无转速计的旋转机械Vold-Kalman阶比跟踪研究

结合旋转机械升降速阶段振动信号的特点,提出了一种无转速计的旋转机械Vold-Kalman阶比跟踪方法。该方法利用能量重心法对振动信号进行频谱校正,估计瞬时频率,获得参考轴转速信号,再对振动信号进行Vold-Kalman阶比跟踪,提取阶比分量。与需要转速计的经典Vold-Kalman阶比跟踪方法相比,该方法无需鉴相装置,完全用软件方式实现,算法精度高。仿真和应用实例分析结果表明此方法能够在时域中准确地提取幅值和频率变化的阶比分量。     

旋转机械阶比跟踪中的阶比交叠噪声消除

旋转机械的升/降速过程的阶比分析中测试数据容易受到阶比交叠噪声分量的干扰,使得分析结果失真甚至无意义.提出了对测试数据先用独立分量进行分解,将混合信号中的阶比分量和非阶比交叉噪声分离为不同独立信号分量,在此基础上再对分离出的阶比分量信号对应独立信号分量进行阶比跟踪分析,解决了阶比跟踪分析中的交叠噪声干扰问题.对Gabor阶比跟踪和独立分量分析的基本原理进行了简要介绍,在此基础上提出了本方法的实现方案,并在阶比分析中解决了独立分量分析具有的不确定性问题.通过仿真试验和实际测试对本方法的有效性进行了评价.     

基于广义解调时频分析和瞬时频率计算的阶次谱方法在齿轮故障诊断中的应用

针对齿轮启停过程中故障振动信号的调频特性,提出了基于广义解调时频分析和瞬时频率计算的阶次谱方法,并将其应用于齿轮瞬态信号的分析。广义解调时频分析是一种新的时频分析方法,它可以将多分量的信号分解为若干个瞬时频率具有物理意义的单分量信号,每个单分量信号可以是调幅-调频信号,因此非常适合处理多分量的调幅-调频信号。而当齿轮发生故障时,其启停过程中的振动信号就表现为多分量的调幅-调频特征。在基于广义解调时频分析和瞬时频率计算的阶次谱方法中,首先采用广义解调时频分析方法将齿轮瞬态信号分解为若干个单分量信号,然后计算各个分量的瞬时频率,再对其瞬时频率信号进行重采样,最后对重采样信号进行频谱分析得到阶次谱,从而提取齿轮振动信号的故障特征,判断齿轮的工作状态。仿真信号和实验信号的分析结果表明了该方法的有效性。     

基于广义解调平滑能量分离算法的瞬时频率估计

平滑能量分离算法能够跟踪调幅调频信号的瞬时频率,结合广义解调和复解析Gabor滤波器的优点能够克服平滑能量分离算法只适用于单分量窄带信号以及对噪声敏感的局限性。理论分析了负频干扰对传统广义解调多分量分离方法的影响,在此基础上提出了一种新的基于广义解调的平滑能量分离算法,该方法利用广义解调将非平稳信号转化为准平稳信号,再通过复解析Gabor滤波器对其进行滤波以达到单分量信号分离的效果,分离出来的准平稳信号无需进行逆广义解调,直接采用平滑能量分离算法求取瞬时频率,经过频率补偿得到原始信号的瞬时频率。仿真和试验结果表明该方法能够克服负频率干扰,且比传统方法具有更高的解调精度,进一步扩大了平滑能量分离算法的应用范围。     

基于弗德卡曼阶比跟踪的转子起停车故障特征提取方法及应用

针对传统起停车过程分析采用短时傅里叶变换提取瞬时幅值及相位会损失瞬变信息的不足,用弗德卡曼阶比跟踪原理（Vold-Kalman Filter Based Order Tracking,VKF-OT）结合全息谱原理,提出新的转子起停车故障特征提取方法。由转子起停车瞬态响应数据中提取随转速变化的阶比分量,通过各阶分量复包络直接求幅值、相位,能克服傅里叶变换的平均效应,保留转子振动瞬变信息;通过VKF-OT集成转子截面振动信息,结合全息谱理论绘制阶比全息瀑布图,提取转子起停车状态的故障特征,并用于起停车瞬态动平衡。结果表明,该方法可有效提取转子典型故障特征、降低转子系统一阶临界振动。     

