移动质量简支梁耦合时变系统建模与实验设计

建立了移动质量简支梁耦合时变系统的动力学模型,通过数值仿真分析了移动质量速度及加速度对耦合时变系统模态参数的影响,得到移动质量诱导产生的附加阻尼。设计并搭建移动质量简支梁实验系统,通过参考实验得到实验系统的初始阻尼,并分别采用频域和时域模态参数辨识方法对质量块不同移动速度下的实验系统进行辨识。结果表明,所建立动力学模型能够对移动质量问题进行准确描述,实验系统可为时变结构动力学分析的理论研究提供实验支持,特别是对时变结构模态参数辨识方法进行实验验证。     

移动最小二乘法的时变结构模态参数辨识

研究时变结构模态参数辨识,基于泛函矢量时变自回归模型(Functional series vector time-dependent AR model,FS-VTAR)提出一种改进的移动最小二乘法的时变结构模态参数辨识方法。该方法源于无网格法中构造形函数进行局部近似的思想,引入带权正交基函数对移动最小二乘(Moving least square,MLS)的基函数进行改进,使得在辨识时间域内构造形函数矩阵过程中不再出现数值条件问题,从而提高了计算精度。把时变系数在形函数上线性展开,利用最小二乘法得到形函数的系数,从而得到时变系数。把时变模型特征方程转换为广义特征值问题提取出模态参数。利用时变刚度系统非平稳振动信号验证该方法,结果表明:改进的移动最小二乘法相比于传统的FS-VTAR模型能有效地避免基函数形式的选择和很高的基函数阶数且更加高效,相比于移动最小二乘法能有效地避免辨识过程中的数值问题,具有更高的模态参数辨识精度。     

用于线性时变系统辨识的固定长度平移窗投影估计递推子空间方法

给出了一个新的用于线性时变参数结构系统模态参数识别的基于固定长度平移窗投影估计的递推子空间方法。首先推得基于平移窗投影估计的子空间跟踪算法,以代替奇异值分解,再推得系统数据矩阵的一阶修正形式,从而得到新的基于平移窗投影估计的递推子空间方法。该方法可有效地降低算法的计算量。最后通过刚度随时间变化的3自由度系统和一具有移动质量的机械臂系统的时变模态频率辨识仿真表明该方法可有效地辨识线性时变系统的伪模态参数。     

一种新的固有频率跟踪及虚假频率剔除方法

针对时变参数辨识中常见的固有频率辨识和虚假频率剔除问题,引入了一种基于新信息准则的子空间跟踪辨识算法,结合实验提出了一种消除虚假固有频率的后处理方法。首先,利用基于新信息准则的子空间跟踪算法辨识出伪时变模态参数;其次,通过聚类方法估计各阶伪固有频率;最后,利用滑动的数据窗比对数据剔除虚假频率。该方法仅需要给出预估的活动模态数即可。不同频率变化形式的仿真算例结果证明了本研究方法较其他辨识算法在信号信噪比较低时具有较高的辨识精度。高温环境下的时变模态实验也验证了该方法的可行性,说明该方法在实际工程中具有较高的应用价值。     

基于时变多变量Prony法的时变振动系统模态参数辨识

在经典的Prony法理论的基础上,提出了可以同时处理多维非平稳信号的时变多变量Prony法,并将其应用于时变多自由度振动系统的模态参数辨识。对传统的递推最小二乘算法加以改进,解决了时变多变量参数模型中时变参数矩阵估计的难题。对时变平面两杆操作臂系统进行仿真和分析,得出了较满意的结果。证明该算法在时变结构模态参数辨识方面,具有有效、准确的计算能力和较强的过程跟踪能力。     

基于模糊优化辨识模块化机器人关节动力学参数

为了能更为合理、准确地辨识出机电结构系统结合面的等效动力学参数,提出了基于模糊优化的辨识方法。以9自由度模块化冗余机器人为研究对象,采用模糊优化方法辨识了机器人关节面等效动力学参数,并利用所识别的参数及机器人各部件有限元模型建立了机器人整机动力学模型,该模型的模态参数与试验结果具有较好的一致性,表明所提出的辨识方法具有较高的辨识精度,是合理和可行的。     

基于最优控制时变非线性参数模糊辨识

运动状态下结构连接处的时变物理参数对研究结构动态特性与工程实际应用具有重要价值,由此提出了一种结构连接处时变非线性物理参数辨识方法。该方法利用每个子结构的动力学模型,将结构连接处的非线性恢复力作为模型的输入,利用最优控制理论与模糊数学的方法辨识出结构连接处的非线性恢复力,并应用子结构法计算出结构连接处的响应,最后利用最小二乘法拟和了非线性恢复力．计算出非线性恢复力的时变系数。仿真算例表明了该方法用来计算连接结构物理参数精度较高。且结果稳定,避免了迭代发散的现象。     

柔性结构的多输入多输出运动系统辨识方法

柔性结构的多输入多输出(Multiple input multiple output,MIMO)运动系统的辨识方法是一个具有理论研究和工程应用价值的问题。随着运动系统结构设计、控制性能等要求不断提高,过去视为刚体的MIMO运动系统的柔性动力学特征将越来越显著,成为限制系统性能的重要因素。在辨识试验获得频域非参数模型基础上,提出一种柔性结构的MIMO运动系统辨识方法,基于正交多项式的总体参数曲线拟合得到同分母的MIMO传递函数矩阵,利用模态叠加原理以及奇异值分解(Singular value decomposition,SVD)原理得到系统的状态空间模型。此方法被应用于光刻机工件台这一典型的带有柔性动力学特征的MIMO运动系统。获得的MIMO状态空间模型具有频域辨识模型同等的辨识精度,证明了提出的辨识方法的有效性。所获得的模型满足用于综合控制设计的要求。     

