Output only modal identification and structural damage detection using time frequency & wavelet techniques

The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification 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 frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.

Output-only identification of the modal and physical properties of structures using free vibration response

The viability of a complete structural characterization of civil structures is explored and discussed. In particular, the identification of modal （i.e. natural frequencies, damping ratios and modal shapes） and physical properties （i.e. mass and stiffness） using only the structure＇s free decay response is studied. To accomplish this, modal analysis from flee vibration response only （MAFVRO） and mass modification （MM） methodologies are engaged along with Wavelet based techniques for optimal signal processing and modal reconstruction. The methodologies are evaluated using simulated and experimental data. The simulated data are extracted from a simple elastic model of a 5 story shear building and from a more realistic nonlinear model of a RC frame structure. The experimental data are gathered from shake table test of a 2-story scaled shear building. Guidelines for the reconstruction procedure from the data are proposed as the quality of the identified properties is shown to be governed by adequate selection of the frequency bands and optimal modal shape reconstruction. Moreover, in cases where the structure has undergone damage, the proposed identification scheme can also be applied for preliminary assessment of structural health.

Automatic modal parameter identification of high arch dams:feasibility verification

Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the densitybased spatial clustering of applications with noise(DBSCAN) algorithm and the stochastic subspace identification(SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

Modal identification of multi-degree-of-freedom structures based on intrinsic chirp component decomposition method

Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination.This paper proposes a new time-frequency method based on intrinsic chirp component decomposition(ICCD)to address these issues.In this method,a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction.The effectiveness and accuracy of the proposed method are illustrated using three examples:a cantilever beam structure with intensive noise contamination or environmental interference,a four-degree-of-freedom structure with two closely spaced modes,and an impact test on a cantilever rectangular plate.By comparison with the identification method based on the empirical wavelet transform(EWT),it is shown that the presented method is effective,even in a high-noise environment,and the dynamic characteristics of closely spaced modes are accurately determined.

线上线下双模式教育的应用——以机械电子工程专业必修课《热力学与传热学基础》为例

后疫情时代,为适应教学要求,逐渐衍生出线上线下双模式的教学方式。本文以机械电子工程专业的必修课《热力学与传热学基础》为例介绍了线上线下双模式教育在教学中的应用。首先介绍了《热力学与传热学基础》这门专业必修课的主要授课内容、学习目标和考核标准,其次以理想气体基本热力学过程的p-v图和T-s图为例,说明了双模式教育的应用为教学带来的好处。

基于谐响应分析的冲击式水轮机转轮共振预测

冲击式水轮机水斗的疲劳断裂与转轮的周期性振动密切相关。以六喷嘴冲击式水轮机模型机组转轮为研究对象,采用基于模态叠加的谐响应分析法,获取转轮在实际工作载荷激励下的形变与应力响应并评估薄弱部位,结合坎贝尔图确定共振转速。结果表明,在实际运行工况下转轮三阶模态振型最易被激发,最大形变值为21.50mm,位于水斗尖端;七阶与八阶频率下被施加最大简谐载荷的水斗等效应力值较大,从根部向水斗尖端缺口处递减,最大应力值出现在分水刃根部,发生明显的应力集中;坎贝尔图显示易发生谐共振的转速为587r/min、660r/min、785r/min,在实际运行中应进一步测试与避免。

Review and comparison of methods and benchmarks for automatic modal identification based on stabilization diagram

Automatic modal identification via automatically interpreting the stabilization diagram provides key technique in bridge structural health monitoring.This paper reviews the progress in the area of automatic modal identification based on interpreting the stabilization diagram.The whole identification process is divided into four steps from establishing the stabilization diagram to removing the outliers in the identification results.The criteria and algorithms used in each step in the existing studies are carefully summarized and classified.Comparisons between typical methods in cleaning and interpreting the stabilization diagram are also conducted.Real structure benchmarks used in the existing studies to validate the proposed automatic modal identification methods are also summarized.Based on the review and comparison,the specific ratio method for cleaning the stabilization diagram,the hierarchical clustering method for interpreting the stabilization diagram and the adjusted boxplot for removing the outliers in the identification results are the most suitable methods for each step.The key point of automatic modal identification based on interpreting the stabilization diagram has also discussed,and it is recommended to pay more attention to cleaning the stabilization diagram.Future study about automatic modal identification under situation with very few sensors deployed should be more concerned.This review aims to help researchers and practitioners in implementing existing automatic modal identification algorithms effectively and developing more suitable and practical methods for civil engineering structures in the future.