计算阶次分析中避免阶次混叠的滤波定阶方法及其应用

针对计算阶次分析中的阶次混叠现象,分析了各阶次信号分量在频域范围的对应关系,提出并分析了在相同转速区间内信号的各阶次分量会发生频率重叠的问题;在此基础上提出了使用滤波器限制频率、确定阶次的方法,推导了滤波器截止频率选择和滤波后信号角域重采样阶次的确定原则。通过仿真信号分析和实际信号验证,提出的方法成功均能有效的阶次混叠问题,有效确定和降低了角域重采样阶次。     

基于自动搜峰瞬时频率估计的自适应多阶比分析技术研究

提出一种利用自动搜峰的瞬时频率估计技术来实现旋转机械自适应多阶比分析(Adaptive Multiple Order Tracking,AMOT)的新方法。首先,通过时频分析得到振动信号的时频分布,根据频率峰值坐标自动选取搜峰起始点,自适应搜索出不同阶比分量的时频峰值。其次,利用最小二乘法将不同频率分量进行拟合实现瞬时频率估计,然后根据参考分量计算出重采样的鉴相时标对原始信号进行重采样,最后通过FFT变换实现阶比分析。该方法通过瞬时频率估计能够自动识别出所有阶比分量,实现优中选优,避免了传统算法中人为直观选取一个分量进行遮掩滤波提取分量的方法,减少了人为选取分量及起始点造成的误差,具有自适应性。并且无需同步采集转速信号,大大简化了应用条件,同时减少了人为因素,提高了分析精度,为旋转机械故障诊断提供了新方法。仿真实验和应用实例验证了方法的有效性。     

基于跟踪滤波的自适应振动控制

针对频率周期振荡的振动系统讨论自适应控制问题,在频率跟踪的基础上对频率变化的振动响应进行控制,给出相应的估计、滤波与控制方法。首先在子空间辨识原理的基础上,通过测量信号自相关序列的递推运算获得信号频率;然后根据估计频率实时调整带通滤波器的中心频率,使其跟踪信号频率,实现信号分量的跟踪滤波;最后在LMS 方法的基础上构造     

基于故障特征系数模板的变转速滚动轴承故障诊断

与恒转速相比,机械中普遍存在的变转速工作模式使滚动轴承的故障诊断更加困难;另外变转速条件下的常规方法—阶比分析存在误差以及计算效率方面的问题,因此,提出了基于故障特征系数模板的滚动轴承故障诊断方法。该方法主要包括六部分:(1)根据目标轴承的几何参数计算其故障特征系数以设定模板;(2)利用快速谱峭度滤波算法对滚动轴承振动信号进行滤波;(3)根据Hilbert变换以及短时傅里叶变换计算滤波信号的包络时频图;(4)通过峰值搜索算法从滤波信号的包络时频图中提取瞬时故障特征频率趋势线;(5)根据转速脉冲信号计算滚动轴承的转速曲线;(6)瞬时故障特征频率与瞬时转频相比获取瞬时故障特征系数,进而通过故障特征系数模板实现滚动轴承的故障诊断。随即以变转速情况下的故障轴承仿真信号以及实测的外圈故障、内圈故障和健康轴承的振动信号为例验证了该算法的有效性。     

