基于EVSEWT的下击暴流时变平均风提取

下击暴流等极端风具有强烈的非平稳特性,给风场特性分析、结构响应计算带来挑战。根据经验将非平稳风分为时变平均风和零均值脉动风分别进行分析,是处理非平稳风速信号的有效方法。分析了几种常见时变均值提取方法的优缺点,在此基础上提出一种基于能量波谷寻找的经验小波变换方法,用于下击暴流时变平均风速提取,并将提取的时变平均风和脉动风用于分析某幢高层建筑顶部位移响应。对2组下击暴流的分析结果得出:与经验模态分解和离散小波变换方法相比,基于能量波谷寻找的经验小波变换方法模型提取的时变平均风更符合预期,对应的结构响应偏于安全。     

自行走悬索桥主缆检修车主桁架风振响应特性研究

根据Davenpot功率谱、Wiener-Khintchine定理、Shinozuka定理模拟脉动风时程曲线;使用Monte-Carlo法、有限元分析方法、matlab软件提取计算用脉动风并对主缆检修车主桁架进行风振响应特性研究。研究结果表明:基于蒙特卡洛法提取的计算用脉动风正确有效,该提取方法可应用到相关的工程抗风设计中。在自然风载荷的作用下,主桁架危险点的位移主要产生在侧向与竖向,而且主桁架在风载荷静力作用下的响应计算是可靠的。Davenport风速谱的函数规律决定,在140m高空模拟的最大总风速小于设计风速。     

移动荷载作用下时变简支钢桥损伤识别

针对移动荷载作用下桥梁结构振动响应信号呈现非平稳性的特点,构建新的一阶本征函数自功率谱最大值变化比和一阶本征函数小波能量变化率两个指标来识别时变结构的损伤。首先,采用小波阀值去噪法对时变结构响应信号进行去噪处理;其次,运用解析模态分解定理提取响应信号的一阶本征函数并构建一阶本征函数自功率谱最大值变化比指标来识别结构的损伤位置,在识别结构损伤位置的基础上,将损伤位置处的加速度响应信号的一阶和二阶本征函数进行线性混叠后,采用快速独立成分分析进行分离,得到更有效的一阶本征函数;最后,基于连续小波变换和时间窗思想,提出一阶本征函数小波能量变化率指标来预测结构的时变损伤。通过移动荷载作用下的时变简支钢桥试验验证所提出的损伤指标,研究结果表明,提出的两个指标能够有效识别结构的损伤位置和时变损伤。     

基于小波包变换的梁体损伤识别

由于小波包变换在分析非平稳信号方面较傅立叶变换更为有效，提出了基于小波包变换的能量变化率指标进行损伤识别的方法。首先，将得到的结构响应信号进行小波包分解，然后通过小波包能量变化率指标来进行损伤定位。通过3种不同损伤工况的梁体室内试验证明．损伤指标可以准确地识别损伤位置。     

基于角域经验小波变换的滚动轴承故障诊断

针对变转速条件下滚动轴承故障特征难以提取的问题,提出了一种基于角域经验小波变换的变转速滚动轴承故障诊断方法。该方法首先利用等角度重采样将变转速下非平稳的滚动轴承故障振动信号转化为角域平稳信号,然后应用经验小波变换(Empirical mode decomposition,EWT)对角域平稳信号进行自适应分解,得到若干个经验模态分量,最后选择峭度值最大的经验模态分量进行包络谱分析,提取出滚动轴承故障的阶比特征。为提高经验小波变换的分解效率,对其频谱分割方法进行了改进。滚动轴承故障诊断实例表明,该方法能够有效地抑制噪声等干扰成分的影响,精确提取滚动轴承故障的阶比特征,为变转速条件下的滚动轴承故障诊断提供一种有效方法。     

基于小波包变换的二滩大坝地震响应能量分析

由于地震响应信号具有明显的非平稳特性,为分析其在不同频带的能量分布规律,结合实际地震监测数据,利用小波包分析良好的时频局部化性质和均匀频带划分能力,对二滩大坝地震动响应信号进行研究。首先,简略地对比了小波变换与小波包变换的异同,讨论了基小波选取的侧重点,提出了小波基混频度的概念,并作为选取小波基的评判指标;其次,对坝体不同位置响应信号在不同频段内进行展开,讨论了不同测点、不同方向上响应信号能量在各频带上的分布规律;最后,总结了结构响应信号的能量在频带上的分布与信号的方向及结构的位置关系。研究结果可为大坝的抗震减振以及与能量相关的安全评估方法提供理论基础。     