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。

对用稳定图识别真假模态的一点改进

利用单位样值响应矩阵和自由响应的特征值分解形式中的特征值前的系数矩阵的范数作为稳定判据，结合稳定图识别真假模态。计算表明，本方法具有较好的分解能力。

稳定图方法在随机子空间识别模态参数中的应用

参数识别是结构健康监测领域研究中的重点。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。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,但该方法容易识别出虚假模态。针对这种情况对稳定图方法进行了改进,避免了虚假模态的出现,进而提高了随机子空间方法的识别精度。稳定图方法改进的重点是用模态置信因子来消除虚假模态。同时由于通常采用的阻尼理论与实际情况尚存在差距,影响了识别效果。在稳定图中将阻尼比的标准放松或取消,得到更加理想的识别效果。最后对此方法在一三跨连续梁的数值模型上进行了验证,结果表明,该方法具有良好的识别效果。

基于泄流响应的高拱坝模态参数辨识与动态监测

模态参数的准确辨识是对在线结构进行损伤诊断和状态监测的难点和核心之一。以二滩高拱坝原型测试为背景,结合随机子空间算法与改进的稳定图对泄流激励下的拱坝模态参数进行辨识,并实现对高坝振动状态的长期在线监测,为结构的安全评估提供依据。针对时域法所面临的模型定阶困难和噪声干扰以及由它们所引起的虚假模态辨识与剔除问题,利用改进的稳定图方法对模态参数进行更为精确辨识,并与ERA算法的识别结果进行对比。研究结果表明,该方法具有良好的品质与较高的计算精度,具有良好的工程应用前景。

模态参数自动识别的虚假模态剔除方法综述

模态参数自动识别技术的关键在于对真实模态和虚假模态进行自动甄别。对模态参数识别方法中自动剔除虚假模态的方法进行了综述。围绕如何自动剔除虚假模态,将现有模态参数自动识别方法分为三类:基于智能算法的稳定图自动分析方法、基于指标阈值的真假模态自动区分方法、基于改进识别算法获得清晰稳定图的自动分析技术。对以上三类方法的原理进行了详细的介绍,提出了各自的特点、存在的问题以及将来的研究方向。结合实测加速度数据,采用具有代表性的自动识别方法对一钢筋混凝土结构建筑进行虚假模态的自动剔除,并对不同方法的识别结果进行了比较。

望远镜塔架系统试验模态分析

塔架系统的振动特性对望远镜的成像质量具有重要的影响。以望远镜塔架模型为研究对象进行模态分析,针对无参数化模态分析引起模态参数不准确的问题,提出采用最小二乘复指数法(LSCE)进行试验模态分析,确定结构的模态频率、阻尼比和模态振型。通过稳态图方法剔除识别过程所衍生的虚假模态,又通过模态置信判据验证模态的相关性,提高了模态参数识别结果的准确性,与无参数化试验模态分析结果对比表明,模态频率的最大误差仅为4.4%,一致性较好,结果表明LSCE算法的正确性和适应性。

基于传递函数有理分式的模态参数识别算法研究

为降低多参考点模态参数识别过程中系数矩阵的计算精度损失,利用传递函数有理分式模型,在正交多项式识别算法的基础上提出一种改进的模态参数识别方法,并给出该算法的实现流程和稳定图的建立方法;通过实测算例对所提出的方法进行验证。结果表明,该方法有效地识别模态参数,并简化参数的计算过程,提高识别效率。该结论将对采用模态参数识别结构的完整性研究提供借鉴。

一种确定自回归滑动平均模型最小阶次的新方法

针对经典Akaike信息准则(AIC)在模型定阶时缺少阶次范围下界而引起的模态遗漏问题,根据稳态图和AIC准则,提出了一种自回归滑动平均模型在模态参数辨识中的定阶方法.该方法先利用稳态图能够鉴别真假模态的特点,进行各阶模态频率的估计和均值的求取,进而根据模态稳定性判定准则计算出阶次范围下界,最后利用AIC准则确定最优的模型阶次.仿真结果表明,与经典AIC准则相比,所提出的方法定阶后进行模态参数的辨识,不仅识别出了经典AIC准则遗漏的第3阶模态参数(误差为0.18%),而且使第1、2阶模态参数的精度分别提高了2.31%和6.31%.对悬臂梁的模态实验结果表明:该方法不仅辨识出了经典AIC准则遗漏的第1阶模态参数,使其误差仅为0.62%,而且也大大提高了其他各阶模态参数的精度.

