共查询到20条相似文献，搜索用时 187 毫秒
1.
基于Hilbert-Huang变换和随机子空间识别技术提出了两种土木工程结构的模态参数识别方法。方法一是基于Hilbert-Huang变换和自然激励技术,通过经验模态分解和Hilbert变换提取信号的瞬时特性,进而利用自然激励技术和模态分析的基本理论识别结构的模态参数;方法二是基于经验模态分解和随机子空间识别技术,通过经验模态分解对信号进行预处理,进而运用随机子空间识别方法处理得到的结构单阶模态响应以识别结构的模态参数。利用这两种方法,通过对一12层钢筋混凝土框架模型振动台试验测点加速度记录的处理,识别了该模型结构的模态参数。识别结果与传统的基于傅里叶变换的识别结果及有限元分析结果的对比验证了这两种方法的可行性和实用性。 相似文献
2.
基于Hilbert-Huang变换和随机减量技术的模态参数识别 总被引：2，自引：0，他引：2
傅里叶分析的信号处理方法对非线性、非平稳信号的处理能力差,传统的模态参数识别方法也存在阻尼比识别精度不高的问题。基于Hilbert-Huang变换和随机减量技术提出了一种新的、实用的模态参数识别方法,首先对结构振动信号进行滤波处理和经验模态分解,得到若干阶本征模态响应,然后利用随机减量技术提取自由衰减响应,进而由Hilbert-Huang变换得到信号的瞬时特性,最后结合模态识别的基本理论识别结构的模态频率和模态阻尼比。为了验证这一方法的有效性,对12层钢筋混凝土框架模型振动台试验一测点的加速度记录进行了处理,识别了模态参数,识别结果与其它识别方法及有限元分析结果的对比表明该方法识别模态频率是可靠的,而模态阻尼比识别的精准性仍然难以确认。 相似文献
3.
为提高基于模态参数的损伤识别方法的损伤敏感性和噪声鲁棒性,将多源数据融合技术引入到苏通大桥主梁损伤定位方法中。基于D-S证据理论对模态柔度和模态应变能指标进行数据融合,并以苏通大桥扁平钢箱梁为分析对象,对融合后损伤定位指标的应用效果进行了讨论。结果表明：基于数据融合的损伤定位方法具有较强的损伤敏感性,只需要较少的低阶模态信息就能识别主梁的早期损伤;数据融合后,损伤定位指标可以在较强的噪声环境下准确地识别斜拉桥钢箱梁的损伤,具有较好的工程实用性。 相似文献
4.
基于HHT的非线性结构系统识别研究 总被引：11，自引：2，他引：11
本文研究基于HHT的多自由度非线性结构系统识别方法。首先通过EMD分解得到结构的非线性模态(NNM),然后对非线性模态进行H ilbert分析,识别出结构的瞬时特征参数(瞬时振幅、瞬时固有频率等),进而由各参数间关系识别出非线性结构的类型。最后通过一个具有非线性刚度的两自由度剪切型建筑结构的数值模拟验证了该方法的有效性。 相似文献
5.
The primary objective of this paper is to develop output only modal identifi cation and structural damage detection.Identif ication of multi-degree of freedom(MDOF) linear time invariant(LTI) and linear time variant(LTV—due to damage) systems based on Time-frequency(TF) techniques—such as short-time Fourier transform(STFT),empirical mode decomposition(EMD),and wavelets—is proposed.STFT,EMD,and wavelet methods developed to date are reviewed in detail.In addition a Hilbert transform(HT) approach to determine ... 相似文献
6.
Hilbert-Huang变换在密频结构阻尼识别中的应用 总被引：14，自引：3，他引：14
Hilbert—Huang变换是一种新的数据处理方法，由经验模分解(Empirical Mode Decomposition)技术及Hilbert变换两部分组成。本文研究此方法对于密频结构阻尼识别的应用。首先对于两自由度系统模型，说明该方法用于阻尼识别的步骤。进而研究存在频率密集现象的高层建筑的阻尼识别问题。上述结果与理论值及由半功率带宽法的识别值进行了比较，对比显示Hilbert．Huang方法较传统方法具有良好的识别密频结构阻尼的性能，适用于大型结构的系统识别。 相似文献
7.
