基于小波分析的梁损伤识别方法初探

通过对冲击荷载作用下有损伤梁的响应进行分析 ,对某一时刻梁的变形进行连续小波变换 ,从小波变换系数的极值点判断损伤的位置 ,对跨中响应的小波包分解得到各频段能量的特征向量 ,作为判断损伤程度的依据     

基于模态曲率与小波变换的网壳结构损伤识别研究

结合模态曲率与小波变换的方法对网壳结构的损伤识别进行研究。以一网壳结构的缩尺模型为例进行数值分析,假设结构35号杆件的截面出现刚度折减的轻微损伤,以模型损伤前后的模态曲率作为损伤指标进行连续小波变换,从而判断结构的损伤位置。数值分析的结果表明,利用模态曲率的小波变换系数差可以粗略定位损伤,而利用曲率模态差值的小波变换系数可以较为准确地定位损伤,且分析及数据处理过程更为简便可靠,可见基于模态曲率与小波变换的损伤识别方法对于网壳结构的损伤定位是非常有效的。     

小波包分析在结构损伤监测中的应用

小波分析具有良好的时频局部化性质,特别适合于分析和处理突变信号。在获得结构的动力响应的基础上,对结构响应信号做小波包分解。根据各种响应信号对损伤的灵敏度,选择损伤特征,通过捕捉结构出现损伤的时刻,实现对结构损伤时刻监控。为模拟测试实际结构响应噪声的影响,在第一层加速响应信号中加入信噪比为5:1的白噪声,运用小波包消噪后再运用小波包分解识别结构的损伤时刻。     

利用多分辨率分析法进行圆拱结构裂纹定位

小波分析可以同时在时域和频域取得最佳分辨率,这使得小波理论成为处理非平稳信号的有利工具。将小波理论引入到分析损伤结构的特征信号有利于准确地评价结构健康状况。本文将利用小波多分辨率分析对单、双位置的拱结构损伤进行定位研究。数值算例表明:利用sym4小波通过对多分辨率分析的高频成份进行单支重构,其重构系数的突变可以指示出裂纹位置。此外,对多位置的损伤识别,小波多分辨率分析依然有效。     

On‐line system identification of structures using wavelet‐Hilbert transform and sparse component analysis

A new wavelet‐Hilbert transform based sparse component analysis (WHT‐SCA) method is presented for online system identification in indeterminate conditions. The instantaneous phase ratios of output signals are obtained by using a wavelet‐Hilbert transform based filter; and the out‐of‐phase data, that causes errors in identification accuracy, is detected and eliminated. Then, modal parameters of the structure are identified through existing relationships between the dispersion of filtered data in the frequency domain. Subsequently, to demonstrate the capability of the online identification, a new controller is introduced by combining the WHT‐SCA and a semi‐active tuned mass damper (STMD), resulting in creation of smart structures. The performance of the proposed method and controller is investigated through examples. The results demonstrate that, modal parameters of structures are identified accurately even with noise contamination and limited number of sensors. Also, the STMD is effectively robust against any variations in modal parameters of the structure.     

基于小波变换的结构损伤识别与试验分析

钢筋混凝土结构在中等以上地震作用下将产生损伤,结构动力特性随之变化。通过对结构微幅振动信号的Fourier分析,可以判断结构是否产生损伤,但是不能确定损伤位置。本文将结构振动信号置于不同频段进行时-频分析,利用小波变换的多分辨率特点对结构损伤进行在线检测,确定损伤位置。通过钢筋混凝土框架的振动台试验,将模型地震反应信号按不同频段分解,提取各频段的损伤信号特征。对于试件模型而言,如果某处出现开裂,即产生损伤,表现在响应信号上为一瞬态分量,通过信号小波变换的尺度函数可以判断结构某层是否损伤。该方法克服了Fourier变换不能反映结构振动信号局部特性的缺点,试验表明本文所采用的方法是可行的。     

基于小波变换的钢筋混凝土结构损伤识别

钢筋混凝土结构在其服役期内,由于受到荷载、地震以及其他因素的作用,会发生损伤,从而威胁整个结构的安全。为了保证结构的安全,需要尽早发现结构的损伤,并且采取防范和修复措施。基于小波变换的多分辨率的特点,提出一种分层小波搜索的方法对结构损伤进行在线检测,确定损伤位置和损伤发生的时刻。通过对某一框架进行数值模拟试验验证可行的基础上,将此法应用于地震作用下钢筋混凝土框架的损伤识别,并与结构模型的模拟地震振动试验观测结果相对比,表明分析结果与试验观测较好吻合。     