改进的计算阶比跟踪及其应用

针对传统计算阶比跟踪在匀速以及转速变化较小时存在计算奇异的问题,提出了基于三次样条插值的改进计算阶比跟踪。该方法根据累积转角与时间呈单增函数关系,将时间表示为累积转角的函数后,采用三次样条插值方法进行求解,可实现任意工况下等角度重样时间的估计。由仿真信号和实验信号验证了该方法在不同转频变化时的有效性。结果表明:改进方法解决了传统计算阶比跟踪在匀速以及转速变化较小时存在的计算奇异问题,拓展了计算阶比跟踪的应用范围,具有较高的工程应用价值。     

A process monitoring scheme based on independent component analysis and adjusted outliers

Recently, the independent component analysis (ICA) has been widely used for non-Gaussian multivariate process monitoring. An elliptical type measure is traditionally used for ICA-based process monitoring. However, it will not work appropriately since the extracted ICA components exhibit skewed distribution. Thus, this study aims to develop a novel process monitoring scheme for ICA. The basic idea of the proposed method is to first screen out outliers in order to describe well majority for training dataset. Hereafter, a rectangular type measure is applied to monitor the process. The efficiency of proposed monitoring scheme will be implemented via a five variables simulation example and a case study of Tennessee Eastman process. Results indicate that the proposed method cannot only deal with the contaminated training dataset but also shows superior fault detection ability when compared with alternative methods.     

基于Hǒlder指数和ICA的海域SAR图点目标检测

本文将分形Hǒlder指数和信号分离相结合,利用独立成分分析技术（ICA,Independent Component Analysis）,实现了海杂波SAR图的散斑抑制和点目标检测。首先,计算点态Hǒlder指数图,并提出二值模糊方法对其处理;接着使用ICA技术得到该图的基图像和独立成分;提出空间分离法,对独立成分进行分离,同时对基图进行对应分类,获得非噪声和噪声两个空间。最后在非噪声空间上重构图像。实验部分,将该算法与传统算法进行对比,证实了该算法的有效性和优越性。     

State-space independent component analysis for nonlinear dynamic process monitoring

The cost effective benefits of process monitoring will never be over emphasised. Amongst monitoring techniques, the Independent Component Analysis (ICA) is an efficient tool to reveal hidden factors from process measurements, which follow non-Gaussian distributions. Conventionally, most ICA algorithms adopt the Principal Component Analysis (PCA) as a pre-processing tool for dimension reduction and de-correlation before extracting the independent components (ICs). However, due to the static nature of the PCA, such algorithms are not suitable for dynamic process monitoring. The dynamic extension of the ICA (DICA), similar to the dynamic PCA, is able to deal with dynamic processes, however unsatisfactorily. On the other hand, the Canonical Variate Analysis(CVA) is an ideal tool for dynamic process monitoring, however is not sufficient for nonlinear systems where most measurements follow non-Gaussian distributions. To improve the performance of nonlinear dynamic process monitoring, a state space based ICA (SSICA) approach is proposed in this work. Unlike the conventional ICA, the proposed algorithm employs the CVA as a dimension reduction tool to construct a state space, from where statistically independent components are extracted for process monitoring. The proposed SSICA is applied to the Tennessee Eastman Process Plant as a case study. It shows that the new SSICA provides better monitoring performance and detect some faults earlier than other approaches, such as the DICA and the CVA.     

Extraction of mass spectra and chromatographic profiles from overlapping GC/MS signal with background

An adaptive immune algorithm (AIA) was proposed for resolution of the overlapping GC/MS signal with background. By using AIA, the chromatographic profiles corresponding to the independent components (ICs) in the overlapping signal are calculated with the mass spectra extracted by means of independent component analysis (ICA). The number of the ICs in the overlapping signal is determined by the difference between the reconstructed and the original data. Both simulated and experimental data are investigated with the proposed AIA approach. It was found that the mass spectra and chromatographic profiles of the components in an overlapping multicomponent GC/MS signal can be accurately resolved with the existence of background, and the results are better than that by using an interactive self-modeling mixture analysis (SIMPLISMA) method. The AIA approach may be a promising tool for the resolution of overlapping GC/MS signal.     