基于SWT与IEWT的齿轮无转速计阶次跟踪

针对变转速工况下,多级齿轮传动低速级齿轮故障信号易受背景噪声干扰,导致频谱特征模糊,微弱故障特征难以提取的问题,提出一种基于同步压缩小波变换(Synchrosqueezing Wavelet Transform, SWT)与改进经验小波变换(Improved Empirical Wavelet Transform, IEWT)相结合的齿轮无转速计阶次跟踪方法。首先为提高无转速计阶次跟踪瞬时频率估计精度,设计连续小波变换-椭圆时变滤波器(Continue Wavelet Transform-Elliptic Time-Varying Filtering, CWT-ETVF)对齿轮振动信号滤波降噪,依据滤波所得单分量的SWT时频分布进行峰值搜索,以实现高精度的瞬时频率估计,然后对时变故障信号等角度重采样获得角域平稳信号。针对EWT方法频谱分割不合理的问题,提出一种依据频谱包络趋势进行边界划分的改进经验小波变换方法对角域平稳信号自适应分解。最后选择合适分量自相关去噪,并通过阶次解调分析识别故障特征。仿真及实测局部断齿数据分析表明,该方法可以准确提取变转速齿轮时变微弱故障特征。     

基于小波域对数正态模型的滚动轴承故障诊断

针对小波分析无法全面准确描述滚动轴承振动信号的非高斯问题,提出一种结合小波变换与对数正态分布模型的故障特征提取方法,以提取能准确反映滚动轴承运行状态的特征信息。首先,通过小波变换对滚动轴承运行时产生的非平稳、非高斯振动信号进行分解重构,得到不同尺度下的重构信号;然后对重构信号建立对数正态分布模型,提取模型的对数均值和对数标准差作为表征滚动轴承运行状态的统计特征;最后采用支持向量机分类器对提取的特征进行故障分类与识别。实验结果表明,该方法可以有效、准确地识别滚动轴承的运行状态。     

小波消噪及其在氮爆式液压破碎锤测试中的应用

本文简述了小波分析理论及基于小波变换的去噪方法 ,针对氮爆式液压破碎锤测量信号非平稳时变特点 ,对其进行小波分析处理。结果表明 ,经过小波消噪后的信号具有更好的可读性及更佳的分析利用价值     

改进的EWT方法在轴承故障诊断中的应用

为了实现经验小波变换中Fourier谱的自适应分割,提出了一种基于能量的尺度空间经验小波变换(Energy Scale Space Empirical Wavelet Transform,ESEWT)方法,并将此方法应用于轴承故障诊断。首先使用尺度空间的方法对傅里叶谱进行自适应划分,得到各频带分界点;接着根据各频带能量筛选频带分界点,使其保留能量大于均值的频带,合并小于均值的相邻频带;然后在得到有效的频带分界点后,设计小波滤波器组,得到分量信号;最后对各分量信号进行Hilbert变换,提取轴承的故障特征频率。通过实验验证,ESEWT方法能够减少频带分界点,在一定程度上改善了频带破裂现象,并且能够精确提取出轴承故障特征频率,凸显了故障频率及其谐波成分,能有效的识别轴承故障。     

Application of non-stationary signal characteristics using wavelet packet transformation

Although Fourier-based methods have been standard methods for frequency analysis, they are not well suited for the analysis of nonlinear or non-stationary systems due to their time-varying natures. Thus, in this paper, a wavelet packet-based technique, which calculates time-varying coherence functions for input/output relationships, is developed. The developed method uses the Coiflet wavelet that has been widely used in signal processing. It is applied to obtain the time-varying coherence function, and to detect the impulse signal from the impulse-embedded signal such as an automobile sound/vibration signal with an external impact caused by a collision or passing over rough terrain. Some characteristics of non-stationary behavior such as the wavelet packet coefficients, maximum phase plane (MPP) analysis and fault detection are also demonstrated. The method gives promising results of non-stationary input-output systems, and so may be used as an effective tool for condition monitoring or fault detection area. This paper was recommended for publication in revised form by Associate Editor Yeon June Kang Jae-Eung Oh received his B.S. degree of mechanical engineering at Hanyang University in 1975 and his M.S. degree of Safety Engineering at Yokohama National University in 1980. He then went on to receive his doctorate degree in environmental engineering from Tokyo Institute of Technology in 1983. He is currently the Vice President of KSNVE.     

