基于递归理论的泵站压力管道运行状态监测

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。     

基于递归理论的泵站压力管道运行状态监测

针对不利因素导致的管道运行异常问题,提出一种基于递归理论的泵站管道运行状态监测方法。首先,通过振动传感器提取压力管道关键部位的实测信息,并将同一位置不同方向的数据信息进行融合,得到一组反映结构整体动力特性的综合数据;其次,利用伪近临法与互信息法分别选取相空间重构参数m和τ;最后,绘制并计算代表管道动力特性的递归图及递归量化指标。将该方法应用于景泰川工程二期七泵站管道运行监测,通过设置不同的运行工况进行验证,结果表明：机组开关瞬间与稳定运行工况下,管道结构振动信号的递归图呈现不同模式,递归量化指标-确定性、对角线平均长度L、递归率及递归熵也呈现明显差异,能有效区分管道振动状态。该方法为压力管道的无损动态监测提供了新思路。     

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。     

Modal identification of linear non-proportionally damped systems by wavelet transform

A time-frequency identification technique based on wavelet transform is formulated and applied to free-decay responses of linear systems with non-proportional viscous damping. The Cauchy mother wavelet is used. Frequencies, modal damping ratios and complex mode shapes are identified from output-only free vibration signals. This identification technique has also shown to be effective when the (non-proportional) damping is significant.     

Output-only modal analysis of wind turbine tower based on vibration response under emergency stop

The identification technique of output-only modal parameters is proposed for the large wind turbine tower under emergency stop. Compared with the response of regular operating conditions, the immediate tower structural response under emergency stop much more resembles a state of free vibration, which is more appropriate for the modal identification of the wind turbine tower. The vibration response is measured in the nacelle, which is easy to perform in the field modal test. The variational mode decomposition (VMD) is applied to decompose the vibration response into several band-limited intrinsic mode functions. The free responses of decomposed functions are extracted by applying the random decrement technique (RDT). Finally, the modal damping ratio and natural frequency are identified from each free modal response by using the Hilbert transform method. Simulations and a 1.5 MW wind turbine field modal test results verify the effectiveness of the proposed identification method. The main modal parameters of wind turbine, including weak modes, are effectively extracted by using output-only vibration responses under emergency stop. The modal parameter identification method is provided for the large wind turbine structure under the engineering condition.     

Time-frequency characterization of nonlinear normal modes and challenges in nonlinearity identification of dynamical systems

Presented here is a new time-frequency signal processing methodology based on Hilbert-Huang transform (HHT) and a new conjugate-pair decomposition (CPD) method for characterization of nonlinear normal modes and parametric identification of nonlinear multiple-degree-of-freedom dynamical systems. Different from short-time Fourier transform and wavelet transform, HHT uses the apparent time scales revealed by the signal's local maxima and minima to sequentially sift components of different time scales. Because HHT does not use pre-determined basis functions and function orthogonality for component extraction, it provides more accurate time-varying amplitudes and frequencies of extracted components for accurate estimation of system characteristics and nonlinearities. CPD uses adaptive local harmonics and function orthogonality to extract and track time-localized nonlinearity-distorted harmonics without the end effect that destroys the accuracy of HHT at the two data ends. For parametric identification, the method only needs to process one steady-state response (a free undamped modal vibration or a steady-state response to a harmonic excitation) and uses amplitude-dependent dynamic characteristics derived from perturbation analysis to determine the type and order of nonlinearity and system parameters. A nonlinear two-degree-of-freedom system is used to illustrate the concepts and characterization of nonlinear normal modes, vibration localization, and nonlinear modal coupling. Numerical simulations show that the proposed method can provide accurate time-frequency characterization of nonlinear normal modes and parametric identification of nonlinear dynamical systems. Moreover, results show that nonlinear modal coupling makes it impossible to decompose a general nonlinear response of a highly nonlinear system into nonlinear normal modes even if nonlinear normal modes exist in the system.     

振动模态固有频率和阻尼比的EMD识别方法

针对机械系统固有频率和阻尼比的识别问题,提出了基于经验模式分解(EMD)的模态参数识别方法.该方法首先对脉冲激励下机械系统的位移响应进行了EMD分解,确定与该系统的各阶模态对应的固有模式函数(IMF),分别对各阶IMF进行希尔伯特变换以得到各自的瞬时幅值和瞬时相位曲线,并对所得曲线进行线性拟合,最后根据拟合曲线的参数来...     

