基于SVD优化EMD的电梯导靴振动信号故障 特征提取

针对电梯导靴振动信号采用经验模态分解(Empirical Mode Decomposition,EMD)难以直接提取早期微弱故障特征的问题,提出基于奇异值分解(Singular Value Decomposition,SVD)优化经验模态分解的电梯导靴振动信号故障特征提取方法。该方法首先对原始信号进行SVD分解,通过奇异值贡献率原则来确定相空间重组的最佳Hankel矩阵结构,利用曲率谱原则与奇异值贡献率原则相结合来确定有效奇异值的阶次;筛选出包含主要故障信息的奇异值进行信号重构,得到剔除噪声信号与光滑信号的突变信号;然后对突变信号进行EMD分解,得到信号的本征模态函数(Intrinsic Mode Function,IMF)分量。最后,对IMF分量作Hilbert变换,求得其Hilbert边际谱,从而获得电梯导靴故障特征频率信息。仿真结果表明该方法有效改善了EMD难以直接提取早期微弱故障特征的问题,更准确地提取了振动信号的故障特征频率,验证了所述方法的有效性。     

一种旋转机械振动信号的有效消噪方法

提出了一种基于奇异值分解(SVD)、Mallat算法和经验模态分解的信号降噪方法.首先,采用香农熵判据寻求最佳小波分解,对带噪部分小波系数进行经验模态分解,提取出信号趋势分量;其次对小波系数剩余部分采用奇异值分解方法降噪,并根据奇异值差分谱自适应的选择奇异值进行重构,将重构后的信号和趋势项叠加作为新的小波系数;最后进行小波重构得到最终的消噪信号.运用模拟信号和齿轮箱断齿故障信号进行仿真,结果表明该方法能够准确地选择用于重构的奇异值个数,并能有效去除信号噪声,保留特征信号的细节信息,尤其对含有趋势项的故障特征有很大实用性.     

基于IMF奇异值熵和t-SNE的滚动轴承故障识别

针对滚动轴承振动信号非线性、非平稳性以及故障难以识别的问题,提出了一种经验小波变换(EWT)、奇异值熵和t分布随机领域嵌入(t-SNE)相结合的滚动轴承故障识别方法.对原始振动信号进行EWT分解得到若干固有模态分量(IMF),对IMF进行奇异值分解求取奇异值熵.利用t-SNE算法对奇异值熵组成的特征矩阵进行降维,所提取...     

基于内禀模态奇异值分解和支持向量机的故障诊断方法

提出了一种基于内禀模态(Intrinsic mode functions,简称IMFs)奇异值分解和支持向量机(Support vector machine,简称SVM)的故障诊断方法.采用经验模态分解(Empirical mode decomposition,简称EMD)方法对旋转机械故障振动信号进行分解,将得到的若干个内禀模态分量自动形成初始特征向量矩阵,然后对该矩阵进行奇异值分解,提取其奇异值作为故障特征向量,并进一步根据支持向量机分类器的输出结果来判断旋转机械的工作状态和故障类型.对齿轮振动信号的分析结果表明,即使在小样本情况下,基于内禀模态奇异值分解和支持向量机的故障诊断方法仍能有效地识别齿轮的工作状态和故障类型.     

基于EMMD和AR奇异值熵的故障特征提取方法研究

提出了一种基于EMMD(extremum field mean mode decomposition)和AR(auto-regressive)奇异值熵的故障特征提取方法。该方法在对故障信号的EMMD分解基础上,选取有限个固有模态函数(IMF,intrinsic mode function)的AR模型参数向量作为故障的初始特征向量矩阵,对初始特征向量矩阵求取奇异值熵,通过奇异值熵的大小表征故障类型。对转子故障数据的分析结果表明该方法能够有效地应用于非线性和非平稳故障信号的特征提取。     

基于二次奇异值分解和最小二乘支持向量机的轴承故障诊断方法

为了解决奇异值分解(SVD)对不同信号分解的有效奇异值个数不同,而影响故障识别准确性的难题,提出了基于二次SVD和最小二乘支持向量机(LS-SVM)的故障诊断方法。该方法利用奇异值曲率谱自适应选择有效奇异值重构信号,进行二次SVD处理,获得相同个数的正交分量,求解其能量熵,并构造故障特征向量,用于LS-SVM分类模型故障识别。将该方法应用于轴承故障诊断,与利用特定个数的主奇异值作为特征向量的方法相比,准确度提高了13.34%,表明了该方法的可行性和有效性。     

