利用小波去噪和HHT的模态参数识别

提出了基于小波去噪和HHT的模态参数识别方法,以改善模态参数识别的精度.该方法先利用小波进行信号去噪,克服噪声对EMD分解的影响,以减少EMD分解过程的计算量和分解层数,对去噪后的信号进行EMD分解提取单模态的自由响应,然后利用自由响应的Hilberr变换识别模态固有频率和阻尼比.利用该方法对某振动台简支梁系统进行了模态参数识别,结果表明在噪声干扰下,该方法识别模态参数的精度较高,特别是阻尼比识别精度高.     

考虑重力影响的索结构损伤行波识别法

因索中某几股断裂而导致索等效截面减小是索结构常见的损伤模式。文中分析此损伤模式下索受扰动后的行波响应，提出重力场中索结构损伤的行波识别法。该方法由索上一点的实测响应数据，根据入射波和反射波到达该点的时间确定损伤位置，根据入射波和反射波的信号能量比及能量的时域分布确定损伤程度和损伤段宽度。为适应现场有噪声测试数据的影响，应用多分辨率分析理论，以Daubechies小波分解行波响应信号，对信号去噪并作出白噪声情况下损伤识别方法的误差估计。仿真结果表明，该方法可采用噪声污染响应信号对重力场中索结构的损伤进行准确的估计，适合小损伤情况下索结构的损伤识别。     

一种工作模态参数的在线时域辨识方法

根据环境激励具有随机性以及线性系统在环境激励下各输出点响应之间的相关函数与系统的脉冲响应函数具有相同的数学表达式等特点，给出了在线模态参数识别的理论，并提出了仅根据环境激励响应识别模态参数的新方法。     

分数阶小波包时频域的信号去噪新方法

为了提高信号去噪的效果,提出了一种基于分数阶小波包变换(FRWPT)的信号去噪新方法。该方法根据输出信号信噪比的大小,用迭代法寻找分数阶小波包变换的最优分数阶p值,通过分数阶小波包变换将带噪信号映射到最优分数阶小波包时频域内,对变换后的信号进行窄带通滤波,最后通过分数阶小波包逆变换对信号进行重构,实现分数阶小波包时频域内的信号去噪。以带噪Bumps信号和语音信号为例的去噪实验结果表明,采用该方法去噪后的信号信噪比明显提高,在抑制噪声的同时可以有效保持细节信息。     

一种结构参数识别法

本文提出一种利用实测固有频率来识别结构参数的方法。在假设质量矩阵计算准确而不需识别的前提下,通过矩阵摄动关系求出刚度矩阵的修正量。该方法计算简单,并有效地解决了振型难以完整测出时的结构参数识别问题。     

一种新的小波阈值函数去噪算法

小波阈值去噪算法是一种经典的振动信号去噪算法,但仍有一定局限性。为了更好地消除噪声对被测振动信号的干扰,提取信号的有用成分,本文对比分析了几种不同小波阈值去噪算法,并在经典小波阈值去噪算法的基础上改进了阈值函数,提出了一种新的小波阈值去噪算法。对模拟信号及实测风机振动信号进行去噪处理并分别与经典及改进的小波阈值去噪效果进行定量比较。结果表明:新的小波阈值函数更好地抑制了噪声污染和保持信号细节,有效地消除了背景噪声,提高了信号特征的可分离性,具有较高的实用价值。     

密集模态分离及其参数识别方法研究

针对密集模态参数识别精度差的问题,分析造成识别精度低的原因.根据影响模态混叠程度的固有频率和阻尼比两大因素,定义衡量模态间混叠程度的模态密集度因子.基于化多模态参数识别问题为单模态参数识别问题的思想,提出加逆衰减指数窗与带通滤波相结合的密集模态分离方法,先将各模态近似提取出来,再用时频分析方法识别模态参数.研究相关处理去噪方法,理论证明小阻尼情况下相关处理去噪的模态参数不变性.通过仿真和实验验证方法的有效性和优越性.     

模态参数识别的时域新方法及其应用

提出了一种用于非同步采样的模态参数识别的时域新方法。该法克服了工程中常用的ＩＴＤ模态参数识别法要求对各测点的自由响应信号同步采样,即多测点时,需要多个Ａ／Ｄ变换器的缺陷。本法只需一个Ａ／Ｄ变换器,即可对测得的多个信号进行循环采样,得到一个时间序列,经济、简便。本文经理论推导,给出模态参数识别计算式,通过计算机仿真验证其正确性,并用于水轮发电机组主要部件的参数识别,得到的结果与频域法识别得到模态参数是一致的。     

基于Morlet小波改善飞行颤振试验参数识别精度的方法

为了解决激励能量有限和现场测试数据量较少、噪声大，系统参数识别的准确度差的问题，采用Morlet小波时频滤波和频域参数识别相结合的方法进行参数识别来提高精度。基于Morlet小波函数建立特性滤波器组进行时频域滤波，讨论滤波参数的选取方法，采用有理正交多项式（RFOP）拟合算法进行频域参数识别，基于欧洲航空界广泛采用的GARTEUR飞机模型数据建立密频模态系统，进行飞行颤振的试验数据仿真。结果表明该方法在信号噪声较大时，可以有效地提高系统参数识别的精度。     

ITD模态参数识别的一种改进方法

本文推荐了一种区分特征值的方法,将ITD法识别的单值频率范围由上半平面扩展到了整个Z平面,并研究了ITD法在整个Z平面上的特性.为了检验ITD法在整个Z平面上的模态识别能力,对一个齿轮箱体进行了实验。在整个Z平面上识别的模态参数优于仅在上半平面所识别的模态参数.     