Recording-based identification of site liquefaction 总被引：2，自引：0，他引：2
Reconnaissance reports and pertinent research on seismic hazards show that liquefaction is one of the key sources of damage to geotechnical and structural engineering systems. Therefore, identifying site liquefaction conditions plays an important role in seismic hazard mitigation. One of the widely used approaches for detecting liquefaction is based on the time-frequency analysis of ground motion recordings, in which short-time Fourier transform is typically used. It is known that recordings at a site with liquefaction are the result of nonlinear responses of seismic waves propagating in the liquefied layers underneath the site. Moreover, Fourier transform is not effective in characterizing such dynamic features as time-dependent frequency of the recordings rooted in nonlinear responses. Therefore, the aforementioned approach may not be intrinsically effective in detecting liquefaction. An alternative to the Fourier-based approach is presented in this study, which proposes time-frequency analysis of earthquake ground motion recordings with the aid of the Hilbert-Huang transform （HHT）, and offers justification for the HHT in addressing the liquefaction features shown in the recordings. The paper then defines the predominant instantaneous frequency （PIF） and introduces the PiF-related motion features to identify liquefaction conditions at a given site. Analysis of 29 recorded data sets at different site conditions shows that the proposed approach is effective in detecting site liquefaction in comparison with other methods. 相似文献
8.
基于环境激励下结构动力响应信号分析与处理识别结构的模态参数,是结构健康监测和损伤诊断的一个重要环节,目前为止,要得到较为可靠的识别结果仍有一定困难,尤其是模态阻尼比。基于自然激励技术和傅里叶变换的时移特性,提出了一种新的结构模态阻尼比估算方法,通过理论推导和仿真算例验证了该方法的可行性,进而利用一刚构-连续组合梁桥在环境激励下的动力测试数据,通过该方法对其阻尼比进行了识别,并将识别结果与数据驱动随机子空间法的识别结果进行了对比。结果表明:提出的方法可以减轻噪声影响,得到可接受的识别结果,可为大型工程结构阻尼比的识别提供一个方便和有效的途径。 相似文献
9.
The Random Decrement Technique (RDT), based on decentralized computing approaches implemented in wireless sensor networks (WSNs), has shown advantages for modal parameter and data aggregation identification. However, previous studies of RDT-based approaches from ambient vibration data are based on the assumption of a broad-band stochastic process input excitation. The process normally is modeled by filtered white or white noise. In addition, the choice of the triggering condition in RDT is closely related to data communication. In this project, research has been conducted to study the nonstationary white noise excitations as the input to verify the random decrement technique. A local extremum triggering condition is chosen and implemented for the purpose of minimum data communication in a RDT-based distributed computing strategy. Numerical simulation results show that the proposed technique is capable of minimizing the amount of data transmitted over the network with accuracy in modal parameters identification. 相似文献
10.
Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, an appropriate data analysis and system identification technique is required to interpret the measured data and to identify the state of the structure. Generally, the recursive system identification algorithm is used. In this study, the recursive subspace identification (RSI) algorithm based on the matrix inversion lemma algorithm with oblique projection technique (RSI-Inversion-Oblique) is applied to investigate the time-varying dynamic characteristics. The user-defined parameters used in the RSI-Inversion-Oblique technique are carefully discussed, which include the size of the data Hankel matrix (i), model order to extract the physical modes, and forgetting factor (FF) to detect the time-varying system modal frequencies. Response data from the Northridge earthquake from the Sherman Oaks building (CSMIP) is used as an example to examine a systematic method to determine the suitable user-defined parameters in RSI. It is concluded that the number of rows in the data Hankel matrix significantly influences the identification of the time-varying fundamental modal frequency of the structure. An algorithmic model order selection method using the eigenvalue distribution of RSI-Inversion can detect the system modal frequencies at each appending data window without causing any abnormality. 相似文献
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12.
