共查询到19条相似文献，搜索用时 171 毫秒
1.
利用神经网络和Kalman滤波技术,提出了一种直接识别结构物理参数的方法,用Kalman滤波技术训练网络。在贮仓振动台实验的基础上,用贮仓在动载作用下的位移、速度作为网络的输入,激振加速度和响应加速度作为网络的输出。仿真计算表明,本文方法是可行的。 相似文献
2.
3.
提出一种自适应协方差的时频域极化滤波方法。该方法在广义S变换时频方法的基础上,构造时频域自适应协方差矩阵,通过特征分析计算时频域瞬时极化参数,设计极化滤波器,实现多分量地震极化分析和滤波。其优势在于协方差矩阵的分析时窗的长度由多分量地震数据的瞬时频率确定,可以自适应于有效信号的周期,在每个时频点计算极化参数不需要进行插值处理;结合时间频率信息,解决在时间域或频率域波形或频率重叠的信号具有明显的直观性。模型数据及实际三分量台站地震数据处理结果表明,该极化滤波方法在台站地震资料分析和处理方面具有很好的直观性和较高的分辨率。 相似文献
4.
本文叙述了一种可以从地球物理时间或空间序列数据中消除干扰噪声、随机噪声以及线性或非线性漂移的新型自适应滤波程序,为从“原始”信号中消除这些噪声、需要一个(以某种未知方式)与原始信号相关的“参考”信号。参考信号经过自适应滤波并被从原始信号中减去。由此产生的误差被用于最速下降法中以调整滤波器的权重,从而使得最小二乘意义上的均方误差极小。上述过程经过反复迭代,直至取得收敛。滤波器的输出即为原始输入中基本信号的最佳最小二乘估计,而不含噪声的影响。该方法的一个显著特点是它能够分辨一个随时间变化的信号而无需对其中信号和噪声的特征有任何先验的了解,同时也说明了当没有参考信号时,如何从序列数据中消除噪声。该程序已被应用于几种合成信号以表明它的特性;对于该程序中的数学部分做了简要的讨论。文中列出了程序的核心部分,并提出了一些在其它地球科学领域中的可能应用。 相似文献
5.
多速率Kalman滤波方法可用于低采样率的位移和高采样率的加速度数据融合,而未知的噪声协方差信息则显著制约着多速率Kalman滤波精度.本文通过将多速率Kalman滤波转换为传统的单速率Kalman滤波,建立了Kalman滤波增益的自协方差矢量与未知的加速度谱密度和观测噪声参数间的线性函数模型,并采用最小二乘估计方法对未知的噪声协方差参数进行估计,进而有效地提高了多速率Kalman滤波精度.数值仿真和震动台实验结果验证了本文方法的正确性和有效性. 相似文献
6.
在重力异常分离中,频率域滤波分离方法是以全局数据频谱特征设计针对性的滤波器实现的.滤波器参数与空间位置无关,因此无法针对局部数据频谱特征动态调整滤波器参数.针对该局限性,在小波域滤波理论和优化滤波方法的基础上,本文研究提出了小波域优化位变滤波法,该方法具有空间变化滤波能力,在不同空间位置实现不同的滤波器特性,从而能实现局部数据频谱与全局数据频谱存在较大差异的重力异常分离问题.理论模型数据分离实验结果表明,在局部数据频谱与全局数据频谱差异较大的情况下,该方法相对于Butterworth滤波和优化滤波等方法具有优势.最后,用一个实例进行检验计算,体现了所提方法技术的效果和应用前景. 相似文献
7.
F-X域经验模态分解去噪方法在处理非稳态地震数据时存在两个局限,一是单纯剔除第一个固有模态分量将导致有效信号缺失及去噪能力偏弱问题,二是分解复信号时对实部和虚部分别分解存在分解数目不一致的风险。本文对上述两个方面进行了改进,提出了一种新的F-X域投影法复数经验模态分解预测滤波方法,首先采用基于空间投影的复数经验模态分解将F-X域地震数据直接分解为不同的复固有模态分量,然后再对这些分量分别进行F-X域预测滤波。合成记录及实际资料测试表明,本文的新方法能更好地衰减随机噪声,更有效地保持地震信号。 相似文献
8.
共模误差是GPS台站位置时间序列中的主要误差源之一,分离和消除共模误差可以探测到原先淹没在噪音中的微弱信号。本文对中国260个卫星导航定位基准站2011～2018年的数据进行处理,获得了位置时间序列,采用主成分分析（PCA）方法对其扣除构造运动等形变信号后的残差序列进行区域共模误差分析。结果表明,PCA方法有效地提取了中国区域共模误差,经过滤波后时间序列的信噪比明显提高,第一主分量空间分布均一,第二主分量空间分布呈现明显的梯度变化,表明第二主分量不是少数台站的局部效应,是对第一主分量的补充和修正,中国区域的共模误差分析不能忽略二阶分量。对振幅调制的影响进行了对比分析,发现顾及振幅调制的共模误差不仅明显减弱了年周期的特征,还显著降低了闪烁噪声,表明由于参数过于简化而未被估计的“信号”会演变成共模误差,未被正确模型化的“信号”会影响区域滤波的准确性。 相似文献
9.