基于奇异值分解和小波包分析的液压泵振动信号特征提取方法

针对液压泵故障特征提取问题,提出了一种基于奇异值分解和小波包变换的液压泵振动信号特征提取方法.通过奇异值分解将噪声非均匀分布的液压泵振动信号正交分解为噪声分布相对均匀的分量,对各分量进行小波包阈值去噪,重构去噪后分量,对去噪后信号进行小波包分解,提取各频带能量特征.以齿轮泵为例,将该方法对齿轮泵的气穴故障、齿轮磨损和侧板磨损3种常见故障和正常状态的振动信号进行特征提取分析,结果表明,该方法可有效提取齿轮泵故障特征.     

基于EMD和神经网络的变速箱状态识别研究

针对变速箱故障信号的非平稳和时变特点,提出了EMD和奇异值相结合的变速箱故障诊断方法;以变速箱箱体振动信号作为分析对象,首先对信号进行EMD分解,提取包含主要信息成分的IMF分量构成特征向量矩阵,计算其奇异值和奇异值熵,分别作为特征向量,通过神经网络和K近邻法判别变速箱的工作状态;在某型装甲车辆的实车测试中,以奇异值作为神经网络的输入特征向量和以奇异值熵作为K近邻法的特征向量均取得了较好的识别效果.     

基于S变换特征提取和隐马尔科夫模型的故障诊断方法研究

轴承的故障信号特征提取和故障的识别在机械化生产中具有重要的意义,对此提出了基于S变换特征提取和隐马尔科夫模型的故障诊断方法。为了获取所需的故障特征信息,首先对采集到的轴承信号进行S变换,并对变换结果进行奇异值分解,提取信号特征。将获取到的奇异值构造成信号特征矩阵,用于建立隐马尔科夫的故障识别模型。试验的结果证明了本文的方法在轴承的故障检测中的有效性。     

基于EMD和奇异值分解技术的滚动轴承故障诊断方法

提出了基于EMD(Empirical mode decomposition)和奇异值分解技术的滚动轴承故障诊断方法。采用EMD方法将滚动轴承振动信号分解成若干个基本模式分量(Intrinsic mode function，IMF)之和，并形成初始特征向量矩阵。然后对初始特征向量矩阵进行奇异值分解得到矩阵的奇异值，将其作为滚动轴承振动信号的状态特征向量，通过建立Mahalanobis距离判剐函数判断滚动轴承的工作状态和故障类型。实验数据的分析结果表明，本文方法能有效地应用于滚动轴承故障诊断。     

A robust fault diagnosis scheme based on signal modal estimation

A novel fault detection/diagnosis technique for linear dynamic systems is proposed. In comparison with existing schemes, the proposed method achieves fault detection/diagnosis using neither observer residuals nor parameter estimation errors; instead, it relies on the estimation of the underlying modal parameters of the system. The estimated modal parameters are compared with pre-calculated characteristic patterns of the system, which are represented as a set of root loci in terms of the physical system parameters. The modal parameter estimation is carried out using a numerically robust least-squares algorithm based on singular value decomposition. A pattern recognition technique employing linear multiprototype distance functions is used to classify the faults according to the variations of physical parameters. The proposed method has been applied to a simulated DC servo system where faults are introduced as abrupt changes in physical system parameters. It is shown that the proposed scheme is capable of diagnosing most of changes in physical system parameters.     

Real-time SVD-based detection of multiple combined faults in induction motors

Early detection of induction motor faults has been a main subject of investigation for many years. Several approaches have been proposed for identifying one or more faults treated in an isolated way. Multiple combined faults on induction motors represent a big challenge since the reliable diagnosis of a faulty condition under the presence of two or more simultaneous faults is really difficult. This work introduces a novel methodology that merges singular value decomposition, statistical analysis, and artificial neural networks for multiple combined fault identification. Obtained results demonstrate its high effectiveness on detecting faulty bearings, unbalance, broken rotor bars, and all their possible combinations. The developed field programmable gate array-based implementation offers a portable low-cost solution for online classification of the rotating machine condition in real time. Thanks to its generalized nature, the introduced approach can be extended for detecting multiple combined faults under different working conditions by a proper calibration.     

