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.     

侵彻过载信号的欠定盲源分离与特征提取

侵彻过载信号成份复杂,传统盲源分离方法无法有效提取弹体侵彻板靶特征,基于此提出一种不受测试传感器数量限制、具有源数估计的侵彻过载信号盲源分离方法。首先,对单通道测试信号进行总体经验模态分解,将分解后的固有模态与原信号组成多维信号;其次,对组成的多维信号奇异值分解,以"前K次奇异值占优"法则估计信号振源个数,利用"最大互相关系数法"筛选最优IMF函数与原信号重组构造多通道混合信号;最后,对多通道混合信号白化处理和联合近似对角化,计算酉矩阵获得测试信号的混合估计。将其用于单通道侵彻过载信号的特征提取,获得了与源信号相关度为0.974 7的加速度特征信号。与现有方法相比,该方法能有效分离出单通道侵彻过载特征信号,并且信号处理过程具有的自适应特性也解决了不同弹靶工况下过载信号滤波频率的选择困难问题。     

盲源分离方法在旋转机械故障诊断中的应用

在旋转机械故障诊断中,从被监测机器测得的振动信号除噪声干扰外,还常混叠有其它机器的振动。由所测得的此混叠信号中分离出被监测机器的信号,并降低噪声的影响,是提高故障诊断准确性的基础。这里采用四阶累积量迫零算法对机器振动信号进行分离,通过仿真信号及实验室实测振动信号的分离验证,此方法是有效的,可作为旋转机械故障诊断的信号预处理方法。     

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.     

基于NPCA的后非线性盲源分离方法

提出一种基于非线性主元分析和H∞滤波算法相结合的后非线性盲源分离方法,首先根据后非线性混叠的线性时变模型构造用于求解时变解混矩阵的非线性主元分析代价函数,然后利用H∞滤波算法优化其函数来恢复混合在非线性观测信号中的源信号,该方法仅需要源信号统计独立特性的先验信息。仿真实验结果表明,该方法不仅能以比其他传统方法更好的估计性能分离服从超高斯和亚高斯不同分布的源信号,还能在线动态地实现信号的后非线性盲分离。     

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.     

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。     

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

Fault diagnosis of gearboxes, especially the gears and bearings, is of great importance to the long-term safe operation. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the health condition of the gearbox in a timely manner to eliminate the impending faults. However, useful fault detection information is often submerged in heavy background noise. Thereby, a new fault detection method for gearboxes using the blind source separation (BSS) and nonlinear feature extraction techniques is presented in this paper. The nonstationary vibration signals were analyzed to reveal the operation state of the gearbox. The kernel independent component analysis (KICA) algorithm was used hereby as the BSS approach for the mixed observation signals of the gearbox vibration to discover the characteristic vibration source associated with the gearbox faults. Then the wavelet packet transform (WPT) and empirical mode decomposition (EMD) nonlinear analysis methods were employed to deal with the nonstationary vibrations to extract the original fault feature vector. Moreover, the locally linear embedding (LLE) algorithm was performed as the nonlinear feature reduction technique to attain distinct features from the feature vector. Lastly, the fuzzy k-nearest neighbor (FKNN) was applied to the fault pattern identification of the gearbox. Two case studies were carried out to evaluate the effectiveness of the proposed diagnostic approach. One is for the gear fault diagnosis, and the other is to diagnose the rolling bearing faults of the gearbox. The nonstationary vibration data was acquired from the gear and rolling bearing fault test-beds, respectively. The experimental test results show that sensitive fault features can be extracted after the KICA processing, and the proposed diagnostic system is effective for the multi-fault diagnosis of the gears and rolling bearings. In addition, the proposed method can achieve higher performance than that without KICA processing with respect to the classification rate.     

并联双重Q因子在齿轮箱复合故障净化提取与盲分离中的应用

基于Q因子的稀疏分解是信号的一种自适应稀疏化表达方法。针对强噪声环境下齿轮箱非平稳复合故障信号难于提取与分离的问题,提出基于并联双重Q因子的快速独立分析方法。首先通过基于并联双重Q因子的小波变换分析方法对单通道故障信号进行降噪和升维处理,根据不同的低Q因子值得到多组低共振的冲击成分,组成多维信号,实现信号升维,然后应用快速独立分析方法进行盲分离。仿真信号数据分析结果及滚动轴承复合故障的实验数据分析结果均表明了该方法的可行性和有效性,为强噪声环境下的复合机械故障信号分离与提取提供了一种新的思路。     

Output-only modal analysis using blind source separation techniques

The present study carries out output-only modal analysis using two blind source separation (BSS) techniques, namely independent component analysis and second-order blind identification. The concept of virtual source is exploited and renders the application of these BSS techniques possible. The proposed modal analysis method is illustrated using numerical and experimental examples.     

