基于应变模态小波变换的框架结构损伤识别研究

采用应变模态的小波变换方法研究了框架结构的损伤识别问题。以有限元分析求解含裂缝平面框架应变模态为基础,利用Guass2小波对框架的应变模态进行小波变换,再用db3小波对应变模态小波变换系数进行去噪处理,最后通过对去噪处理后的小波系数模极大值点来识别框架结构裂缝的位置,建立了基于应变模态小波变换识别平面框架损伤的方法。以一层平面框架为例,分别给出了框架梁含有裂缝、框架柱含有裂缝、框架梁和柱均含有裂缝的有限元模型,计算得到结构的应变模态,并通过应变模态小波分析来识别平面框架裂缝的位置。从识别结果发现,经小波去噪处理后应变模态小波系数的模极大值点能够有效识别框架结构的损伤,数值计算验证了方法的有效性。本文研究对工程结构损伤诊断有参考价值。     

基于多尺度几何变换的导航雷达图象去噪仿真

导航雷达在采集、传输和显示过程中,由于多种因素的影响导致最终形成的图像中含有大量的噪声,影响了使用者对导航信息的分析和应用.传统的雷达图像去噪算法大多采用小波变换,但这种方法存在边缘模糊等问题.为了去除导航图像的噪声并解决小波变换中存在的边缘模糊问题,本文提出用基于多尺度几何变换的图像去噪方法对导航雷达图像进行处理,并利用基于多尺度几何变换的方法(包括基于Curvelet系数维纳滤波去噪方法和基于Contourlet域去噪方法)和基于小波变换的BayesShrink方法分别对含有模拟杂波和噪声的导航雷达图像进行仿真实验.实验结果表明:与基于小波变换的图像去噪方法相比,基于多尺度几何变换去噪方法能够更加有效去除雷达杂波和噪声.     

基于小波变换的精密测试转台测角系统的故障诊断

针对精密测试转台测角系统的突变性故障、饱和故障和高频干扰故障,研究并提出了一种基于离散小波变换的故障特征提取方法。这种方法采用db4小波函数对测角系统激磁信号进行小波变换,将原始信号分解为近似信号和细节信号。根据分解得到第一层的细节信号,可以定位突变性故障和饱和故障。通过多层分解,确定近似信号和细节信号的最大幅值,根据信噪比大小可以确定是否存在高频干扰故障。实验结果显示,通过该方法可以实现对测角系统激磁电源故障准确的故障特征提取和定位,提高了精密测试转台的安全性和可靠性。     

一种基于小波变换的故障诊断改进算法

为提高多传感器组合导航系统对各导航传感器的在线故障检测能力，提出了一种基于调频高斯小波变换的导航传感器故障诊断改进算法。该算法在分析调频高斯小波特性的基础上，采用高斯小波变换计算出观测量的小波系数后，然后利用带遗忘因子的数据平滑算法对小波系数进行平滑，通过判断平滑值来诊断导航工作正常与否。其优点是仅利用传感器的观测量来直接检测导航传感器故障，适当选择小波变换的拉伸因子和数据的衰减因子可以对方差突变等软故障进行有效的在线检测，并解决了误检问题。仿真结果证明了该算法的有效性。     

基于改进SPECK的雷达图像无损压缩算法

针对VDR雷达图像的无损压缩技术要求高速、高效、低内存占用量的特点,在分析图像整数小波变换的基础上,研究一种基于整数小波和改进SPECK快速无损压缩算法.该算法利用整数小波对图像进行变换,进而采用SPECK进行编码.SPECK算法采用易于计算和并行处理的块结构,不仅提高了编码速度,还具有动态存储小、容错性好等特点.算法中还引入了哈希表和综合匹配法对SPECK算法进行了改进.通过对VDR雷达图像的无损压缩实验证明,该算法不仅提高了压缩速度,而且提高了压缩比,具有广泛的应用价值.     

