改进型盲源分离在结构模态识别中的应用

提出一种基于量子遗传算法的二阶盲源分离识别结构模态参数的新方法,首先,对测量信号进行Hilbert变换构建分析信号;然后,采用量子遗传算法求解最优时延,利用最优时延的二阶协方差矩阵同时对角化进行信号分离,得到的混合矩阵作为模态振型;最后,对单自由度模态响应提取模态频率和阻尼比.钢框架结构实验结果表明,该方法不仅适用于实模态情况,同时适用于复模态情况,且计算简单、识别精度高.     

基于滑动窗邻域保留投影的工作模态分析

为了识别线性慢时变结构的工作模态参数,提出一种基于滑动窗邻域保留投影(MWNPE)的工作模态参数识别方法。该方法基于“时间冻结”理论,利用固定长度的窗口,将每个窗口内的非平稳信号看作平稳的随机序列,从而将线性时变结构离散成有限个线性时不变结构。在每个窗口内,利用邻域保留投影算法寻找窗口内位移响应数据的低维嵌入,低维嵌入与模态坐标响应矩阵相对应;再利用单自由度识别技术从模态响应矩阵中识别出窗口的模态固有频率;最后,利用最小二乘广义逆估计出变换矩阵,变换矩阵与模态振型矩阵相对应。通过质量慢时变三自由度(DOF)的仿真结构验证表明,所提方法能有效识别出线性慢时变结构的工作模态参数,且识别精度优于滑动窗主成分分析方法和滑动窗等变自适应源分离(EASI)方法。     

基于邻域保留投影的工作模态参数识别

针对拉普拉斯特征映射和等距离映射算法识别弱非线性特征模态精度低的缺点，提出一种利用邻域保留投影算法的工作模态参数识别方法。该方法利用局部线性特征寻找结构位移响应数据的低维嵌入数据，低维嵌入数据与模态坐标响应矩阵相对应；利用单自由度识别技术从模态响应矩阵中识别出结构的模态固有频率；再用最小二乘广义逆估计变换矩阵，变换矩阵与模态振型矩阵相对应。该方法能够保留数据的局部线性特征，从而识别弱非线性模态。通过三维圆柱壳仿真数据集的识别结果表明，相比拉普拉斯特征映射和等距离映射算法，邻域保留投影算法能够更有效地识别出弱非线性特征模态的参数，平均识别精度更高。     

基于拉普拉斯特征映射的三维结构模态分析

为了仅从平稳振动响应信号中识别线性时不变三维结构的工作模态参数,提出一种基于拉普拉斯特征映射的三维结构模态分析方法.该方法首先将复杂三维结构的振动响应数据视作处于高维空间的数据集,利用拉普拉斯特征映射寻找该数据集的低维嵌入数据.低维嵌入数据对应模态响应矩阵,利用单自由度识别技术从模态响应矩阵中识别出模态固有频率.最后,...     

环境风激励下中信广场的模态参数识别

为了准确地识别建筑结构的模态参数,提出了一种基于多重信号分类算法(multiple signal classification,简称MUSIC)、经验小波变换(empirical wavelet transform,简称EWT)和同步提取小波变换(synchroextracting transform,简称SET)的结构模态参数识别方法。首先,通过MUSIC-EWT对实测振动信号进行分解;其次,使用SET对单模态信号进行去噪处理;然后,采用自然环境激励技术(natural excitation technique,简称NExT)得到单模态信号的自由衰减响应;最后,利用Hilbert变换(hilbert transform,简称HT)和曲线拟合获得结构的自振频率和阻尼比。通过三层框架结构的数值模拟验证了该方法的准确性和鲁棒性。利用该方法对台风"达维"作用下广州中信广场的实测加速度数据进行分析,并将估计的结构模态参数和其他识别方法的分析结果进行对比,进一步证明了该方法的准确性和鲁棒性。     