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。     

改进的ESMD用于公共场所异常声音特征提取

由于公共场所异常声音的特殊性及背景噪声的复杂性,极点对称模态分解(ESMD)用于异常声音分解时,存在一些理论和技术上的缺陷。经分析认为公共场所异常声音为非线性、非平稳信号,背景噪声服从T分布。为此,提出改进的ESMD用于公共场所异常声音分解,得到有利于识别的特征。所提出方法的特点是将T分布噪声序列添加到具有背景噪声的异常声音信号中,以减小背景噪声对特征提取的影响;将模态分量的排列熵作为判定异常声音与背景噪声的准则,自适应筛选有效的模态分量;用对称中点插值法替代极值中点奇偶插值法,以缓解ESMD插值端点不明确带来的模态失真。在公共场所异常声音数据库上进行了相关实验。实验结果表明,所提出的方法与目前典型的时频信号处理方法相比,在提高公共场所异常声音分类识别率的同时,缩短异常声音的分解时间,是一种有效的公共场所异常声音特征提取方法。     

一种适用于信源数时变的自适应盲源分离算法

针对信源数动态变化情况下的盲源分离问题,首先采用一种基于交叉验证技术的估计方法用于估计时变的源数,然后推导了一种基于自然梯度和Frobenius范数相结合的自适应盲源分离算法,该算法不需要对源信号作任何约束性的假设,因此该算法适合于分离服从超高斯或亚高斯分布的信号。提出的算法通过了源数不变和源数动态变化2种方式实验的验证。     

具有延迟的未知数目稀疏源盲混合信道估计方法

在具有延迟的稀疏源盲分离中,混合信道的估计是一个关键组成部分,其估计精度直接影响到源信号的估计精度.提出了一种源信号数目未知时的混合信道盲估计方法.首先根据时频域观测信号幅值呈线性聚类的几何特点,提出了一个新的线性聚类有效性准则,并根据此准则估计源信号数目.同时利用改进的霍夫变换寻找每一类数据的致密直线方向,作为衰减矩阵列向量的估计.然后利用时频域不同观测信号间的相位差特性,估计延迟矩阵.几个语音信号的仿真实验表明了该方法的有效性和实用性.     

采用盲源提取和后小波滤波的胎儿心电图提取

胎儿心电图能够提供有关胎儿健康的信息,帮助确定胎儿是否有疾病,在临床上具有很重要的意义。将心电图仪的电极放置到孕妇体表,获取来自母体内部的心电信号,该信号是母体心电图与胎儿心电图的混合。目前主要有两种方法可以通过处理混合信号,获得胎儿心电图。其中,采用盲源提取的方法可以用较小的运算量获得胎儿心电图,但这种方法提取的胎儿心电图容易受到噪声的污染。而在小波域进行盲源分离,可以分离出较为纯净的胎儿心电图,该方法的缺点是运算量大。提出了一种新的胎儿心电提取方法,该方法首先利用盲源提取算法从采集到的心电信号中提取出受到噪声污染的胎儿心电图,然后再利用小波滤波滤除噪声,获取较为纯净的胎儿心电图。该方法既具有较小的运算量,又可以获得较好的提取性能。合成心电数据仿真和实际胎儿心电图提取试验均验证了该方法的有效性。     

一种图像边缘特征提取算法

边缘作为一个重要特征是图像目标检测中基础而又困难的一个问题.用常规方法进行边缘检测时,噪声会影响到边缘特征提取的准确性.为了减小噪声对图像边缘特征的影响,改善边缘特征的定位精度,本文提出了一种新的图像边缘特征提取算法.该方法利用小波变换天生的多尺度特性,对小波变换各尺度下的细节图像用互能量交叉的方法进行噪声抑制和边缘识别.最后作了实例验证.     

自适应变尺度特征点提取方法

为了提高特征点匹配速度,设计了一种自适应变尺度构造图像金字塔的特征点提取方法。该方法采用FAST特征点数量作为尺度空间信息量的度量,利用相邻两层模糊图像的信息量差作为金字塔分层依据,通过调整尺度参数,使相邻图像间的细节特征均匀变化;并使用匹配点数量阈值控制金字塔的高度,设计利用"边匹配,边构造"的图像匹配策略来提高特征匹配的效率。最后,将所设计方法与SIFT、FAST、ASIFT三种特征提取方法进行比较。实验结果表明:所设计方法在变尺度条件下的正确匹配率可以达到43.59%,与SIFT相比提高了25.51%,提取的特征点在目标经历各种光照、角度等变化之后仍能正确表示目标。本文所设计方法根据目标图像特点自适应选择参数,不需要人工调整就可获得理想的匹配效果,能适应各种变化条件下的特征提取和匹配工作,并能提高特征提取和匹配效率。     

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.     

Application of improved morphological filter to the extraction of impulsive attenuation signals

Rotating machinery response is often characterized by the presence of periodic impulses modulated by high-frequency harmonic components. It can be defined with three parameters, which are natural frequency, fault frequency and decay coefficient. In this paper, we propose an improved morphological filter for feature extraction of the above signals in the time domain. Firstly, an average weighted combination of open-closing and close-opening morphological operator, which eliminates statistical deflection of amplitude, is utilized to extract impulsive component from the original signal. Then, according to the geometric characteristic of impulsive attenuation component, the structure element is constructed with an impulsive attenuation function, and a new criterion is put forward to optimize the structure element. The proposed method is evaluated by simulated impulsive attenuation signals with different natural frequencies and vibration signals measured on defective bearings with outer race fault and inner race fault, respectively. Results show that the background noise can be fully restrained and the entire impulsive attenuation signal is well extracted, which demonstrates that the method is an efficient tool to extract impulsive attenuation component from mechanical signals.     

瞬态信号边际谱中故障特征频率的定常化提取

Hilbert-Huang变换具有自适应的分析非平稳信号的能力,因此常被用于机械故障诊断.此时,从边际谱中提取与故障特征频率对应的峰值是主要的故障特征提取方式.对于转速变化的旋转机械,故障特征频率常与转频相关,随转频变化而变化,所以常因边际谱中故障谱线不单一而难以进行特征提取.提出了故障特征频率的定常化方法,将故障特征相对某一转速定常化为单一谱线,解决了瞬态信号故障特征难以提取的难题.并用实例验证了其有效性.