建筑物在强震中可能受到损伤,通过对结构瞬时频率的分析可以诊断出结构的损伤发展过程。本文探讨了基于H ilbert-Huang变换的结构物损伤诊断方法,研究了如何从结构地震响应信号中提取模态响应、1阶模态振型和损伤发展规律。本文采用HHT法分析了Northridge地震中某超高层建筑物的强震记录,分析结果表明:带有间歇检验准则的经验模态分解法能够提取结构的模态振动响应;通过分析不同楼层的相对H ilbert边际谱能够识别出结构的1阶模态振型;分析结构振动中瞬时频率的时变特点,可以直观地掌握振动中结构的损伤发展规律。 相似文献
13.
Two‐stage damage detection algorithms of structure using modal parameters identified from recursive subspace identification 
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下载免费PDF全文 Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, appropriate data analysis and feature extraction techniques are required to interpret the measured data and to identify the state of the structure and, if possible, to detect the damage. In this study, the recursive subspace identification with Bona‐fide LQ renewing algorithm (RSI‐BonaFide‐Oblique) incorporated with moving window technique is utilized to identify modal parameters such as natural frequencies, damping ratios, and mode shapes at each instant of time during the strong earthquake excitation. From which the least square stiffness method (LSSM) combined with the model updating technique, called efficient model correction method (EMCM), is used to estimate the first‐stage system stiffness matrix using the simplified model from the previously identified modal parameters (nominal model). In the second stage, 2 different damage assessment algorithms related to the nominal system stiffness matrix were derived. First, the model updating technique, called EMCM, is applied to correct the nominal model by the newly identified modal parameters during the strong motion. Second, the element damage index can be calculated using element damage index method (EDIM) to quantify the damage extent in each element. Verification of the proposed methods through the shaking table test data of 2 different types of structures and a building earthquake response data is demonstrated to specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction. 相似文献
14.
目前作为结构健康监测系统核心的损伤识别大多是基于模态参数变化而进行的,但模态参数对局部损坏不敏感,导致损伤识别精度不够。波在结构中的传播状态可以更好地反映局部损伤状况,波动能量可以作为损伤识别的有效指标。为了提高环境激励下结构损伤识别的精度,采用S变换分析了结构输出信号,建立波动能量指标,从而使波动能量指标的使用领域扩展到非平稳信号范围。最后通过三层钢框架试验及弹性分层剪切梁的数值模型对该方法进行了验证,结果表明:该方法不仅能够有效识别结构损伤位置,而且能够识别出损伤程度。 相似文献
15.
对损伤部位向量(DLV)法作了简单介绍,并用该方法对钢框架进行了损伤识别和损伤定位。该方法假定结构损伤前后为线性,对结构损伤前后柔度矩阵差进行奇异值分解,将奇异值为零所对应的向量,作为静荷载施加在无损结构的测点位置,则应力为零的单元为可能损伤的单元。对3种不同工况的钢框架进行了振动模态试验,用前3阶模态参数构造框架的柔度矩阵,按照DLV法对其进行了损伤识别,识别结果与已知损伤情况相一致。从测试自由度不完备、噪声和振型质量归一化系数这3个方面对识别效果进行了分析,结果表明:当损伤使结构动力特性有微小改变时,使用该方法不易定位损伤,应结合局部损伤识别方法进行判定;当损伤使结构动力特性有较大改变时,该方法能有效识别损伤的单元。DLV方法概念简单,理论明确,不受结构类型的限制,不需要结构的数学模型和模型缩聚或扩展技术,只需获得结构损伤前后的前几个低阶模态参数,即可识别结构一处或多处损伤,实际应用时可操作性强。 相似文献
16.
Dong Yinfeng Li Yingmin Xiao Mingkui Lai Ming 《Soil Dynamics and Earthquake Engineering》2008,28(1):7-19
Some limitations of the Hilbert–Huang transform (HHT) for nonlinear and nonstationary signal processing are remarked. As an enhancement to the HHT, a time varying vector autoregressive moving average (VARMA) model based method is proposed to calculate the instantaneous frequencies of the intrinsic mode functions (IMFs) obtained from the empirical mode decomposition (EMD) of a signal. By representing the IMFs as time varying VARMA model and using the Kalman filter to estimate the time varying model parameters, the instantaneous frequencies are calculated according to the time varying parameters, then the instantaneous frequencies and the envelopes derived from the cubic spline interpolation of the maxima of IMFs are used to yield the Hilbert spectrum. The analysis of the length of day dataset and the ground motion record El Centro (1940, N–S) shows that the proposed method offers advantages in frequency resolution, and produces more physically meaningful and readable Hilbert spectrum than the original HHT method, short-time Fourier transform (STFT) and wavelet transform (WT). The analysis of the seismic response of a building during the 1994 Northridge earthquake shows that the proposed method is a powerful tool for structural damage detection, which is expected as the promising area for future research. 相似文献
17.