基于数值模拟的顶板围岩损伤过程 动力响应信号的能量分析

 通过室内相似模型试验,模拟金属矿山巷道顶板从稳定状态到损伤发展直至顶板冒落的过程,利用封装应变传感器的光纤光栅锚杆监测振动信号,记录不同围压、不同冲击振动强度下裂隙顶板与完整顶板响应信号,利用小波变换提取信号的频带能量分布特征。根据室内试验对光纤光栅用于顶板稳定性监测的研究成果,利用径向基函数神经网络对监测时间序列进行预测,并基于频带能量的观点,利用Matlab建立不同损伤条件下的瞬态冲击信号评估系统,并对一段连续信号进行分析。损伤顶板振动响应信号的频带能量受到裂隙开展的影响,与完整顶板相比,出现明显的频带尖点,能量峰值从高频向低频移动。基于小波频带能量的围岩顶板稳定分析方法,首先,利用小波分解各频带能量分布特征,并通过分解结果中应力波的能量衰减程度可判别出岩体中是否存在裂隙;其次,利用小波分解后各频带能量比和归一化能量比来确定损伤程度和裂隙开展状态,为金属矿山巷道顶板稳定性监测提供了一种较为可靠的方法。     

基于小波分析的结构损伤信号奇异性检测

结构发生损伤时,其动力特性会发生突然变化,因此会产生奇异信号。Lipschitz指数是表征信号局部奇异性特征的一种度量,可以用来识别结构损伤的发生。小波变换是一种时频域分析方法,具有多分辨率分析的特点,是奇异性信号分析的合适工具。在突变时刻小波系数出现模极大值,通过小波系数模极大值求得的Lipschitz指数可以作为衡量突变程度的指标,由此可以准确识别结构发生损伤的时刻。通过对一空间结构损伤前后加速度信号的奇异性进行分析,验证本文的相关理论。     

A wavelet-based method to generate artificial wind fluctuation data

A method using the inverse wavelet transform is proposed to generate artificial wind velocity fluctuations. At first, in order to investigate the time-scale structure of natural wind, the wavelet transform is applied to the time history of a measured wind velocity data. Taking the results into account, the wavelet-based method is constructed such that the created time history possesses the characteristics similar to those of the natural wind data. The time histories are in particular synthesized to have a target power spectrum and intermittency similar to measured time histories. The characteristics of the time histories produced with the proposed method are discussed.     

Application of stress wave propagation technique for condition assessment of timber poles

Abstract

The stress wave propagation technique can be effectively used to assess the condition of timber utility poles. However, reliable detection of damage based on the reflected wave within the time domain is not always possible. Therefore, various signal processing methods such as frequency-domain analysis and time-frequency analysis can be adopted to overcome this problem depending on the application. In this paper, Hilbert–Huang and continuous wavelet transforms are selected as signal processing methods to analyse the reflected wave. The signal is initially subjected to an empirical mode decomposition process prior to the computation of instantaneous frequencies of the decomposed signals using the Hilbert–Huang transformation. The anomalies in the instantaneous frequency plots can be used to identify any damage and its location along the pole. Additionally, the decomposed signals are subjected to a wavelet transformation to further confirm the existence of damage. The combined Hilbert–Huang and continuous wavelet transform technique is applied to the stress wave signal recorded from the in-service poles to assess the accuracy of the proposed method. This method increases the confidence level of defect identification of timber utility poles.     

小波分析与神经网络在结构多处损伤监测中的应用

采用小波分析对获得的结构动力响应进行小波分解，根据各种响应信号对损伤的灵敏度选择损伤特征，从而识别结构多次出现损伤的时刻，实现对结构损伤时刻的监控；对结构第1层加速度响应信号做小波包分解，得到各频段能量的特征向量，作为特征参数输入到BP神经网络中实现结构多处损伤位置和程度识别。模拟算例表明，小波分析和BP神经网络联合运用能准确地诊断结构多处损伤的时刻、位置和程度，具有一定的可行性。     

钢筋混凝土腐蚀损伤超声导波信号小波分析及评价

本文针对钢筋混凝土中的腐蚀损伤,应用电化学方法对钢筋混凝土小梁进行不同程度的加速腐蚀试验,并对其分别进行超声导波测试,经过首波能量分析,得出腐蚀前后信号能量变化。又分别对原始信号进行小波包分析,得到各腐蚀阶段的小波包能量谱,通过各频段信号的能量分布的变化来诊断结构的损伤程度。用小波包分析得出信号的时频曲线,以及各个损伤阶段的波形特征,损伤类别,和频率分布。     

Vibration based structural health monitoring: Wavelet packet transform based solution

One prominent problem for vibration-based structural health monitoring is to extract condition indices which are sensitive to damage and yet insensitive to measurement noise. In this paper, a condition index extraction method based on the wavelet packet transform (WPT) is proposed. This transform leads to the formulation of a novel condition index: wavelet packet signature (WPS). The sensitivity of the WPS to the change of structural parameters is derived and validated on a five-degrees-of-freedom spring-mass system. Results show that the WPS is significantly more sensitive to the stiffness change than the natural frequencies and the mode shapes. Its sensitivity is slightly better or comparable to that of the modal flexibility matrices depending on the location of damage. A variability analysis is also performed to study the effect of measurement noise on the proposed WPS. Results show that the WPS does not show any significant variation even under the presence of 10 dB noise. To illustrate the potential of the WPS, a damage indicator is formulated and used to monitor the health condition of the structural system. An experimental study on a three-storey frame shows that when incorporated with a statistical process control approach, the WPS-based damage indicator can distinctly identify the presence of damage in the system.     