一种模态弱响应且模态密集的参数识别方法

针对运行模态分析中响应信号数据样本较短、模态密集,以及弱响应信号淹没在大噪声中,系统模态难以全部辨识的问题,提出了联合相关函数与传递率识别系统模态参数的方法(联合方法),该方法先应用传递率近似频响函数获取系统弱响应频率特征函数,然后再通过小波变换进行模态识别。运用随机激励下的GARTEUR飞机模型仿真运行状态的输出进行了数值仿真实验。结果表明:相比于多参考最小二乘复频域法,联合方法不仅能提高模态频率的识别精度,而且还能极大地提高阻尼比的识别精度,尤其是传递率对模态密集的弱响应模态识别结果良好。     

环境激励下高桩码头物理模型模态实验

建立高桩码头物理模型,进行环境激励下的模态实验研究.利用特征系统实现算法(eigensystem realizationalgorithm,简称ERA),结合自然激励技术(natural excitation technique,简称NExT),即NExT-ERA模态识别方法编写模态识别程序来识别物理模型的模态参数,并与有限元模型的计算结果进行对比分析.研究结果表明,物理模型实验值与有限元计算结果相比,二者数值接近,误差很小,说明NExT-ERA模态识别方法能够应用于环境激励下高桩码头的模态识别.环境载荷激励下结构的动力响应信号较弱,结构的高阶模态一般无法激出,某些数据识别出的模态参数存在“漏阶”现象,因此结合有限元模型进行分析非常必要.     

基于欠定盲源分离的结构模态参数识别

针对欠定情况下传统盲源分离(blind source separation,简称BSS)算法无法有效识别结构模态参数的问题,研究了一种不受传感器数量限制的BSS算法。算法主要分为振型矩阵估计与单模态信号分离两步。首先,利用各阶模态响应信号在时频域中的聚类特性估计结构的模态振型;然后,在已知振型矩阵的基础上,通过L1范数最小化算法分离出多个单模态信号;最后,利用单模态参数识别方法提取各阶模态的频率与阻尼比。经仿真与实验验证,本研究方法可以准确识别出结构的各阶模态参数,同时对测量噪声不敏感,具有很好的噪声鲁棒性,在工程实践中具有一定的应用价值。     

基于稀疏成分分析的欠定工作模态参数识别

针对基于独立成分分析的工作模态参数识别方法可识别模态数较少的问题,提出一种基于稀疏成分分析的欠定工作模态参数识别方法.该方法从线性时不变小阻尼结构的模态振型、模态坐标的性质和稀疏成分分析的基本假设出发,找出模态振型与混合矩阵之间、模态坐标响应与稀疏成分之间的一一对应关系,将欠定工作模态参数识别问题转化为稀疏成分分析问题.首先,从基于传统盲源分离的工作模态参数识别中存在的问题和稀疏成分分析流程,建立了基于稀疏成分分析的工作模态参数识别框架和流程步骤;其次,针对欠定工作模态参数识别振型的解释和评价问题,提出欠定条件下识别振型的特点及评价方法;最后,讨论了可识别模态数、模态遗漏、虚假模态及方法的适用范围,并与独立成分分析方法进行了理论比较.通过5自由度仿真数据集下的工作模态参数识别,表明所提方法具有有效性和优越性.     

基于ICA独立分量分析的模态参数识别研究

阐述了模态识别原理和独立分量分析原理,说明了ICA混叠矩阵与模态频率之间的对应关系,将信号处理技术应用于机械系统动力学问题中。对XXX型弹体涂镀层厚度检测仪机架进行试验模态分析,对获取的振动信号采用不同的算法提取出机架的固有频率并进行对比,结果证明,基于ICA方法的模态参数识别方法可以得到较好的识别。获得的模态参数结果对于降低检测仪振动、保证检测仪稳定运行具有重要价值。研究结果对于仅可测得响应的机械系统模态参数识别问题,具有一定理论和实际意义。     

The identification of a dam's modal parameters under random support excitation based on the Hankel matrix joint approximate diagonalization technique