很多地区地震资料的信噪比较低,而用于压制与信号具有不同方向的随机噪声的常规二维滤波方法常常产生假信息。基于相邻信号具有相干性这一假设,本文提出了一种叠后衰减随机噪声的多道滤波方法。该方法利用信噪比最高的中频段信息(含有主频的这一频率区间)分时窗计算信号单位矢量,并将该时窗内全频段数据向信号单位矢量方向投影,对各时窗(包括时间方向和空间方向)重叠部分按比例进行加权。我们利用这种方法对含有陡倾角的合成地震数据和海上二维实际地震资料进行了处理,处理效果很好。这种方法较为费时,但不受倾角限制,应用范围广。 相似文献
10.
针对当前提取地震前兆数据易受到噪声干扰,且数据库中数据更新速度较慢的问题,提出基于空间相关的地震前兆数据库信息提取与数据更新方法。利用快速Myriad滤波器,引入滑动窗,选择窗口数据参与到计算中,将计算结果当作目前窗口滤波输出值,实现数据滤波,由此实现信息提取的去噪处理。依据初步滤波结果,将当前数据作为中心,并确定空间窗,在横向上进行相关数据统计。针对空间窗中各数据选取滑动时窗,并对其中的数据进行S变换,利用指数拟合的数据传输能力参数,获取缺失数据的填充修复参数。引入曲面加权函数对填充修复参数进行平滑,根据平滑之后的填充修复参数对S变换数据进行更新,实现空间相关下地震前兆数据库的数据更新。实验结果表明,所提方法的数据信噪比较高,数据更新时间较短。 相似文献
11.
基于两阶段稳定图的随机子空间识别结构模态参数 总被引：4，自引：1，他引：3
基于振动的结构健康监测的前提是从振动测试信号中提取结构模态参数。随机子空间方法是近年来发展起来的一种线性系统辨识方法,可以有效地从环境激励的结构响应信号中提取结构模态参数。在随机子空间识别方法中,确定系统的阶数是该方法的关键工作,稳定图方法是一种比较新颖的确定系统阶次的方法。但是随机子空间方法容易产生虚假模态,这也是随机子空间方法的一个主要缺陷。因此针对于这一缺陷提出了一种基于两阶段稳定图的随机子空间识别结构模态参数方法,该方法的基本思想是将在现场采集的结构的输出信号进行分段,将各段信号用随机子空间结合稳定图进行识别,然后将所有各段所识别的模态参数再一次用稳定图方法进行分析,得出结构的模态参数。最后用一三跨连续梁的数值模型对该方法进行验证,结果表明该方法具有良好的识别效果。 相似文献
12.
This paper reviews the theoretical principles of subspace system identification as applied to the problem of estimating black‐box state‐space models of support‐excited structures (e.g., structures exposed to earthquakes). The work distinguishes itself from past studies by providing readers with a powerful geometric interpretation of subspace operations that relates directly to theoretical structural dynamics. To validate the performance of subspace system identification, a series of experiments are conducted on a multistory steel frame structure exposed to moderate seismic ground motions; structural response data is used off‐line to estimate black‐box state‐space models. Ground motions and structural response measurements are used by the subspace system identification method to derive a complete input–output state‐space model of the steel frame system. The modal parameters of the structure are extracted from the estimated input–output state‐space model. With the use of only structural response data, output‐only state‐space models of the system are also estimated by subspace system identification. The paper concludes with a comparison study of the modal parameters extracted from the input–output and output‐only state‐space models in order to quantify the uncertainties present in modal parameters extracted from output‐only models. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
13.
This paper describes the identification of finite dimensional, linear, time‐invariant models of a 4‐story building in the state space representation using multiple data sets of earthquake response. The building, instrumented with 31 accelerometers, is located on the University of California, Irvine campus. Multiple data sets, recorded during the 2005 Yucaipa, 2005 San Clemente, 2008 Chino Hills and 2009 Inglewood earthquakes, are used for identification and validation. Considering the response of the building as the output and the ground motion as the input, the state space models that represent the underlying dynamics of the building in the discrete‐time domain corresponding to each data set are identified. The time‐domain Eigensystem Realization Algorithm with the Observer/Kalman filter identification procedure are adopted in this paper, and the modal parameters of the identified models are consistently determined by constructing stabilization diagrams. The four state space models identified demonstrate that the response of the building is amplitude dependent with the response frequency and damping, being dependent on the magnitude of ground excitation. The practical application of this finding is that the consistency of this building response to future earthquakes can be quickly assessed, within the range of ground excitations considered (0.005g–0.074g), for consistency with prior response—this assessment of consistent response is discussed and demonstrated with reference to the four earthquake events considered in this study. Inclusion of data sets relating to future earthquakes will enable the findings to be extended to a wider range of ground excitation magnitudes. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
14.
