSS) and the underlying mathematical tool of independent component analysis (ICA) are of continuously growing interest in the scientific community of various research domains. Vibration analysis is a potential application field of this quite recent technique.Actually, BSS methods aim to retrieve unknown source signals from a set of their observations coming to a matrix of sensors, without necessarily having any prior knowledge about the sources. In monitoring and diagnosis purposes, bearing defects constitute a problem for manufacturers who aim at predicting those faults as well as potential engines breakdowns. These defects may be the unknown sources one wants to estimate from a set of recorded signals by a matrix of accelerometers placed close to the rotating machine.It has been shown that these vibration signals are wide-sense cyclostationary [[11] R.B.Randall, J. Antoni, S. Chobsaard, The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals, Mechanical Systems and Signal Processing 15 (5) (2001) 945–962]. The new algorithm of BSS proposed in this work is based, precisely, on that property. Second-order statistics of such processes led us to a new separation criterion for blind source separation. The theoretical results of this study, simulation and experimental analysis are presented in here. Perspectives for future research conclude this paper.     

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。     

融合小波分解与时频分析的单通道振动信号盲分离方法

针对单通道振动信号盲源分离是一个病态问题,且传统的振动信号盲源分离方法往往忽略信号的非平稳性的问题,提出了一种融合小波分解与时频分析的单通道振动信号盲源分离方法。首先利用小波分解与重构将单通道信号转化为多通道信号,解决了盲源分离的欠定问题;然后利用基于时频分析的盲源分离算法分析非平稳信号,得到源信号的估计信号,实现了非平稳信号盲源分离。仿真和实验结果表明,该方法可以有效地解决单通道非平稳振动信号的盲源分离问题。     

基于Gabor变换的欠定盲信号分离新方法

结合Gabor变换和盲信号分离的各自优点,提出了一种基于Gabor变换的欠定盲信号分离新方法.首先通过混合信号的Gabor变换系数之间的相互关系,得到了源信号个数的估计;然后对Gabor变换后的信号进行阈值处理,并进行Gabor逆变换得到新的混合信号,从而实现混合信号的升维.再利用现有的盲信号分离方法进行处理,该方法不受源信号个数的限制,因此属于一种欠定盲信号分离方法;最后,通过一组仿真信号的欠定盲分离验证了该方法的有效性.     

基于经验模式分解的单通道机械信号盲分离

盲源分离是机械设备复合故障诊断的一种有效方法,经验模式分解是非平稳信号分析的有力工具,它将非线性、非平稳信号分解成为一系列线性、平稳的本征模函数信号。在机械故障信号盲分离中,单通道机械信号盲分离是一个病态问题。针对单通道机械信号盲分离的困境,综合盲源分离和经验模式分解各自的优点,提出基于经验模式分解的单通道机械信号源数估计和盲源分离方法。针对单通道机械观测信号进行经验模式分解,并将单通道信号和其本征模函数组成多维信号,利用奇异值分解估计机械源数目,根据源信号数目重组多通道机械混合信号,并利用FastICA算法实现机械信号的盲分离。将该方法应用于轴承和齿轮的仿真研究,正确分离出轴承和齿轮源信号,仿真研究表明,它能很好地解决单通道机械信号的源数估计和盲源分离难题。     

航空发动机转子振动信号的分离测试技术

在传统谱分析方法的基础上,尝试应用盲源分离技术对飞机发动机振动信号进行振源分离.首先,介绍了发动机振动信号的基本处理方法和常见的发动机故障类型及特征,引入了盲源分离理论并讨论了其在航空发动机振动信号处理中应用的可行性.然后,对某型涡扇发动机振动过大的现象进行了故障诊断分析.最后,应用FastICA和JADE算法对检测的振动信号进行分析,分离出了发动机的振源信号.这说明发动机振动信号分析采用盲源分离与谱分析相结合的技术可以有效分离振源信号,提高故障诊断的准确性.     

高速列车非平稳振动信号盲源分离方法及应用

高速列车具有若干时变激励源,传统的时频分析方法只能对观测的混合振动的总体强度分布、时频域结构加以分析,不能分离出与各振源对应的信号分量从而明晰振源状态与故障特征。盲源分离是一种可行的分析方法,但由于高速列车振动信号具有时变振源数目、时变信号长度、受车速调制的变频非平稳等特征,传统的盲源分离方法不适用。为了提高高速列车非平稳信号的盲源分离效果,基于自适应滤波理论提出全局最优信噪比盲源分离新方法,并对其可分离性的判别依据进行论证。新方法的有效性经仿真计算和实测数据分析得到验证。研究表明：新方法对高速列车时变非平稳信号的盲源分离效果优于传统的基于非线性函数的盲源分离方法和基于高阶累积量的盲源分离方法。     