连续小波变换离散化的爆炸振动特征分析

应用连续小波变换的离散化关系,针对一个改进的L-P（littlewood-paley）小波基函数,给出了一种实现频率完全分割的时频特征分析方法,并对爆炸振动时频特征进行了研究。80 kg TNT地面爆炸时地面垂向振动速度的时间能量密度分布情况表明,在质点振动峰值速度到达时刻爆炸振动的频率范围比较宽,而其他时刻的振动频率相对较为集中,时频能量分布的峰值正好对应于爆炸振动速度的峰值到达时间。基于小波变换的爆炸振动频谱特征与Fourier变换的结果具有良好的一致性。此外,还给出了利用小波变换结果建立爆炸振动随机演变理论模型的基本方法。     

基于小波变换的陀螺漂移建模与实验研究

在基于小波变换陀螺漂移建模方法的基础上对其进行了实验研究,详细阐述了实验的步骤,提出了有关工程问题的解决方法。实验结果表明,基于小波变换的陀螺漂移建模方法的精度高且明显优于传统方法。     

基于小波包变换的梁式结构损伤定位方法

本文针对梁式结构,基于小波包变换理论,提出一种新的损伤定位方法。该方法利用小波包变换将结构的响应分解到不同的模态,引入曲率公式定义了新的小波能量指标,使得能量指标对损伤位置更加敏感。根据工程实际,构造了对应于结构区域的损伤指标。利用D-S证据理论对得到的各阶损伤指标进行数据融合,从而优化了识别结果。通过数值模拟和实验,对新提出的方法进行了验证,结果表明该方法有效,并可应用于工程实际。     

基于小波变换的MIMU随机误差建模

微机电惯性传感器的输出具有噪声大、漂移强的特性,因此必须建立合理的随机误差模型,以便使用滤波算法进行补偿,减小其对系统精度的影响。本文使用艾伦方差分析法分析了ADI公司的MIMU——ADIS16355的静态输出信号,指出按照自相关特性,可以将微机电惯性传感器的随机误差分为白噪声成份和有色噪声成份,并基于此提出了基于小波变换的噪声分离预处理和AR时间序列建模算法的微机电惯性传感器随机误差复合建模方法。使用该算法对ADI公司的微机电惯性测量单元ADIS16355进行了随机误差建模,得到了该MIMU的AR时间序列随机误差模型。仿真结果表明,相较于传统的一阶马尔科夫近似随机误差模型,基于小波变换的AR时间序列随机误差建模方法能够有效滤除白噪声对于时间序列分析建模的影响,获得精度更高的随机误差模型。     

基于小波方差的MEMS IMU随机误差模型间接估计方法

MEMS IMU(Mirco Electro Mechanical System Inertial Measurement Unit)微机电惯性测量模块广泛应用于组合导航系统,其中MEMS IMU随机误差模型的准确性对导航精度有着重要的影响。针对Allan方差法在估计随机误差模型方面的不足,研究了间接估计方法。该方法以Daubechies离散小波变换与间接推断原理为基础,根据小波系数的零均值平稳特征,对分解尺度进行确定,将小波方差作为间接估计辅助参数,分析了最优估计准则的渐近一致性,最后使用高斯牛顿法对估计结果进行校正,获得满足渐近一致性的随机误差模型参数估计结果。仿真结果表明,间接估计方法提高了随机误差模型的估计精度,其中一阶马尔科夫过程的相关时间估计精度提高了12.383%,解决了一阶马尔科夫过程模型的准确估计问题。通过试验结果分析,进一步证明了以上结论。     