随机子空间方法在桥塔模态参数识别中的应用 总被引：3，自引：0，他引：3
基于环境振动的结构模态参数识别方法正逐渐成为国内外研究的一大热点。环境振动方法就是仅仅利用结构测试的输出信号进行结构的模态参数识别,随机子空间方法就是其中的一种。随机子空间法是近年来发展起来的一种线性系统辩识方法,可以有效地从环境激励的结构响应中获取模态参数。它属于时域的方法,该方法不需要进行FFT变换,它不仅可以识别结构的频率,而且可以识别结构的阻尼和振型。文章首先介绍了随机子空间的理论,然后用该方法对正在施工中的南京长江三桥的南塔进行模态参数识别,通过与其他方法的识别结果进行比较,证明随机子空间方法不失为一种有效的模态参数识别方法。 相似文献
18.
为高效准确识别桥梁结构损伤,将深度学习与结构动力特性相结合,提出基于双层深度置信网络的桥梁结构损伤识别方法。首先取结构前3阶竖向振动频率和跨中节点前3阶竖向振动模态位移为参数,将其共同作为首层深度置信网络(DBN)的输入数据对结构的损伤位置进行识别;然后以1阶竖向振动的模态位移差作为参数,基于二层DBN对结构损伤程度进行预测;最后以郑许市域铁路桥梁为例进行验证。计算结果显示,当不考虑误差时,基于双层深度置信网络的结构损伤方法进行识别且结果精确;当噪声程度不超过10%时,定位识别结果准确率达100%;当噪声程度不超过15%时,定量识别结果最大绝对误差限不超过1.15%,识别结果准确;与传统的BP神经网络方法相比,本方法识别精度更高,抗噪性更强。 相似文献
19.
对一基础隔震钢筋混凝土框架结构在无填充墙情况下进行了环境激励下的动力测试,重点利用Hilbert-Huang变换与随机减量技术相结合的方法识别了其模态参数,并与随机子空间识别法、有理分式多项式法识别的结果进行了对比。识别结果表明在环境激励下,基础隔震结构的基本周期远小于多遇和罕遇地震工况下设计计算的基本周期;等效黏滞阻尼比很小,近乎于基础固定模型。对隔震层阻尼特性的分析表明,环境激励下可以将基础隔震结构视为经典的比例阻尼系统。进一步以识别的模态参数为基准,采用优化的方法数值反演了环境激励下该结构隔震层的实际水平等效刚度,结果表明其值为多遇地震下计算刚度取值的10.75倍。 相似文献
20.
Evaggelos Ntotsios Costas Papadimitriou Panagiotis Panetsos Grigorios Karaiskos Kyriakos Perros Philip C. Perdikaris 《Bulletin of Earthquake Engineering》2009,7(2):469-483
A bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors.
Sophisticated structural identification methods, combining information from the sensor network with the theoretical information
built into a finite element model for simulating bridge behavior, are incorporated into the system in order to monitor structural
condition, track structural changes and identify the location, type and extent of damage. This work starts with a brief overview
of the modal and model identification algorithms and software incorporated into the monitoring system and then presents details
on a Bayesian inference framework for the identification of the location and the severity of damage using measured modal characteristics.
The methodology for damage detection combines the information contained in a set of measurement modal data with the information
provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in
the structure. The effectiveness of the damage detection algorithm is demonstrated and validated using simulated modal data
from an instrumented R/C bridge of the Egnatia Odos motorway, as well as using experimental vibration data from a laboratory
small-scaled bridge section. 相似文献