Unmixing functional magnetic resonance imaging data using matrix factorization

Functional magnetic resonance imaging (fMRI) data is processed by different techniques for detection of activated voxels including principal component analysis (PCA), independent component analysis (ICA), non‐negative matrix factorization (NMF), and so on. In this work, a modified version of NMF method is proposed in which data is not supposed to be non‐negative. The proposed scheme is applied to synthetic fMRI data along with NMF conventional method. The results of the proposed scheme show that it is not only computationally efficient but also has good quality results as compared to that of NMF in terms of average correlation. Finally, proposed method is applied to monkey's fMRI data, and the results are compared with that of NMF and ICA. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 195–199, 2012     

基于H(o)lder指数和ICA的海域SAR图点目标检测

本文将分形H(o)lder指数和信号分离相结合,利用独立成分分析技术(ICA,Independent ComponentAnalysis),实现了海杂波SAR图的散斑抑制和点目标检测.首先,计算点态H(o)lder指数图,并提出二值模糊方法对其处理;接着使用ICA技术得到该图的基图像和独立成分;提出空间分离法,对独立成分进行分离,同时对基图进行对应分类,获得非噪声和噪声两个空间.最后在非噪声空间上重构图像.实验部分,将该算法与传统算法进行对比,证实了该算法的有效性和优越性.     

ICA和ANC技术在声脉冲无损检测中的应用

作为一种无损检测手段，声脉冲检测技术已被广泛应用于产品质量测试中，但是在工农业应用环境下，脉冲信号常常会被周围的噪声所干扰。尝试利用独立成分分析（ICA）和主动噪声控制技术（ANC）来降低这些干扰。在ICA方法中，将被测样本所激发的声脉冲响应、以及干扰噪声作为两个独立成分（IC），进行了分离实验；对于ANC系统，由于待抵消噪声中含有有用信号（声脉冲），所以该系统是一种特殊的、具有信噪比处理增益的有选择性噪声控制系统。文中分别对ICA和ANC技术做了简要介绍，并进行了声学实验，实验表明这两种方法都有较好的应用前景，同时也各有优缺点。     

基于瞬时频率估计的自适应Vold-Kalman阶比跟踪研究

结合旋转机械启停阶段振动信号的特点,提出了一种基于Viterbi算法和短时傅里叶变换(STFT-VA)的瞬时频率估计算法,STFT-VA算法在高噪声、临近阶比和交叉阶比情况下有较高的精度.实现了基于STFT-VA算法的自适应Vold-Kalman阶比跟踪(AVKF-OT),和传统的以硬件实现的Vold-Kalman阶比跟踪(VKF-OT)相比,此方法具有无需转速计等硬件、用纯软件的方法实现.实验结果表明,该方法能够在时域中准确地提取幅值和复杂频率变化的阶比,适合于复杂旋转机械振动响应特征提取.     

A high‐order approach for modelling transient wave propagation problems using the scaled boundary finite element method

A high‐order time‐domain approach for wave propagation in bounded and unbounded domains is proposed. It is based on the scaled boundary FEM, which excels in modelling unbounded domains and singularities. The dynamic stiffness matrices of bounded and unbounded domains are expressed as continued‐fraction expansions, which leads to accurate results with only about three terms per wavelength. An improved continued‐fraction approach for bounded domains is proposed, which yields numerically more robust time‐domain formulations. The coefficient matrices of the corresponding continued‐fraction expansion are determined recursively. The resulting solution is suitable for systems with many DOFs as it converges over the whole frequency range, even for high orders of expansion. A scheme for coupling the proposed improved high‐order time‐domain formulation for bounded domains with a high‐order transmitting boundary suggested previously is also proposed. In the time‐domain, the coupled model corresponds to equations of motion with symmetric, banded and frequency‐independent coefficient matrices, which can be solved efficiently using standard time‐integration schemes. Numerical examples for modal and time‐domain analysis are presented to demonstrate the increased robustness, efficiency and accuracy of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.