Fault Detection in DC Electro Motors Using the Continuous Wavelet Transform

Two time–frequency methods were used to detect typical faults in DC electro motors: the windowed Fourier transform and the continuous wavelet transform. Four groups containing three electro motors each were manufactured with typical faults and examined. These faults included a bearing fault, an increased unbalance, a fragmented brush and a fragmented collector. The velocity of the vibrations at selected points on the electro motors was measured with a laser probe. The parameters of both transforms were selected in order to make both methods comparable. Because of the poor frequency variance, the windowed Fourier transform was, in this case, proven to be inferior to the continuous wavelet transform. Therefore, the continuous wavelet transform was chosen as the primary tool for fault detection.Three criteria were found that successfully discriminated between the typical faults. These were the highest magnitude level, the frequency of the first and second harmonics and the time period between the magnitude pulses in the third (highest) frequency region. If the maximum magnitude levels versus the period of the pulses in the third frequency range are plotted, four distinct regions corresponding to four different faults are obtained. Since the regions do not overlap, linear classifiers can be used with the presented criteria.     

Wavelet‐based data compression for flow simulation on block‐structured Cartesian mesh

A data compression method based on image encoding techniques is presented for a flow simulation data set. An input flow field data set is converted into the octree structure by discrete wavelet transform, and then quantized finely or coarsely depending on its importance in the flow field. Embedded zerotree wavelet encoding as the image encoding technique and entropy encoding reduce the data size by making use of the octree structure created previously. The present compression method is incorporated in a block‐structured Cartesian mesh method called Building‐Cube method. The Building‐Cube method gives not only good performance in the flow simulation but also consistency with the embedded zerotree wavelet encoding in the data compression. Three compression cases for incompressible and compressible flow simulations, including a large‐scale simulation with O(10 ⁸) mesh points, demonstrate that the present compression method gives both high compression ratios and good qualities of compressed data. Copyright © 2013 John Wiley & Sons, Ltd.     

Application of multi-dimensional wavelet transform to fluid mechanics

This paper first reviews the application research works of wavelet transform on the fluid mechanics. Then the theories of continuous wavelet transform and multi-dimensional orthogonal(discrete) wavelet transform, including wavelet multiresolution analysis, are introduced. At last the applications of wavelet transform on 2 D and 3 D turbulent wakes and turbulent boundary layer flows are described based on the hot-wire, 2 D particle image velocimetry(PIV) and 3 D tomographic PIV.     

一种新的小波及其在结构损伤检测中的应用

结构受冲击将引起某些部位的震荡、裂纹甚至断裂,从而导致结构刚度下降.此时振动信号会出现奇异性或携带突变信息,而这些突变信息反映了结构的损伤情况.加之冲击信号测试环境一般较为复杂,环境噪声对测试存在一定的影响.然而,利用合理的小波变换技术去噪可以识别出损伤情况.本文通过对冲击振动的信号特性进行分析构造了一种新的基本小波,并论证了该小波的基本特征.最后运用小波分析的信号奇异检测理论结合实例将该小波用于冲击振动仿真分析中.计算结果表明,利用该小波进行结构损伤信号分析能清晰地识别结构损伤的时刻.采用该小波函数的构造方法,可避免通常采用Daubechies系列小波作为信号分析而要繁琐地选择不同小波函数的过程.     

Determination of the optimal Gabor wavelet shape for the best time-frequency localization using the entropy concept

The continuous Gabor wavelet transform (GWT) has been utilized as an effective and powerful time-frequency analysis tool for identifying the rapidly-varying characteristics of some dispersive wave signals. The effectiveness of the GWT is strongly influenced by the wavelet shape that controls the time-frequency localization property. Therefore, it is very important to choose the right Gabor wavelet shape for given signals. Because the characteristics of signals are rarely known in advance, the determination of the optimal shape is usually difficult. Based on this observation, we aim at developing a systematic method to determine the signal-dependent shape of the Gabor wavelet for the best time-frequency localization. To find the optimal Gabor wavelet shape, we employ the notion of the Shannon entropy that measures the extent of signal energy concentration in the time-frequency plane. To verify the validity of the present approach, we analyze a set of elastic bending wave signals generated by an impact in a solid cylinder.     