结构损伤识别中的小波变换法研究

对小波变换在结构损伤识别中使用归结为，对结构服役状态的监测和对结构损伤的定位和评估两类，并选取典型的使用实例进行介绍和评论，最后对小波理论在该领域的应用前景进行了展望。     

Decentralized Parametric Damage Detection Based on Neural Networks

In this paper, based on the concept of decentralized information structures and artificial neural networks, a decentralized parametric identification method for damage detection of structures with multi-degrees-of-freedom (MDOF) is conducted. First, a decentralized approach is presented for damage detection of substructures of an MDOF structure system by using neural networks. The displacement and velocity measurements from a substructure of a healthy structure system and the restoring force corresponding to this substructure are used to train the decentralized detection neural networks for the purpose of identifying the corresponding substructure. By using the trained decentralized detection neural networks, the difference of the interstory restoring force between the damaged substructures and the undamaged substructures can be calculated. An evaluation index, that is, relative root mean square (RRMS) error, is presented to evaluate the condition of each substructure for the purpose of health monitoring. Although neural networks have been widely used for nonparametric identification, in this paper, the decentralized parametric evaluation neural networks for substructures are trained for parametric identification. Based on the trained decentralized parametric evaluation neural networks and the RRMS error of substructures, the structural parameter of stiffness of each subsystem can be forecast with high accuracy. The effectiveness of the decentralized parametric identification is evaluated through numerical simulations. It is shown that the decentralized parametric evaluation method has the potential of being a practical tool for a damage detection methodology applied to structure-unknown smart civil structures.     

Time-Frequency Signal Analysis of Earthquake Records Using Mexican Hat Wavelets

Abstract:   A method is presented for time-frequency signal analysis of earthquake records using Mexican hat wavelets. Ground motions in earthquakes are postulated as a sequence of simple penny-shaped ruptures at different locations along a fault line and occurring at different times. The single point source displacement of ground motion is idealized by a Gaussian function. For the purpose of signal analysis of accelerograms, the ground motion record generated by a simple penny-shaped rupture is used to form the basis wavelet function. After a careful study of the characteristics of various wavelet functions, the Mexican hat wavelet was found to be the most appropriate wavelet basis function to represent the acceleration of a single point source rupture. The result of the signal processing of an accelerogram is presented in the form of a scalogram using the coefficients of the continuous Mexican hat wavelet transform to describe the signal energy in the time-scale domain. The proposed signal processing methodology can be used to investigate the characteristics of accelerograms recorded on various types of sites and their effects on different types of structures.     

Wavelet packet based damage detection in beam-like structures without baseline modal parameters

In this paper, a new approach for damage detection in beam-like structures is presented. The method can be used without the need for baseline modal parameters of the undamaged structure. Another advantage of the proposed method is that it can be implemented using a small number of sensors. In the proposed technique, the measured dynamic signals are decomposed into the wavelet packet decomposition (WPD) components, the power spectrum density (PSD) of each component is estimated and then a damage localisation indicator is computed to indicate the structural damage. The proposed method is firstly illustrated with a simulated beam and the identified damage is satisfactory with assumed damage. Then, the method is applied to a steel beam. The effect of damage location and the effects of wavelet type and the decomposition level are examined. The results show that the proposed method has great potential in crack detection of beam-like structures.     

Crack Detection from the Slope of the Mode Shape Using Complex Continuous Wavelet Transform

Abstract: A new method for cracks detection in beams is proposed by using the slope of the mode shape to detect cracks, and by introducing the angle coefficients of complex continuous wavelet transform. This study is aimed at detecting the location of the nonpropagating transverse crack. A series of beams with cracks that are simulated by rotational springs with equivalent stiffness are analyzed. The mode shape and the slope of this lumped crack model are calculated. Through complex continuous wavelet transform of the slope of the mode shape using Complex Gaus1 wavelet (CGau1), the locations of cracks are detected from the modulus line and the angle line of wavelet coefficients. By comparison, the singularity is much more apparent from the angle line of complex continuous wavelet transform. This demonstrates that the proposed method outperforms the existing method of wavelet transform of the mode shape with real wavelets. Also, this method can detect cracks in beams with different boundary conditions. The influence of crack locations and crack depth on crack detection is discussed. Finally, the noise effect is studied. Through the multiscale analysis, the locations of cracks may be detected from the angle of wavelet coefficients.     

Wavelet Packet-Autocorrelation Function Method for Traffic Flow Pattern Analysis

Abstract:   Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet-ACF method is proposed for analysis of traffic flow time series and determining its self-similar, singular, and fractal properties. A DWPT-based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic-forecasting models .