Operational modal identification of suspension bridge based on structural health monitoring system

An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.

Modal Analysis Using Operating Modal Identification Method

Sometimes it is very difficult for some large-scale operating structures tomeasure the input forces. Modal parameters must be estimated on response-only. A poly-referencetime-domain operating modal identification complex exponential method is presented sincecross-correlation functions have the same form as impulse response functions. Then a poly-referencefrequency-domain operating modal identification method is proposed in this paper. An experiment onan aircraft model is performed to verify the proposed schemes. The results show that both outlinedschemes can extract the parameters from output-only and the modal parameters extracted by proposedfrequency-domain method are more accurate than those by presented time-domain complex exponentialmethod.

An operational modal analysis method in frequency and spatial domain

A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

System Identification of Heritage Structures Through AVT and OMA:A Review

In this review article,the past investigations carried out on heritage structures using Ambient Vibration Test(AVT)and Operational Modal Analysis(OMA)for system identification(determination of dynamic properties like frequency,mode shape and damping ratios)and associated applications are summarized.A total of 68 major research studies on heritage structures around the world that are available in literature are surveyed for this purpose.At first,field investigations carried out on heritage structures prior to conducting AVT are explained in detail.Next,specifications of accelerometers,location of accelerometers and optimization of accelerometer networks have been elaborated with respect to the geometry of the heritage structures.In addition to this,ambient vibration loads and data acquisition procedures are also discussed.Further,the state of art of performing OMA techniques for heritage structures is explained briefly.Furthermore,various applications of system identification for heritage structures are documented.Finally,conclusions are made towards errorless system identification of heritage structures through AVT and OMA.

Operational modal parameter identification with colored noise excitation

Operational Modal Analysis(OMA) refers to the modal analysis of a structure in its operating state. The advantage of OMA is that only the output vibration signal of a system is used in the analysis process. Classic OMA is based on the white noise excitation assumption and many identification methods have been developed in both time domain and frequency domain. But in reality, many environmental excitations are not compliance with the white noise assumption. In this paper, a method of half power bandwidth analysis is applied to power spectrum analysis to deal with the colored noise and trapezoidal spectral excitation. The modal frequencies and modal damping ratios are derived and the error caused by trapezoidal spectral and colored noise excitation are analyzed. It is proved that the OMA algorithm based on the white noise assumption can be extended to the colored noise environments under certain conditions. Finally, a simulation example with a cantilever beam and a vibration test with four kinds of colored noise and trapezoidal spectrum base excitation are carried out and the results support the proposed method.

基于改进SSI法的加工机器人工作模态在线辨识

为提高串联式机器人的加工能力,提出一种基于改进SSI法的加工机器人工作模态在线辨识方法,该方法首先在传统SSI法的基础上进行改进,利用NExT法、模态置信因子及模态保证准则3种手段来提高加工机器人工作模态参数辨识精度。其次,利用加工机器人铣削加工振动数据对其走刀路径上关键点位出的模态频率和阻尼比进行在线辨识。最终,通过机器人切削加工实验验证该方法,实验结果表明,相比于锤击实验结果,改进SSI法模态参数辨识误差在7%以内,且相比于传统SSI法,改进SSI法模态参数辨识精度更高。因此,若仅关心模态频率及阻尼比,该方法可实现加工机器人走刀过程中模态参数的在线辨识,可为后续机器人进行工艺规划和优化提供输入条件。

结构运营模态参数识别的稀疏分量分析新方法

结合时频掩码技术和模糊C均值聚类,提出一种结构运营模态参数识别新方法。该方法根据结构振动响应的能量信息建立时频掩码,通过时频掩码求解结构模态响应,采用单自由度模态参数识别技术从模态响应中识别模态频率和阻尼比。结构振动响应能量峰值处的时频系数被依次提取,经单源点检测后采用模糊C均值聚类对其聚类,将第一个聚类中心作为模态振型。通过数值案例和框架结构试验验证所提方法的有效性。结果表明,所提方法具有良好的模态参数识别精度和噪声鲁棒性。

改进DBSCAN的自动工作模态分析方法

为解决随机子空间法在模态参数识别过程中自动性差、虚假模态难以识别剔除等问题,提出一种新的模态参数辨识方法。采用协方差驱动的随机子空间法(Covariance⁃driven stochastic identification,SSI⁃COV)识别系统的模态参数;根据软硬准则初步剔除虚假模态并绘制三维稳定图;对基于密度的带噪声的空间聚类算法(Density⁃based spatial clustering algorithm with noise,DBSCAN)进行改进,自动确定敏感参数ε,并对候选模态进行聚类分析;对每一簇类模态,计算模态质量评价准则(Modal quality assessment criterion,MQAC),制定筛选准则,自动剔除虚假模态并识别真实模态。利用本文方法对桁架结构、广州塔、Z24桥实例进行模态参数识别验证,结果表明该方法可实现典型工程结构的自动工作模态分析,可有效剔除非白噪声激励及测量噪声导致的虚假模态。