Structured singular values with nondiagonal structures. II.Computation

In part I we introduced a robust stability measure-termed generalized structured singular value. In part II we address computational issues pertaining to this notion. Our main contribution is a computational method which would render the computation of the generalized structured singular value both more efficient and potentially more accurate. As in the computation of the usual structured singular value, the key in our method is to compute an upper norm bound scaled via a similarity transformation. It is shown that this bound is as tight as those obtained elsewhere and that it can be computed considerably more efficiently. Furthermore, it is shown that this bound is actually equal to the generalized structured singular value when the uncertainty has four blocks     

基于最优解耦的关联大系统的故障检测

对于关联大系统的故障检测,通常的做法是通过进行观测器的特征结构配置使残差信号与动态关联项完全解耦,这种方法具有很大的局限性.本文将对这一问题作进一步扩展,当系统模型不能满足残差与关联项解耦时,运用最优解耦理论,通过矩阵的奇异值分解实现最优解耦,从而保证故障的检测效果.     

A distribution independent data-driven design scheme of optimal dynamic fault detection systems

In this paper, design issues of data-driven optimal dynamic fault detection systems for stochastic linear discrete-time processes are addressed without precise distribution knowledge of unknown inputs and faults. Concerning a family of faults with different distribution profiles in mean and covariance matrix, we introduce a bank of parameter vectors of parity space and construct the parity relation based residual generators using process input and output data. In the context of minimizing the missed detection rate for a prescribed false alarm rate, the design of fault detection system is formulated as a bank of distribution independent optimization problems without posing specific distribution assumption on unknown inputs and faults. It is proven that the optimal selection of individual parameter vector can be formulated as a generalized eigenvalue–eigenvector problem in terms of the means and covariance matrices of residuals in fault-free and each faulty cases, and is thus solved via singular value decomposition. The tight upper bounds of false alarm rate and missed detection rate are simultaneously achieved quantitatively. Besides, the existence condition of the optimal solutions is investigated analytically. Experimental study on a three-tank system illustrates the application of the proposed scheme.     

Finite frequency fault detection for T-S fuzzy singular multiple timedelay systems

This paper focuses on the problem of fault detection (FD) for a class of nonlinear systems described by the T-S fuzzy singular model with multiple time delays and actuator faults. Two finite-frequency performance indices are introduced to measure fault sensitivity and disturbance robustness. To reduce the conservatism of the existing results, a finite frequency domain approach to fuzzy singular multiple time-delay systems is proposed. Then based on the approach, filter design conditions for the solvability of this problem are presented in terms of linear matrix inequalities (LMIs). Finally, simulation studies are provided to demonstrate the application of the proposed method.     

基于奇异值分解的异常切片挖掘

切片操作是联机分析处理的主要功能之一,在决策支持应用中发挥着重要作用.由于人工的切片过程非常低效,且易忽略重要信息,提出了一种自动、智能的异常切片挖掘方法.该方法基于奇异值分解技术来提取切片的数据分布特征,然后在提取出的奇异值特征之上,利用基于距离的孤立点检测技术发现异常的切片.在人工生成的数据和实际应用的切片数据上所作的实验结果都表明了该方法的高效性和可行性.     

利用SVD对带噪声的模糊图像进行盲复原

介绍了具有非负和有限支撑约束的递归逆滤波器盲图像复原算法。在此基础上，利用分块奇异值分解和压缩技术，提出一种去噪声方法，使得可以复原被噪声污染的具有全黑、全白或全灰的背景和有限支撑的目标图像。计算机仿真结果表明，新的方法具有更好的图像复原性能。     

Rapid distributed model predictive control design using singular value decomposition for linear systems

The issue of model predictive control design of distribution systems using a popular singular value decomposition (SVD) technique is addressed. Namely, projection to a set of conjugate structure is dealt with in this paper. The structure of the resulting predictive model is decomposed into small sets of subsystems. The optimal inputs can be separately designed at each subsystem in parallel without any interaction problems. The optimal inputs can be directly obtained and the communication among the subsystems can be significantly reduced. In addition, the design of distribution model predictive control (DMPC) with constraints using the SVD framework is also presented. The unconstraint inputs are checked in parallel in the conjugate space. Without solving the QP problem of each subsystem, the suboptimal solution can be quickly obtained by selecting the bigger singular values and discarding the small singular values in the singular value space. The convergence condition of the proposed algorithm is also proved. Two case studies are used to illustrate the distribution control systems using the suggested approach. Comparisons between the centralized model predictive control method and the proposed DMPC method are carried out to show the advantages of the newly proposed method.