基于环境激励的大跨度斜拉桥模态参数和索力识别

模态参数和索力是评估斜拉桥健康状态的关键参数。建立了崖门大桥的有限元模型并对其进行了基于环境激励的模态测试和拉索振动测试;提出了基于ERA的多参考点稳定图算法,设置不同的参考点,利用自然激励技术结合特征系统实现算法(NExT-ERA)识别模态参数,通过阻尼比、基于输出矩阵的一致模态指标(CMI_O)和模态置信度(MAC)作为判别标准,识别出崖门大桥的竖向和横向模态参数,通过和增强频域分解法(EFDD)识别结果的比较,可知该算法具有良好的识别效果;分析了斜拉索与主梁的共振频率范围,通过二次拟合识别较长拉索的低阶频率,根据两种不同方法的索力识别结果可知,该桥的索力分布比较均匀对称。

随机子空间产生虚假模态及模态遗漏的原因分析

参数识别是结构健康监测领域研究中的重点。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。由于它具有只需给定系统的阶次一个参数,不存在不收敛的情况等优势,越来越受到国内外业内人士的广泛关注。但是该方法也不是十全十美的,容易产生虚假模态和模态遗漏现象,这些都严重影响了识别效果。因此将识别结果中的虚假模态剔除,是随机子空间方法进一步在理论和应用上拓展的关键。要做到这些,首先要分析虚假模态产生的原因。该文就对虚假模态的产生原因进行了分析。分析表明虚假模态产生的原因主要有两方面:一方面是由于随机子空间方法的基本计算过程而导致的;另一方面是由于实际应用中输入信号不满足白噪声的假定和/或输出信号受到环境的干扰而导致的。分析中采用了一数值模拟例子。

环境激励下结构模态辨识的识别概率直方图方法研究

在环境激励下辨识结构模态时,系统阶次作为关键计算参数不易准确判定,通常采用基于假设系统阶次的稳定图方法来辅助进行,但其中稳定轴的判定较为主观,容易遗漏真实模态及引入虚假模态。基于数据挖掘技术,提出识别概率直方图(Identification-probability Histogram,IpHist)的新方法,对不同假设系统阶次下通过随机子空间识别得到的多组备选模态,根据频率容差和模态置信度容差准则进行一致性聚类,继而计算群组聚类结果的识别概率,并绘制相应的识别概率直方图,最后选取识别概率大的结果作为结构模态结果。通过IASC-ASCE结构健康监测工作组提供的4层框架Benchmark模型算例,阐述了所提IpHist方法在环境激励下辨识结构模态的有效性,显示了方法较强的抗噪能力。

基于随机子空间结合稳定图的拱桥模态参数识别方法

为了避免目前常用的结构模态参数识别方法容易出现虚假模态等缺陷,提出了一种将随机子空间法与稳定图法相结合的模态参数识别方法。通过随机子空间法有效地从环境激励的结构响应中获取模态参数,通过稳定图方法确定系统阶次。通过对稳定图方法的改进,避免了虚假模态的出现,进而提高了随机子空间方法的识别精度,并在一钢管混凝土拱桥上对这种新方法进行了验证。结果表明,该方法具有良好的识别效果。

PolyMAX模态参数识别算法的快速实现

PolyMAX是近几年发展起来的宽频带模态参数识别算法,该方法在抗干扰能力、识别密集模态、以及处理大阻尼复杂结构方面具有非常好的特性。通过避免缩减正则方程计算中的矩阵求逆以及乘法运算,实现了该算法的快速实现。最后采用七自由度仿真算例与航空发动机涡轮盘实测算例进行验证。结果表明:快速实现能够在对识别精度影响很小的情况下显著提高参数识别速度。