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.     

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。     

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。     

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。     

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。     

基于小波域平稳子空间分析的风力发电机齿轮箱故障诊断

风力发电机齿轮箱故障信号为非平稳瞬态微弱信号,容易被齿轮啮合信号及其他噪声淹没。提出一种融合连续小波变换(Continuous wavdot transform,CWT)和平稳子空间分析(Startionary subspace analysis,SSA)的信号分解方法并应用于风力发电机齿轮箱故障诊断中。平稳子空间分析作为一种盲源分离技术可将高维数据分解成平稳源部分和非平稳源部分,对待分析信号各分量间的独立性没有要求且不需要任何先验信息。连续小波变换则可利用其所具有的多尺度分析特性把一维时间序列转换为不同尺度下的多维时间序列。对观测得到的一维时间序列数据进行连续小波变换得到多维时间序列作为平稳子空间分析的输入,利用平稳子空间分析方法将该多维时间序列分解为平稳源信号分量和非平稳源信号分量,对非平稳源信号进行包络谱分析得到齿轮箱故障的特征频率。该小波域平稳子空间分析方法被应用于一个实际风力发电机齿轮箱振动信号的分析,试验结果表明该方法可有效地诊断出齿轮箱中的轴承故障。     

应用多分辨SVD和ICA的轴承复合故障诊断研究

针对滚动轴承复合故障信号中故障特征难以分离的问题,提出了基于多分辨奇异值分解(SVD)和独立分量分析(ICA)的复合故障诊断方法。首先利用多分辨SVD将复合故障振动信号分解为几个分量实现维数的增加;然后将分解得到的分量组合为混合信号,并利用ICA进行欠定盲分离;最后对分离后的独立分量进行Hilbert包络解调,由此实现对复合故障特征信息的分离和故障识别。通过对滚动轴承内外圈复合故障的试验信号分析表明,该方法可以有效地分离和提取轴承复合故障的特征信息。     

基于EMD-PCA的轴承故障源盲分离方法

结合经验模态分解和主分量分析各自的优点,提出了一种基于EMD-PCA的轴承故障源的盲分离方法。利用EMD方法对混合观测信号进行分解得到若干个本征模函数分量,把所有的IMF分量重新组合在一起,作为新的观测信号,然后采用PCA对新的观测信号进行共性分析以得到源信号中的主要成分,并进行了轴承故障源分离试验验证。     

LMD-ICA联合降噪方法在滚动轴承故障诊断中的应用

针对经典独立分量分析（ICA）只能应用于观测源数不少于信号源数的超定盲源分离问题,提出局部均值分解和ICA相结合的欠定盲源分离新方法。该方法将采集的单通道振动信号进行局部均值分解,基于互相关准则对分解的分量进行重组,构建虚拟噪声通道;将虚拟噪声通道与振动信号作为盲源分离的信号输入,采用基于负熵的FastICA算法实现信号源和噪声的分离,从而达到降噪目的。将该方法应用于滚动轴承故障信号,频谱分析结果表明,该方法处理后的信号中噪声得到一定程度滤除,频谱中毛刺更少,故障特征频率更加明显,有利于故障特征的提取,实验分析证明了该方法的有效性。     

基于形态分量分析与能量算子解调的齿轮箱复合故障诊断方法

针对齿轮箱复合故障的故障特征分离,提出了一种基于形态分量分析与能量算子解调的齿轮箱复合故障诊断方法。该方法先根据振动信号中各组成成分形态的差异,采用形态分量分析方法构建不同形态的稀疏表示字典进行故障成分分离,将齿轮箱复合故障信号分解为包含齿轮故障信息的谐振分量、包含轴承故障信息的冲击分量和噪声分量,然后分别对谐振分量和冲击分量进行能量算子解调分析,最后根据各解调谱诊断齿轮和轴承故障。算法仿真和应用实例表明该方法能有效地分离齿轮箱复合故障振动信号中齿轮与轴承的故障特征。     

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.     

基于量子计算的独立分量分析算法及应用

将量子优化原理应用于独立分量分析中,提出了量子独立分量分析算法(quantum independent component analysis,简称QICA),针对3组特定信号进行了混合与分离的仿真实验,得到了较好的分离效果。将该算法用于齿轮箱振动信号的源分离及其故障诊断中,实验结果表明,该算法用于齿轮箱振动信号分离可以明显增强故障信息,降低齿轮箱故障诊断难度。     

基于LMD的包络谱特征值在齿轮箱故障诊断中的应用研究

针对齿轮箱故障振动信号大多是多分量的调幅-调频信号,而传统包络分析法又太依赖经验值选取参数的问题,对齿轮箱振动信号的分解方法、包络分析方法以及提取特征值等方面进行了研究,提出了一种基于局部均值分解(local mean de-composition,LMD)的包络谱特征值的方法。该方法首先利用局部均值分解对齿轮箱信号进行了处理,获得了包含有不同频率特征的PF(product function)分量,最后对包含有主要故障信息的第一级PF分量进行了包络分析,提取了包络谱的特征频率,以此来判别齿轮箱的工作状态和故障类型。利用齿轮箱正常状态、局部损伤、磨损故障3种齿轮箱振动信号的实例进行了验证。研究结果表明,利用LMD分解后求取包络谱特征频率的方法能够较为准确地判别齿轮箱的工作状态和故障类型。     

齿轮箱复合故障信号的非线性盲源分离算法研究

针对齿轮箱复合故障分析问题,文中提出一种新型非线性盲源分离（Nonlinear Blind Source Separation, NBSS）算法。该算法先利用反向传播（Back Propagation, BP）神经网络逼近非线性混合模型的逆,并对经过BP 神经网络处理后的信号进行独立成分分析（Independent Component Analysis, ICA）;然后以独立成分分析后的信号的负熵作为适应度函数,采用遗传算法对BP神经网络的参数进行寻优;最后利用优化的BP神经网络参数,对观测到的混合信号进行分解,分离出纯净的振源信号。与采用粒子群优化（Particle Swarm Optimization, PSO）算法的核独立成分分析（Kernel ICA, KICA）相比,该方法提取的分离信号具有更高的精度,为齿轮箱复合故障诊断提供了关键技术与有效方法。     

改进的共振稀疏分解方法及其在滚动轴承复合故障诊断中的应用

滚动轴承复合故障信号中各故障特征受到传输路径和其他干扰源的影响,在多缺陷共存条件下提取单个缺陷诱发的故障特征存在困难。提出一种基于双参数优化、子带重构改进的共振稀疏分解(RSSD)滚动轴承复合故障诊断方法：首先利用人工鱼群算法自适应选择RSSD的品质因子和分解层数以构造与故障特征匹配的最优小波基,获得包含瞬态冲击的低共振分量;然后依据提出的子带筛选准则选择并重构低共振分量中包含瞬态冲击成分的最佳子带;最后通过多点最优最小熵反卷积（MOMEDA）方法识别并提取重构信号中周期性故障冲击。仿真信号和轴承全寿命周期复合故障信号分析结果表明,与RSSD-MCKD方法相比,所提出方法能有效提取复合故障信号中各故障特征,精确实现轴承复合故障诊断。     

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。     

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 .