金属跌落零件冲击响应信号的时频分析

分别研究了用平滑Wigner分布和小波变换分析跌落零件冲击信号的方法，比较了各种分析方法的特点。结果表明，这些时频域分析方法有很多优越性，但是也存在一些有待进一步研究的问题。     

Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines,fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification.The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides,the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier Fast ICASVM and could produce the excellent classification results.The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.     

基于LabVIEW的双馈风力发电机组在线谐波分析研究

针对双馈风力发电机组在并网运行时,其电力电子变流装置将向电网注入大量谐波电流的问题,将基于LabVIEW的在线谐波分析技术应用到双馈风力发电机组的谐波监测分析中。开展了针对双馈风力发电机组注入电网谐波的傅里叶分析和小波包分析,提出了将傅里叶变换与小波包变换联合使用的方法;在LabVIEW上搭建了仿真平台,对双馈风力发电机组可能存在的谐波进行了仿真分析试验。试验及研究结果表明,对于平稳信号与非平稳信号进行谐波分析时,将傅里叶变换与小波包变换联合使用,两者的效能得到明显的提高,优于单独使用傅里叶变换或小波包变换,且可更准确地获取基波和谐波的实时波形与频域特性,谐波定位更准确,为谐波补偿提供了更可靠、准确的信息。     

传感器故障后多变量经验小波变换多点预测

为有效应对多点风速传感器或风压传感器故障而造成的损失,同时为了降低运算的复杂性和工程应用的难度,需要提出同步恢复缺失数据的模型。传统的多通道信号诊断采用多元经验模态分解(multivariate empiricalmode decomposition,简称MEMD),笔者提出多变量经验小波变换(multivariable empirical wavelet transform,简称MEWT)来同步恢复多点缺失数据。具体应用时,首先,运用MEWT将多点信号同时分解为一系列模态;然后,利用核函数极限学习机(kernel-based extreme learning machine,简称KELM)实现同步预测,同时运用杜鹃搜索(cuckoo search,简称CS)算法对模型的正则化参数以及核参数进行智能寻优。多步预测时,采用多输入多输出(multi-input multi-output,简称MIMO)策略代替传统的滚动策略。建筑物表面实测多点风压数据和实测多点下击暴流风速数据用于验证模型的可行性。与噪声辅助的多元经验模态分解核函数极限学习机的对比结果表明,该模型能更高精度地同步恢复多点多步信号。     

基于气弹模型风洞试验的输电塔气动阻尼研究

输电塔挂线和不挂线情况下的气动阻尼比是精确进行其风振响应评估的重要参数。基于完全气弹模型风洞试验测试得到的位移与加速度响应时程,结合使用经验模态分解法(empirical mode decomposition,简称EMD)、小波分析和随机减量方法(random decrement technology,简称RDT)以及Hilbert变换识别了输电塔在挂线和不挂线情况下各阶振型结构阻尼比和气动阻尼比,分析输电塔在挂线和不挂线情况下结构阻尼比和气动阻尼比的变化规律。结果显示,输电塔挂线时的结构阻尼比比不挂线情况要大;输电塔的气动阻尼比随各阶振型频率的增加而减小,随着风速的增加而增加。最后,采用最小二乘法拟合得到了在试验风速条件下气动阻尼比的经验公式。     

Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques

Time-frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults.     

二进小波变换在建筑结构振动中的应用

探讨了二进小波变换在建筑结构振动中的应用。提出了一种检测结构受到冲击的新小波方法,利用该方法有效地完成了结构振动特征信号的提取,首先分析了二进小波的定义、特性及检测突变的原理,之后以一典型信号作检测原理的数值模拟,最后通过一框架模型实验对该法进行了验证,研究表明,二进小波变换方法在建筑物受冲击或振动等非平称信号作用时,具有较好的分析效果和实用价值。