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。     

Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

The problem of linear time-varying(LTV) system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ‘‘frozen-time' assumption are not able to determine the dynamic stability of LTV systems.Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system's stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam experimental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides a new way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.     

Unsupervised identification of arbitrarily-damped structures using time-scale independent component analysis: Part I

In this study, a new method is proposed to identify the dynamic parameters of structures with higher accuracy compared to current methods. First, the wavelet-transformed representation of system responses is extracted from measured responses, and then the independent component analysis is used to achieve the modal characteristics. The simulation results of a multi-degree-of-freedom system illustrate that this method is capable of accurately identifying the modal information of lightly- and highly-damped structures. It is represented that continuous wavelet transform, due to its adaptive time-frequency resolution, is more efficient to be incorporated into independent component analysis compared to Short time Fourier transform (STFT). The latter is unable to accurately determine the modal response, especially at higher frequencies, while the proposed method can identify the system with marked accuracy. The efficiency of proposed method is also investigated under additive noise. Results shown that for highly- and lightly- damped system, the proposed method is able to capture the modal parameters especially in higher frequencies of vibration, along with the modal assurance criterion values with satisfactory accuracy, which indicates the robustness of the procedure compared to other available methodologies.     

Output-only modal identification based on hierarchical Hough transform

Sparse component analysis (SCA) has been introduced to the output-only modal identification for several years. This paper proposes a new method based on hierarchical Hough transform to extract the modal parameters of mechanical structures. First, the measured system responses are transformed to Time-frequency (TF) domain using Short time Fourier transform (STFT) to get a sparse representation. Then, Hough transform is applied to the TF coefficients hierarchically to identify the hyperplanes and the mixing matrix is calculated. Finally, the modal responses are recovered by using l ₁ -optimization and inverse STFT. From the recovered modal responses, natural frequencies and damping ratios are extracted. Numerical simulation of a 4 Degree-of-freedom (DOF) spring-mass system verifies the validity of the method. Free vibration of a steel cantilever beam is captured by a high-speed camera and then analyzed by the proposed method. The comparison of the estimated natural frequencies and damping ratios illustrates the good performance of the proposed algorithm.     

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Hankel矩阵联合近似对角化的结构模态识别

针对基于二阶盲辨识(second order blind identification,简称SOBI)的模态参数识别方法存在的不足,提出了一种基于Hankel矩阵联合近似对角化(Hankel matrix joint approximate diagonalization,简称HJAD)技术的结构运行模态分析(operational modal analysis,简称OMA)的新方法。该方法通过对随机子空间类模态识别方法常用的Hankel矩阵进行联合近似对角化,以分离各阶模态响应,进行模态识别。与基于SOBI的模态识别方法相比,在具体实施过程中,仅需要在分析数据中添加与实测振动响应对应的时间延迟的数据,实现难度较小。数值算例和物理模型试验的分析结果表明,所提出的基于HJAD技术的结构运行模态分析方法,不仅具有鲁棒性强和计算效率高的优点,还可以克服传统的基于SOBI的模态识别方法的模态识别能力受测点数目限制的问题。     

线性时变结构模态参数时域辨识方法的研究进展

随着工程领域的不断拓展,越来越多具有显著时变特性的工程结构进入应用,时变结构动力学问题日渐凸显。从反问题的角度出发,介绍了时变结构动力学问题的研究背景与时变结构模态参数辨识的意义。在对时变结构模态参数辨识方法进行分类的基础上,给出了从参数化时域辨识模型中提取"时间冻结"模态参数的过程。系统全面地总结了时变结构模态参数时域辨识方法的研究进展,归纳了现阶段可用于辨识方法验证的实验系统,指出了该领域目前存在的一些问题以及今后需要进一步研究的主要方向。     

Identification of modal parameters of a time invariant linear system by continuous wavelet transformation

The applications of wavelet transforms have received significant attentions in many fields. This work proposes a procedure for identifying modal parameters of a linear system using the continuous wavelet transform. The merits of the proposed procedure over the exiting schemes of applying the wavelet transform to system identification for a linear system are in use of the time invariance property and filtering ability of the transform to enhance the efficiency of identifying the modal parameters of a structure from its earthquake responses or free vibration responses. The effectiveness and accuracy of the proposed procedure are validated via numerical simulations. The effects of noise and wavelet function on identifying the modal parameters of the structure are also explored in processing the numerically simulated acceleration responses of a six-story shear building subjected to base excitation. The dynamic characteristics of close modes are accurately determined. Finally, the proposed procedure is adopted to obtain the modal parameters of a three-story non-symmetric steel frame from its measured acceleration responses in a shaking table test. A total of nine modes are identified, including modes with high frequencies and very small amplitude.     