An extended Kalman filter algorithm with local iteration is presented for the identification of non-linear and non-stationary soil properties. Borehole-array strong motions were recorded at a liquefied site during the 1995 Hyogoken-nanbu earthquake. In this study, a modified Kalman filtering method in which the extended Kalman filter is iteratively used at every local time-step to track rapid parameter changes is proposed. The method is then applied to the instrumented soil layer, which is modeled by an equivalent linear model. An identification of non-linear and non-stationary soil properties was conducted successfully; and non-linear restoring force–displacement relationships including progression with time were obtained. 相似文献
15.
Application of subspace identification technique to long‐term seismic response monitoring of structures 
下载免费PDF全文
下载免费PDF全文 This paper addresses the issue of structural system identification using earthquake‐induced structural response. The proposed methodology is based on the subspace identification algorithm to perform identification of structural dynamic characteristics using input–output seismic response data. Incorporated with subspace identification algorithm, a scheme to remove spurious modes is also used to identify real system poles. The efficiency of the proposed method is shown by the analysis of all measurement data from all measurement directly. The recorded seismic response data of three structures (one 7‐story RC building, one midisolation building, and one isolated bridge), under Taiwan Strong Motion Instrumentation Program, are analyzed during the past 15 years. The results present the variation of the identified fundamental modal frequencies and damping ratios from all the recorded seismic events that these three structures had encountered during their service life. Seismic assessment of the structures from the identified system dynamic characteristics during the period of their service is discussed. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
16.
An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering 总被引：4，自引：0，他引：4
The ensemble Kalman filter (EnKF) is a commonly used real-time data assimilation algorithm in various disciplines. Here, the EnKF is applied, in a hydrogeological context, to condition log-conductivity realizations on log-conductivity and transient piezometric head data. In this case, the state vector is made up of log-conductivities and piezometric heads over a discretized aquifer domain, the forecast model is a groundwater flow numerical model, and the transient piezometric head data are sequentially assimilated to update the state vector. It is well known that all Kalman filters perform optimally for linear forecast models and a multiGaussian-distributed state vector. Of the different Kalman filters, the EnKF provides a robust solution to address non-linearities; however, it does not handle well non-Gaussian state-vector distributions. In the standard EnKF, as time passes and more state observations are assimilated, the distributions become closer to Gaussian, even if the initial ones are clearly non-Gaussian. A new method is proposed that transforms the original state vector into a new vector that is univariate Gaussian at all times. Back transforming the vector after the filtering ensures that the initial non-Gaussian univariate distributions of the state-vector components are preserved throughout. The proposed method is based in normal-score transforming each variable for all locations and all time steps. This new method, termed the normal-score ensemble Kalman filter (NS-EnKF), is demonstrated in a synthetic bimodal aquifer resembling a fluvial deposit, and it is compared to the standard EnKF. The proposed method performs better than the standard EnKF in all aspects analyzed (log-conductivity characterization and flow and transport predictions). 相似文献
17.
This study presents an effective method for identifying predictive models and the underlying modal parameters of linear structural systems using only measured output and excitation time histories obtained from dynamic testing. The system under examination is modelled as a first‐order multi‐input multi‐output time‐invariant system, and the structural model is realized using the Eigensystem Realization Algorithm together with the Observer/Kalman filter IDentification algorithm. The identified state‐space model is further refined using a non‐linear optimization technique based on sequential quadratic programming. The numerical examples show that the developed methodology performs very well even in the presence of inadequate instrumentation and measurement noise, and that the methodology is highly capable of creating realistic predictive models of structural systems, as well as estimating their underlying modal parameters. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
18.
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
19.
This paper addresses the issue of system identification for linear structural systems using earthquake induced time histories of the structural response. The proposed methodology is based on the Eigensystem Realization Algorithm (ERA) and on the Observer/Kalman filter IDentification (OKID) approach to perform identification of structural systems using general input–output data via Markov parameters. The efficiency of the proposed technique is shown by numerical examples for the case of eight-storey building finite element models subjected to earthquake excitation and by the analysis of the data from the dynamic response of the Vincent-Thomas cable suspension bridge (Long Beach, CA) recorded during the Whittier and the Northridge earthquakes. The effects of noise in the measurements and of inadequate instrumentation are investigated. It is shown that the identified models show excellent agreement with the real systems in predicting the structural response time histories when subjected to earthquake-induced ground motion. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献