Determination of the minimum sample size for the transmission load of a wheel loader based on multi-criteria decision-making technology

The present paper aims to provide a new approach in estimating the minimum sample size of the transmission load of a wheel loader under multiple operating conditions based on multi-criteria decision-making (MCMD) technology. Extreme load values (ELVs) and load cycles under multi-operating conditions are carefully considered, and the mean and the standard deviation of ELVs and the fatigue life are the three criteria selected for estimating the sample size. Using MCMD, the weight values of the three criteria are determined, where the eigenvector and entropy information methods, together with linear combination weighting, are adopted. The optimal minimum sample size (MSS) is estimated based on the feasible values determined by the three criteria and their corresponding weight values. As an example, the load time history of the semi-axle of a wheel loader is analyzed in detail. As the ELVs and load cycles are studied, the optimal MSS can properly represent the load characteristics. The objectivity and validity of the optimal MSS are assured using the combination of the eigenvector and entropy information methods.     

基于Haar小波变换的位移场测量方法

本文提出了一种应用小波变换对固体表面位移场进行测量的新方法，在图像分析时，用各点属于不同尺度的小波变换系数来表征该点周围的子区。将试件表面位移前后的两幅数字图像进行小波变换，通过变形前后两幅图像小波变换系数之间的相互匹配，使位移前后两幅图中的子区对应起来，从而确定图像的位移场。本文应用Haar小波变换进行了计算机模拟实验和实物位移实验。引入亚像素技术，获得了0．02的位移测量精度。     

Fourier and wavelet analyses for fatigue assessment of concrete beams

We investigate damage detection in a simply-supported pre-stressed beam. A crack was propagated by fatigue loads, which were applied up to two million cycles. Both fast Fourier transform (FFT) and continuous wavelet transform (CWT) are used in the analysis of the structural response to impulse loads. The acceleration response of the full-scale beam was measured each time a certain number of cycles of fatigue loads were applied. The results of this study show that both methods can clearly identify the crack growth induced by fatigue loads. The natural frequencies found by FFT are sensitive to the crack progression. The results from the CWT analysis show a clear difference in structural responses between the initial and damaged states of the structure. The response accelerations are de-noised by a soft-thresholding method before they are analyzed by CWT. In addition to the frequency components, the CWT shows the moment in time when particular frequencies occur. Therefore, wavelet analysis has the potential of becoming an effective tool for damage detection and health monitoring of structures for which the natural frequencies are irregularly changing. As the crack grows, the magnitude of ridges obtained by CWT analysis decreases significantly, which indicates the reduction in structural stiffness.     

A novel spectrogram visual security encryption algorithm based on block compressed sensing and five-dimensional chaotic system

Based on block compressed sensing theory, combined with a five-dimensional chaotic system, we propose and analyze a novel spectrogram visual security encryption algorithm. This research is devoted to solving the compression, encryption and steganography problems of spectrograms involving large data volumes and high complexity. First, the discrete wavelet transform is applied to the spectrogram to generate the coefficient matrix. Then, block compressed sensing is applied to compress and preencrypt the spectrogram. Second, we design a new five-dimensional chaotic system. Then, several typical evaluation methods, such as the phase diagram, Lyapunov exponent, bifurcation diagram and sample entropy, are applied to deeply analyze the chaotic behavior and dynamic performance of the system. Moreover, the corresponding Simulink model has been built, which proves the realizability of the chaotic system. Importantly, the measurement matrix required for compressed sensing is constructed by the chaotic sequence. Third, dynamic Josephus scrambling and annular diffusion are performed on the secret image to obtain the cipher image. Finally, an improved least significant bit embedding method and alpha channel synchronous embedding are designed to obtain a steganographic image with visual security properties. To make the initial keys of each image completely different from other images, the required keys are produced using the SHA-256 algorithm. The experimental results confirm that the visual security cryptosystem designed in this study has better compression performance, visual security and reconstruction quality. Furthermore, it is able to effectively defend against a variety of conventional attack methods, such as statistical attacks and entropy attacks.