Modal analysis is an effective method for monitoring a dam's health. Modal parameters can be identified from the measured vibration response of a dam under ambient excitation, such as that from an earthquake. In this paper, we first use the state space model to analyze the vibration of a dam under ambient support excitation and conclude that the nature excitation technique (NExT) can be used to the measured absolute acceleration response of the dam under band-limited stochastic support excitation to obtain its impulse response. To overcome some of the limitations of the traditional modal identification method for a structure under ambient excitation, we propose a modal parameter identification method based on the Hankel matrix joint approximate diagonalization (HJAD) technique. In this method, the Hankel matrix is defined as the covariance matrix of a vector, which is composed of measured acceleration responses, and their time-lagged data. This modal parameter identification method can be regarded as an improvement to the traditional time domain method because it introduces the joint approximate diagonalization (JAD) technique into the original method. On the other hand, the method can be regarded as an improvement to the SOBI-based modal identification method because it uses the Hankel matrix instead of the covariance matrix of response to perform the JAD. Therefore, this method combines the advantages of the two existing modal identification methods and overcomes some of their limitations. Compared with the SOBI-based modal parameter identification method, the implementation of the method presented in this paper is very convenient because we need only to add time-lagged response data to the analysis. The numerical analysis results show that the proposed modal parameter identification method based on HJAD technology not only has the advantage of a traditional blind modal parameter identification algorithm, but can also overcome the limitation of not being able to estimate more active modes than the number of available sensors. According to the satisfactory performance of this method in the analysis of strong-motion earthquake observation data for a gravity dam, the modal parameter identification method proposed in this paper has the potential for application in water conservancy engineering.     

基于小波半软阈值消噪的盲源分离方法

为了有效提取含噪机械故障信号中的故障特征信息,研究了一种基于小波半软阈值消噪的盲源分离方法。利用小波半软阈值对故障信号进行消噪处理;采用联合近似对角化算法对信号进行盲源分离;考虑在噪声干扰下预消噪常常不足以消除全部噪声,因此在盲源分离后再进行适当的消噪处理,以提高其分离性能。实验验证了所提出方法的有效性和可行性。     

Model order determination and noise removal for modal parameter estimation

Given a noisy impulsive response function (IRF) that has been contributed by an unknown number of modes, this article proposes a different approach from the traditional methods for estimating modal parameters from this noisy IRF. The major difference lies in the way of handling noise and choosing the computational model order. Whereas the traditional approach accommodates noise by purposely increasing the computational model order, the proposed approach uses the actual system order as the computational model order and rejects noise prior to performing the modal parameter estimation. The proposed approach includes three steps: (1) model order (or number of modes) determination from the measured IRF—by finding the rank of a Hankel matrix constructed from the measured IRF, (2) noise removal from the measured IRF to obtain a filtered IRF—by implementing Cadzow's algorithm for the structured low rank approximation (SLRA) on the Hankel matrix, and (3) modal parameters estimation from the filtered IRF—by using the complex exponential method (Prony's method). Numerical studies include both synthesized and experimental data. While measured IRFs with mild and strong noise levels are simulated for a 5 degree-of-freedom mass-spring-dashpot system, the modal parameter estimations based on the filtered IRFs are very good for both noise levels. While experimental data are measured from two accelerometers mounted at a cantilever beam, the modal parameters estimated from the filtered IRFs of the two accelerometers are in excellent agreement.     

An improved stochastic subspace identification for operational modal analysis

An improved stochastic subspace identification algorithm is introduced to solve the low computational efficiency problem of the Data-driven stochastic subspace identification. Compared with the conventional algorithm, it needs much less cost of memory and computing time because it does not have a process of the QR decomposition of Hankel matrix. Model similarity index is proposed to measure the reliability of the modes obtained by the improved stochastic subspace identification. Furthermore, the stabilization diagram in combination with the modal similarity index is adopted to effectively indicate spurious modes resulting from noise and model redundancy. A criterion named the modal norm is introduced to indicate the dominating mode. A numerical example on the parameter estimation of a linear time-invariant system of 7 degrees of freedom and one experimental example on the parameter estimation of Chaotianmen bridge model in Chongqing are presented to demonstrate the efficacy of the method.     