基于信号时频分析理论识别时变模态参数实验

为研究温度对结构模态参数的影响设计了一套温度可控的实验设备。在这套实验设备提供的可控温度环境中采集悬臂梁结构的加速度响应信号,利用基于信号时频分析的模态参数辨识算法处理实验数据,得到其时变模态参数,包括固有频率和振型,以此研究温度对其模态参数的影响。分析结果显示了基于信号时频分析的模态参数辨识算法在处理非平稳信号以得到结构的时变模态参数上的应用前景,更重要的是实验数据的分析结果较好地反映了温度对结构模态参数的影响,为热环境下结构振动特性分析提供了可靠而且有价值的分析方法和实验依据。     

基于信号时频分析理论识别时变模态参数的实验

为研究温度对结构模态参数的影响设计了一套温度可控的实验设备。在这套实验设备提供的可控温度环境中采集悬臂梁结构的加速度响应信号,利用基于信号时频分析的模态参数辨识算法处理实验数据,得到其时变模态参数,包括固有频率和振型,以此研究温度对其模态参数的影响。分析结果显示了基于信号时频分析的模态参数辨识算法在处理非平稳信号以得到结构的时变模态参数上的应用前景,更重要的是实验数据的分析结果较好地反映了温度对结构模态参数的影响,为热环境下结构振动特性分析提供了可靠而且有价值的分析方法和实验依据。     

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.     

Modal parameter estimation and monitoring for on-line flight flutter analysis

The clearance of the flight envelope of a new airplane by means of flight flutter testing is time consuming and expensive. Most common approach is to track the modal damping ratios during a number of flight conditions, and hence the accuracy of the damping estimates plays a crucial role. However, aircraft manufacturers desire to decrease the flight flutter testing time for practical, safety and economical reasons by evolving from discrete flight test points to a more continuous flight test pattern. Therefore, this paper presents an approach that provides modal parameter estimation and monitoring for an aircraft with a slowly time-varying structural behaviour that will be observed during a faster and more continuous exploration of the flight envelope. The proposed identification approach estimates the modal parameters directly from input/output Fourier data. This avoids the need for an averaging-based pre-processing of the data, which becomes inapplicable in the case that only short data records are measured. Instead of using a Hanning window to reduce effects of leakage, these transient effects are modelled simultaneously with the dynamical behaviour of the airplane. The method is validated for the monitoring of the system poles during flight flutter testing.     

Improved independent component analysis based modal identification of higher damping structures

Structural modal parameter identification under ambient excitation has strong engineering value and theoretical significance. As the most popular tool for solving Blind Source Separation (BSS) problems, Independent Component Analysis (ICA) is able to directly extract the time-domain modal parameters, including frequencies, damping ratios and modal shapes. ICA, however, has a fatal flaw of failing to identify structures with higher damping. To overcome the flaw above, the paper proposes a new method named “ICA + IDT”. Firstly, free vibration response of a structure is obtained from structural outputs under ambient excitation. Inverse damping transfer (IDT) is employed to turn a highly damped signal into a low damping response signal without changing of frequencies and mode shapes. Then, structural modal parameters are extracted from the low damping response signal by ICA. Finally, the identified damping ratios are adjusted to eliminate the impact of IDT. To verify the effectiveness and applicability of IDT + ICA proposed herein, two numerical simulations—mass-spring model and simply supported concrete beam—and an experiment model of three-story steel frame are built, and the analysis results reveal that presented method can identify structures with higher damping effectively.     

Structural identification based on optimally weighted modal residuals

The structural parameter estimation problem based on measured modal data is often formulated as a weighted least-squares problem in which modal residuals measuring the fit between experimental and model predicted modal properties are build up into a single weighted residuals metric using weighting factors. Standard optimisation techniques are then used to find the optimal values of the structural parameters that minimise the weighted residuals metric. Due to model error and measurement noise, the results of the optimisation are affected by the values assumed for the weighting factors. In this work, the parameter estimation problem is first formulated as a multi-objective identification problem for which all Pareto optimal structural parameter values are obtained, corresponding to all possible values of the weights. A Bayesian statistical framework is then used to rationally select the optimal values of the weights based on the measured modal data. It is shown that the optimal weight value for a group of modal properties is asymptotically, for large number of measured data, inversely proportional to the optimal value of the residuals of the modal group. A computationally efficient algorithm is proposed for simultaneously obtaining the optimal weight values and the corresponding optimal values of the structural parameters. The proposed framework is illustrated using simulated data from a multi-dof spring–mass chain structure. In particular, compared to conventional parameter estimation techniques that are based on pre-selected values of the weights, it is demonstrated that the optimal parameter values estimated by the proposed methodology are insensitive to large model errors or bad measured modal data.     

IDENTIFICATION OF TIME-VARYING MODAL PARAMETERS USING LINEAR TIME-FREQUENCY REPRESENTATION

A new method of parameter identification based on linear time-frequency representation andHilbert transform is proposed to identify modal parameters of linear time-varying systems frommeasured vibration responses. Using Gabor expansion and synthesis theory measured responses arerepresented in the time-frequency domain and modal components are reconstructed by time-frequencyfiltering. The Hilbert transform is applied to obtain time histories of the amplitude and phase angle ofeach modal component, from which time-varying frequencies and damping ratios are identified. The