抑制Winger分布交叉项的胎心电半盲分离方法

根据母亲心电和胎儿心电在时域上具有的局部稀疏特性,提出了一种新的基于抑制源信号Wigner-Ville时频分布交叉项的胎心电信号半盲分离方法。该方法首先搜索腹部观测信号中母亲心电信号和胎儿心电信号局部稀疏的区域,再将该区域观测信号变换到Winger-Ville时频域,并在其中搜索源信号的子项和交叉项,以构建盲分离的目标函数提取胎儿心电信号。该方法将被认为无用的时频交叉项引入盲分离算法实现了胎儿心电信号的分离。模拟信号的实验证明,该算法与传统利用伪Wigner-Ville时频分布去除Wigner-Ville交叉项的盲分离方法性能非常接近。临床实验表明,该方法与FastICA方法和传统去除Wigner-Ville交叉项的盲分离方法获得的提取信号相似系数分别为0.953 3和0.970 8。     

Noise cancellation of ultrasonic NDE signals using blind source separation

This paper proposes a new denoising method for ultrasonic NDE (nondestructive evaluation) signals using blind separation (BSS) technology. The proposed denoising method consists of four steps. First, a reconstructed phase space (RPS) is constructed from observed ultrasonic NDE signals. The information about the underlying sources (e.g., ultrasonic signal, noise, etc.) acting on this system is contained in this RPS. Second, independent component analysis (ICA) is performed on the RPS to recover all sources underlying the RPS. Next, the ultrasonic signal component is selected by a decision criterion related to the denoising application and, finally, is reconstructed to obtain the denoised ultrasonic signal. To validate the proposed method, it has been applied to the experimental ultrasonic NDE signals of the test sample and is compared with the wavelet denoising method in SNR (signal-to-noise ratio) enhancement. The experimental results show that the SNR of the ultrasonic NDE signals can be enhanced greatly using the proposed denoising method and the proposed method has almost the same denoising performance as the wavelet denoising method in SNR enhancement. A trait of the proposed denoising method is the ability to denoise ultrasonic NDE signals by separating the ultrasonic signal and noise using blind source separation technology. The text was submitted by the authors in English.     

盲分离与Hilbert-Huang结合应用于航空发动机振动信号分析

在航空发动机故障诊断中,首要任务是分析故障信号提取故障特征。针对航空发动机非平稳振动信号,提出了利用盲分离(BSS)获得发动机的振源信号,结合Hilbert-Huang变换(HHT)对振源信号进行时频分析提取故障特征的方法。首先利用仿真信号验证了此方法的有效性,然后分析了某航空涡扇发动机空中停车故障并与直接应用HHT分析的结果进行比较,证实了盲分离与HHT的结合能更准确地提取航空发动机非平稳故障特征。     

基于盲源分离与小波降噪的旋转机械故障分析

基于小波降噪和盲源分离相结合对机械信号进行分离与故障诊断。首先使用经分析选择的较好小波阈值对非平稳振动信号进行降噪,然后运用盲源分离技术分离出激振信号,结果表明利用小波阀值降噪后进行盲源分离时分离信号与源信号相似系数优于直接盲源分离;将小波降噪和盲源分离相结合应用于某燃气轮机的实测故障信号提取,诊断出转子发生了不平衡及碰摩等故障现象,与实测情况相符,有效说明了该方法在旋转机械故障诊断中的实用性。     

基于小波变换的盲信号分离的神经网络方法

提出一种新的盲信号分离的神经网络方法,该方法将小波变换和独立分量分析(ICA,Independent Component Analysis)相结合。利用小波变换的滤噪作用,将混合在原始信号中的部分高频噪声滤除后,再重构原始信号作为ICA的输入信号,有效地克服了现有ICA算法不能将噪声从源信号中分离的缺陷。实验结果表明,将该方法用于多通道脑电信号的盲分离是很有效的。     

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。     

基于独立分量分析的潜艇振源贡献量定量计算方法

振动噪声控制对于潜艇具有重要意义。常用的振动噪声分析方法仅分析了噪声的来源,而未对振动噪声源对总振动噪声的贡献量进行定量计算。介绍盲源分离基本模型,以及基于独立分量分析理论和聚类评价方法提高盲分离性能的改进固定点算法,并基于该算法和先验信息理论提出一种定量计算振源贡献量的新方法。通过仿真试验分析基于两种不同分离准则算法的分离性能以及振源贡献量计算结果。将该方法应用于某型号潜艇缩比模型振源贡献量的定量计算中,对比分析表明该方法具有很高的计算精度。研究结论可为振动噪声的主动控制提供可靠的依据。