基于激光多普勒测振的结构工作模态分析

为验证激光多普勒测振仪快速识别高层建筑结构模态参数的适用性,提出一种采用两台远距离激光多普勒测振仪进行环境振动测试的方法。以一悬臂灯杆为例进行实测,获得振动速度响应。该方法实施便捷,测量精度能够满足要求。本文采用频域分解法(FDD)及随机子空间算法(SSI)对其模态参数进行了识别,为进一步应用于环境激励下的高层建筑等大型结构提供试验基础。结果表明,两种算法均可较好地获取结构自振频率及模态振型,自振频率差异很小,振型则几乎一致。并对灯杆进行简化的有限元分析,将模态识别结果与数值模拟结果相比,二者振型吻合较好,但自振频率略有差别。为避免模态遗漏和虚假模态,应结合多种模态识别算法进行模态分析。最后采用该方法对一超高层建筑进行环境激励下的振动实测与模态参数识别。

A refined Frequency Domain Decomposition tool for structural modal monitoring in earthquake engineering

Output-only structural identification is developed by a refined Frequency Domain Decomposition（rFDD） approach, towards assessing current modal properties of heavy-damped buildings（in terms of identification challenge）, under strong ground motions. Structural responses from earthquake excitations are taken as input signals for the identification algorithm. A new dedicated computational procedure, based on coupled Chebyshev Type Ⅱ bandpass filters, is outlined for the effective estimation of natural frequencies, mode shapes and modal damping ratios. The identification technique is also coupled with a Gabor Wavelet Transform, resulting in an effective and self-contained time-frequency analysis framework. Simulated response signals generated by shear-type frames（with variable structural features） are used as a necessary validation condition. In this context use is made of a complete set of seismic records taken from the FEMA P695 database, i.e. all 44 ＂Far-Field＂（22 NS, 22 WE） earthquake signals. The modal estimates are statistically compared to their target values, proving the accuracy of the developed algorithm in providing prompt and accurate estimates of all current strong ground motion modal parameters. At this stage, such analysis tool may be employed for convenient application in the realm of Earthquake Engineering, towards potential Structural Health Monitoring and damage detection purposes.

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

Conventional modal parameter identifications are usually based on frequencyresponse 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 aswind/wave load) are not measurable. Modal parameters estimation must base itself on response-onlydata. Over the past years, many time-domain modal parameter identification techniques fromoutput-only are proposed. A poly-reference frequency-domain modal identification scheme onresponse-only is presented. It is based on coupling the cross-correlation theory with conventionalfrequency-domain modal parameter extraction. An experiment using an airplane model is performed toverify the proposed method.

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。

内部激励下高速列车齿轮箱振动行为及轴承载荷特性实验研究

内部激励是影响高速列车齿轮箱振动及齿轮箱轴承动载荷的重要因素。借助齿轮箱传动系统试验台架,在多种扭矩与转速工况下,开展了高速列车齿轮箱箱体振动响应及齿轮箱轴承载荷测试试验。对各工况下齿轮箱不同部位的振动信号进行分析,发现特定转速下的齿轮啮合频率能够激发齿轮箱箱体的模态共振,而扭矩能够影响系统的频响特征。对加速工况下的齿轮箱振动加速度进行了阶次跟踪,并通过基于阶次的工作模态识别方法获取了齿轮箱箱体的模态参数,发现齿轮箱的工作模态振型导致了齿轮箱在不同转速下振动行为的差异。通过对比不同工况下齿轮箱振动加速度均方根和实测轴承载荷变异系数,建立了齿轮箱轴承载荷动态特性与齿轮箱振动行为间的对应关系。

Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge

Sea-crossing bridges have attracted considerable attention in recent years as an increasing number of projects have been constructed worldwide.Situated in the coastal area,sea-crossing bridges are subjected to a harsh environment(e.g.strong winds,possible ship collisions,and tidal waves)and their performance can deteriorate quickly and severely.To enhance safety and serviceability,it is a routine process to conduct vibration tests to identify modal properties(e.g.natural frequencies,damping ratios,and mode shapes)and to monitor their long-term variation for the purpose of early-damage alert.Operational modal analysis(OMA)provides a feasible way to investigate the modal properties even when the cross-sea bridges are in their operation condition.In this study,we focus on the OMA of cable-stayed bridges,because they are usually long-span and flexible to have extremely low natural frequencies.It challenges experimental capability(e.g.instrumentation and budgeting)and modal identification techniques(e.g.low frequency and closely spaced modes).This paper presents a modal survey of a cable-stayed sea-crossing bridge spanning 218 m+620 m+218 m.The bridge is located in the typhoon-prone area of the northwestern Pacific Ocean.Ambient vibration data was collected for 24 h.A Bayesian fast Fourier transform modal identification method incorporating an expectation-maximization algorithm is applied for modal analysis,in which the modal parameters and associated identification uncertainties are both addressed.Nineteen modes,including 15 translational modes and four torsional modes,are identified within the frequency range of[0,2.5 Hz].