基于EEMD的单通道盲源分离在轴承故障诊断中的应用

针对一维观测矩阵的极度欠定盲分离模型,结合盲源分离和总体经验模式分解的优点,利用总体经验模式分解将单通道信号转化为固有模态矩阵,重组观测矩阵,再通过近似联合对角化实现信号的盲分离。数据仿真说明该方法能提取低信噪比下的轴承故障信息。实验中,对2种不同故障的轴承进行故障诊断,从而进一步证明了该方法的有效性。     

V型双缸柴油机噪声源及其激励源响应的识别分析

采用基于四阶累积量的JWSmICA技术,对V型双缸柴油机噪声源及其激励源进行识别。该技术首先在不同工况下利用单缸熄火法对机械噪声和燃烧噪声进行了分离与识别,得到了柴油机主要噪声源,然后把基于四阶累积量的联合近似对角化算法(JADE)及小波时频分析结合起来,对标定工况下噪声源的主要激励源响应进行了分离与识别,结合单缸熄火与ICA识别结果,找到了以气门落座冲击和活塞敲击为主的主要机械激励源与燃烧激励源。利用该技术分析得出了降低燃烧激励源响应的中低频振动能量是控制噪声源关键的结论。     

A framework for blind modal identification using joint approximate diagonalization

In this paper, a second-order statistical method employed in blind source separation (BSS) is adapted for use in modal parameter identification. Modal responses and mode shapes are estimated by the use of second-order blind identification (SOBI) on an expanded and pre-treated dataset. Frequency and damping can be obtained from the modal responses by simple single degree of freedom methods. Using this approach, a class of new non-parametric output-only modal identification algorithms is proposed and examples of its use are provided. It is demonstrated that the proposed methodology provides a novel and robust approach to modal identification. For the example shown, it is deduced that quality of the modal parameters produced by the method is competitive with the state of the art parametric methods.     

基于改进自适应遗传算法的固定结合面动态特性参数优化识别

针对固定结合面传统建模方法精度较低和结合面的动态特性参数难以确定的问题,改进了基于弹簧阻尼单元的建模方法,提出了基于实验模态分析和改进自适应遗传算法的固定结合面动态特性参数的优化识别方法。该方法以有限元计算的理论固有频率和阻尼比与其对应实验模态分析结果的相对误差最小为目标函数,使用改进的自适应遗传算法优化识别固定结合面的刚度和阻尼参数。以自行设计制作的固定结合面模型为研究对象进行了建模、实验、参数识别等分析,分析结果表明：所提出的方法是正确的、有效的,参数识别误差在5%以内,达到了较高的识别精度。     

USING CROSS—CORRELATION THEORY TO EXTRACT MODAL PARAMETERS IN FREQUENCY—DOMAIN

Conventional modal parameter identifications are usually based on frequency response functions, which require measurements of both the input force and the resulting response. However, in many cases, only response data are available while the actual excitations (such as wind/wave load) are not measurable. Modal parameters estimation must base itself on response-only data. Over the past years, many time-domain modal parameter identification techniques from output-only are proposed. A poly-reference frequency-domain modal identification scheme on response-only is presented. It is based on coupling the cross-correlation theory with conventional frequency-domain modal parameter extraction. An experiment using an airplane model is performed to verify the proposed method.     

Measurement and correlation of high frequency behaviors of a very flexible beam undergoing large deformation

A correlation method of high frequency behaviors of a very flexible beam undergoing large displacement is presented. The suggested method based on the experimental modal analysis leads to more accurate correlation results because it directly uses the modal parameters of each mode achieved from experiment. First, the modal testing and the parameter identification method are suggested for flexible multibody dynamics. Due to the flexibility of a very thin beam, traditional testing methods such as impact hammer or contact type accelerometer are not working well. The suggested measurement with high speed camera, even though the test beam is very flexible, is working well. Using measurements with a high speed camera, modal properties until the 5th mode are measured. And After measuring each damping ratio until the 5th mode, a generic damping model is constructed using inverse modal transformation technique. It’s very interesting that the modal transformation technique can be also applied even in the ANCF simulation which uses the global displacement and finite slope as the nodal coordinates. The results of experiment and simulation are compared until the 5th mode frequency, respectively, by using ANCF forced vibration analysis. Through comparison between numerical simulation and experiment, this study showed that the proposed generic damping matrix, modal testing and parameter identification method is very proper in flexible multibody dynamic problems undergoing large deformation.