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TVAR的自适应时频分析及在故障诊断中的应用

研究了非平稳信号的时变自回归（TVAR）建模方法，通过引入基函数将非平稳时变参数的辨识转化为线性时不变问题的辨识；在此基础上，应用带遗忘因子的递归最小二乘算法进行参数估计，实现了信号的自适应时频分析。通过仿真算例将该法与短时Fourier变换、Wigner分布的结果相比较，验证了该方法时频分辨率高的优越性。最后，将该方法应用于轴承的故障诊断，结果表明，该方法用于故障诊断的特征提取是有效的。     

瞬态成分参数的最小二乘法辨识及其轴承故障特征提取应用

机械系统中轴承出现剥落、裂纹等局部故障,运行时振动信号中出现瞬态冲击响应成分,可通过瞬态成分的检测与提取实现故障特征提取。在瞬态成分建模的基础上,提出基于最小二乘法的瞬态成分参数辨识方法,并将其应用于轴承局部故障时振动信号中瞬态成分特征迭代提取。基于Morlet小波参数化表达式建立双边不对称的瞬态成分模型,应用Levenbery-Marquardt方法辨识模型参数,迭代提取信号中的瞬态成分,并通过Wigner-Ville分布获得瞬态成分高聚集性且瞬态成分之间无交叉项的故障特征时频表示。将基于最小二乘法的瞬态成分参数辨识方法应用于轴承局部故障特征提取,结果表明:该方法能通过参数辨识提取各瞬态成分,瞬态成分时频分布将故障的时频特征以高聚集性且瞬态成分之间无交叉项的形式表示出来,从而有效提取轴承故障特征。     

基于指数窗截取递推最小二乘法的齿轮啮合刚度辨识算法研究

齿轮在机械传动系统中有着广泛应用,由于齿轮啮合过程中参与啮合的轮齿对数周期变化,因此,齿轮啮合刚度为时变参数,在啮合时会产生啮合振动。当齿轮副出现齿根裂纹时,啮合刚度会减小,齿轮啮合产生的系统振动响应也发生改变,通过振动响应辨识齿轮啮合刚度能够监测齿轮副的健康状态。针对齿轮啮合刚度的时变特征,提出了基于指数窗截取递推最小二乘(Exponential window recursive least square,EWRLS)算法和振动信号瞬时频率的齿轮啮合刚度辨识方法。进行啮合刚度辨识时,EWRLS算法将输入、输出齿轮的转速曲线分别作为辨识输入信号和观测信号,使用指数窗函数进行数据截断,使用递推最小二乘算法估计系统参数。为了计算输入、输出齿轮的转速曲线,使用经验模态分解(Empirical mode decomposition,EMD)方法将振动信号分解为具有不同变化频率的本征模态函数(Intrinsic mode function,IMF),并根据IMF的平均频率重构输入、输出齿轮的特征信号。通过Hilbert变换计算特征信号的瞬时频率曲线,从而获得各齿轮的转速曲线。使用仿真和实测信号对算法进行验证,结果表明,EWRLS算法能够辨识齿轮副的时变啮合刚度。     

基于Hilbert-Huang变换与理想带通滤波器的系统识别

针对经验模态分解存在模态混叠现象,提出基于Hilbert-Huang变换与理想带通滤波器的系统识别方法。该方法利用傅里叶变换得到结构加速度响应频响函数,粗略估计固有频率范围,通过半功率带宽法设计理想带通滤波器,定量化确定通带带宽,使信号在经过滤波器后频域内零相移,同时不改变其幅值谱。结构响应通过指定频带的理想带通滤波器产生若干窄带信号,利用经验模态分解获取结构模态响应,经Hilbert变换构造模态响应解析信号,并通过线性最小二乘拟合提取结构模态参数与物理参数。结果表明:半功率带宽法可实现带通滤波器频带的定量化设计,理想带通滤波器的零相移特点较好契合Hilbert-Huang变换用于系统识别的要求,两者结合可有效地解决模态混叠现象,减少虚假模态,大大提高结构系统识别精度。