基于Gabor变换降噪和盲信号分离的轴承故障诊断方法

针对轴承故障信号往往淹没在强烈的系统噪声中导致故障特征难以提取的情况,提出了一种基于Gabor变换降噪和盲信号分离的故障诊断方法。该方法利用Gabor变换对时频信号的优良分辨率和盲信号分离技术的优势,先对非平稳信号进行降噪,再通过盲信号分离技术对降噪后的信号进行分离,提取出故障频率。实验结果表明,该方法能有效地诊断出轴承故障特征。     

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.     

一种基于子空间法的机械故障欠定盲分离新算法

针对以往稀疏盲信号分离算法中恢复源信号时所采用的线性规划或最短路径法计算相对复杂,提出了一种基于子空间法的机械故障欠定盲源信号恢复方法。该算法假设源信号由两个正交向量构成:其中一个向量位于混叠矩阵A的行空间中,另一个位于A的零空间中,位于行空间中的向量可以通过A的Moore-Penrose伪逆得到,位于零空间中的向量通过贝叶斯估计得到。新算法容易实现,分离速度快,能够很好地满足盲分离对速度的要求。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分离齿轮系统的典型故障,取得了较好效果。     

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。     

基于时频分析的欠定信号盲分离与微弱特征提取

盲源分离对于多振源信号的故障诊断与识别是一种有效的方法,但是传统的盲源分离算法都是针对观察信号大于或等于源信号的情况,但对于观察信号小于源信号的欠定盲分离问题,这在很大程度上制约了盲源分离的实际应用。通过应用经验模式分解和时频分析对非平稳信号分析的优势,提出基于时频分析的欠定盲源分离方法进行设备微弱特征提取。对振动信号进行经验模式分解,并根据分解得到的内蕴模式分量估计源信号个数并选择最优的观察信号,将振动信号与选择的最优观察信号组成新的观察信号进行基于时频分析的盲源分离,通过对仿真信号和齿轮箱实测信号进行验证分析。并与基于独立分量分析的盲源分离算法进行对比,研究表明基于时频分析的盲源分离对混合信号具有更好的分离效果,能够较好地对微弱特征进行提取。     

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。     

基于独立分量分析的高光谱图像端元提取算法

本文提出了一种基于独立分量分析的高光谱图像端元提取算法.独立分量分析是一类解决盲信号分离问题(BSS)技术的总称,而所谓的盲信号分离是在源信号和混合系统未知的情况下,只根据混合信号分离出源信号的问题.在假设端元分布独立的情况下,本文把端元提取问题转化为盲信号分离问题,并利用一种具体的ICA技术--联合对角化算法来解决.最后,通过仿真验证了算法的有效性.     

基于EMMD和BSS的单通道旋转机械故障诊断方法

针对在欠定的观测信号情况下,传统基于矩阵的盲源分离算法效果比较差的问题,提出一种基于极值域均值模式分解和盲源分离的单通道旋转机械信号故障特征提取方法,并应用于实际的故障诊断中.该方法先通过极值域均值模式分解法分解观测信号,把得到的固有模态函数和原观测信号一起组成新观测信号,从而实现了信号升维,使欠定问题转化为正定问题;然后,由奇异值分解和贝叶斯准则进行源数估计;最后,利用基于四阶累积量的特征矩阵联合对角化方法实现信号的盲分离.通过仿真,验证了该方法对旋转机械故障信号进行盲源分离的可行性.将提出的方法应用到齿轮和轴承系统的故障诊断中,进一步证明了该方法的有效性.     

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。     

基于核函数的二阶盲辨识的单通道信号盲分离方法研究

调整权值的二阶盲辨识(WASOBI)算法已应用于故障诊断领域,但尚不能在欠定状态下对复合故障进行诊断。将该算法与核函数相结合实现了欠定盲源分离,并将其应用到复合故障诊断中。首先运用核函数将单通道信号构造为多维信号,并利用K-SVD源数估计方法估计出源信号个数,然后根据估计的结果重构出正定的观测信号矩阵,解决欠定问题,最后采用调整权值的二阶盲辨识算法将各故障源信号分离出来。仿真分析和实验结果表明,该方法能有效地解决欠定盲源分离问题,并使轴承各故障源信号分离,实现复合故障诊断。     

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.     

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 .     

基于LCD的齿轮箱混合故障盲源分离研究

盲源分离是一种有效的混合故障诊断方法,而局部特征尺度分解（LCD）是非平稳信号的有效分析处理工具,综合两者的优点,提出了基于LCD的齿轮箱混合故障盲源分离方法。将源信号LCD分解,得到新的多维信号,采用Bayesian信息准则（BIC）估计盲源的数目并对多维信号进行重组。最后进行联合近似对角化处理,实现源信号的盲分离。仿真和实验结果表明,该方法能够有效地实现齿轮箱混合故障盲源分离。     

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。     

Maximum kurtosis principle for the parameter selection of Gabor wavelet and its application to ultrasonic signal processing

The noise suppression techniques with wavelet transform (WT) are widely used in nondestructive testing and evaluation (NDT&E), especially in ultrasonics. But the wavelet based filter has the property of equal Q-factor, so, it is impossible to choose the central frequency and the bandwidth arbitrarily at the same time. This paper develops a new technique using WT to eliminate this drawback. In this paper, a weak ultrasonic signals identification method by using the optimal parameter Gabor wavelet transform is proposed. We address the choice of the optimal central frequency and bandwidth of the Gabor wavelet using the kurtosis maximization algorithm. The central frequency and bandwidth of the optimal parameter Gabor wavelet matched that of the ultrasonic signal very well. Numerical and experimental results have been presented to evaluate the effectiveness of the optimal parameter Gabor wavelet transform on ultrasonic flaw detection. This technique is a simpler and effective technique for processing heavy noised ultrasonic signals.     

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。     

FREQUENCY OVERLAPPED SIGNAL IDENTIFICATION USING BLIND SOURCE SEPARATION

The concepts, principles and usages of principal component analysis (PCA) and independent component analysis (ICA) are interpreted. Then the algorithm and methodology of ICA-based blind source separation (BSS), in which the pre-whitened based on PCA for observed signals is used, are researched. Aiming at the mixture signals, whose frequency components are overlapped by each other, a simulation of BSS to separate this type of mixture signals by using theory and approach of BSS has been done. The result shows that the BSS has some advantages what the traditional methodology of frequency analysis has not.     

A fast multi-level decomposition algorithm for n-dimensional discrete Gabor transforms

Recently, joint spatial and spatial-frequency representations have been used in signal processing of non-stationary signals due to their natural local property and high joint resolution in both the spatial and spatial-frequency domain. However, a major obstacle to their implementation is their large computation requirements. This paper presents a fast n-dimensional Gabor transform and signal reconstruction algorithm employing multi-level parallel decomposition and fast Fourier transform techniques. The algorithm structure lends itself to implementation using VLSI/ASIC technology. Examples of two-dimensional Gabor transform and reconstruction performed on a AT computer demonstrate the substantial computational saving that can be achieved using the fast Gabor transform.     

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。