Modal analysis of subway tunnel in soft soil during operation

Modal parameters are of great significance in civil engineering because they can characterize the properties of structures and be used for vibration control and structural health monitoring.Subway tunnels are long linear truss structures combined with the mutual cou-pling of the surrounding soil.Therefore,the operational modal analysis of a mutual coupling tunnel is complicate,as is the modal iden-tification of shield tunnels in a time–frequency domain,and these are hot civil engineering topics.Using the shield tunnel of Shanghai metro line No.12 project as a case study,we carried out the vibration response monitoring of a subway tunnel during operation and presented methods to identify structural modal parameters.The modal parameters of lower vibration modes were estimated using response measurements.Modal frequencies and shapes were identified with high precision and accuracy using the orthogonal polynomial clustering algorithm under hammer excitation conditions and the autoregressive-moving-average model under ambient excitation con-ditions.The dynamic behavior of a mutual coupling tunnel presented obvious low frequency characteristics,and the first 9th order mode frequencies were less than 100 Hz.The diagonal values of the modal assurance criteria were all greater than 0.85.The modal parameters can be used for the health monitoring of operational subway tunnels.

基于博弈K均值聚类的模态参数自动识别算法

为提高基于随机子空间(stochastic subspace identification, SSI)法模态参数识别的自动化程度,提出了基于博弈K均值(game-based K-means, GBK)聚类的两阶段模态参数自动识别算法。首先,对采集到的数据通过SSI识别出大量待分析的极点并根据所提出的硬性指标对虚假模态进行初步剔除;其次,将前后两阶模态的多类型偏差指标构造成特征矩阵进行GBK聚类,进一步将剩余虚假模态进行剔除;之后,将提取的结构模态进行层次聚类得到各阶模态;随后将提出的方法通过六自由度质量-弹簧结构数值模型进行验证,最后,将此算法应用于某大跨悬索桥的实际监测数据中进行模态参数自动识别,进一步验证了该方法在实际工程中对采集到的海量数据进行自动化识别的可行性和适用性。

倒频谱编辑法在含转子结构运行模态参数辨识中的应用

工况模态分析或称运行模态分析,通常假设结构所受载荷为白噪声,响应谱中峰值皆由结构模态导致。然而机械系统通常含有转子部件,在运行状态下测量响应中周期性谐波或其倍频响应占据主导,加剧了工况下模态参数辨识的难度。针对上述问题,该文提出了基于倒频谱编辑的工况模态参数辨识方法,识别测量响应中的谐波及其倍频响应并在参数辨识前去除其对模态参数提取的影响。所提方法适用于多谐波激励、谐波频率与结构模态频率重叠、慢变谐波等情况,通过三自由系统算例、自由边界钢梁试验和某型航空发动机试车实测数据验证了方法的可靠性。

运行模态分析的频域空间域分解法及其应用

提出了一种基于频域空间域分解(Frequency and Spatial Domain Decomposition,FSDD)的运行模态分析方法。该法将同时具有输入和输出的试验模态分析的经典方法———复模态指示因子(Complex Mode In-dicator Function,CMIF)法拓展到了仅有输出响应的运行状态模态分析。FSDD法采用奇异值分解将信号空间和噪声空间分离开来,把奇异值曲线作为模态指示的依据,以奇异值向量作为加权函数得到每一阶模态的增强功率谱(Power Spectrum Density,PSD),进而在频域内对增强PSD曲线进行最小二乘拟合以得到准确的模态频率和阻尼参数。采用了一个二层楼仿真算例和在欧洲广为人知的瑞士Z24公路大桥实测算例来验证FSDD算法。

工作模态分析方法的应用研究

在供水管道系统中,管道系统的振动是噪声污染的主要原因,有必要对管道系统的动态特性进行深入地研究．本文对管道系统实验模型进行工作模态分析,利用带阻滤波技术对含有谐波的响应信号进行预处理,使信号满足白噪声激励条件,生成ODS FRF函数,并拟合得到系统的工作模态参数和振型．与试验模态分析结果相对比,并经实例分析,证实了该工作模态分析理论与方法的可行性,可以为供水系统的减振降噪及改造研究提供可靠的依据．