FEATURE EXTRACTION OF VIBRATION SIGNALS BASED ON WAVELET PACKET TRANSFORM

A method is proposed for the analysis of vibration signals from components of rotating machines, based on the wavelet packet transformation (WPT) and the underlying physical concepts of modulation mechanism. The method provides a finer analysis and better time-frequency localization capabilities than any other analysis methods. Both details and approximations are split into finer components and result in better-localized frequency ranges corresponding to each node of a wavelet packet tree. For the purpose of feature extraction, a hard threshold is given and the energy of the coefficients above the threshold is used, as a criterion for the selection of the best vector. The feature extraction of a vibration signal is accomplished by computing the reconstruction signal and its spectrum. When applied to a rolling bear vibration signal feature extraction, the proposed method can lead to be very effective.     

基于小波熵的空化状态检测与识别

空化是广泛存在且对流体机械危害很大的物理现象.文中将小波熵方法引入空化噪声分析,探讨基于小波熵的空化初生检测和空化状态识别方法,并对小波基的选取和计算窗口宽度及滑动步长对小波熵状态检测和识别性能的影响进行详细讨论.通过数值实验和空化噪声实例表明,小波熵能十分敏感地检测到空化初生及空化状态的改变,可为空化状态的检测和识别提供一种有效识别手段.     

APPLICATION OF WAVELET PACKET ANALYSIS IN DRILL WEAR MONITORING

In this work, an attempt has been made to develop a drill wear monitoring system which is independent to cutting conditions of the drilling process. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. An advanced signal processing technique, the wavelet packet transform has been used on the acquired current signature to extract features for indirect representation to the amount of drill wear. Experimental sensitivity analysis reveals that in comparison to time domain features, wavelet packet features are more sensitive to flank wear and less sensitive to the cutting conditions. A multilayer neural network model has then been developed to correlate the extracted wavelet packet features with drill flank wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the flank wear with acceptable accuracy.     

APPLICATION OF WAVELET PACKET ANALYSIS IN DRILL WEAR MONITORING

In this work, an attempt has been made to develop a drill wear monitoring system which is independent to cutting conditions of the drilling process. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. An advanced signal processing technique, the wavelet packet transform has been used on the acquired current signature to extract features for indirect representation to the amount of drill wear. Experimental sensitivity analysis reveals that in comparison to time domain features, wavelet packet features are more sensitive to flank wear and less sensitive to the cutting conditions. A multilayer neural network model has then been developed to correlate the extracted wavelet packet features with drill flank wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the flank wear with acceptable accuracy.     

基于小波包的遗传神经网络故障诊断系统研究

用遗传算法建立了汽机故障诊断地人工神经网络模型,以小波包分解技术获得的10个频段上的能量为网络的输入模式,对汽机常见的几种故障进行分类训练,并应用于待识别故障样本的识别计算,结果表明该方法在汽机故障诊断中是有效的。     

IDENTIFICATION OF DIESEL FRONT SOUND SOURCE BASED ON CONTINUOUS WAVELET TRANSFORM

Acoustic signals from diesel engines not only colitain useful information but also includeconsiderable noise components. To extract information for condition monitoring purposes the continu-ous wavelet transform (CWT)is used for the characterization of engine acoustics. The charasteristicsof the CWT in terms of the representation of short duration transient signals are reviewed firstlyWavelet selection and CWT implementation are then detailed. With the wavelet transform, the majorsoures of the exterior radiation sound of the engine front are surveyed. The research provides a reli-able basis for engineering practice to reduce vehicle sound level. Furthermore, the idenification resultsof the measured acoustic signals are compared with the identification results of the measured surfacevibration, and good agreement is observed.     

小波分析识别裂纹参数中的噪声影响

鉴于信号和噪声进行多尺度小波变换时,其小波系数模极大值在多尺度上表现出截然不同的性质,直接将悬臂梁含噪声的前四阶模态进行小波分析,从小波系数模极大值在多尺度上的特性可以定位损伤,讨论高阶模态在损伤识别中的优缺点.定义一个和小波系数直接相关的集中因子,建立其与裂纹深度之间的关系,并分析模态中的噪声对裂纹深度识别的影响规律.研究可对实测环境中小波分析识别损伤提供一定的参考.     

全信息小波包分析及其在旋转机械故障诊断中的应用

针对传统旋转机械单通道故障诊断的不足,结合设备状态检测和故障诊断中微弱振动信号难以提取的问题,在介绍全信息技术的基础上,提出新的信号处理方法——全信息小波包分析,用小波包变换对双通道信号分别进行分解,以提取信号中的微弱局部成分,把需要的对应小波包进行重构并用全矢谱技术进行融合,根据融合后的数据进行故障诊断。工程应用实践表明,全信息小波包分析是一种新的、较为实用的信号处理方法。     

子结构界面连接参数识别的波传播法

提出一种基于波传播法识别子结构连接界面刚度和阻尼的新方法。以子结构在平面内的弯曲振动为例,将单元的状态变量与波模式联系起来。通过单元波幅系数的识别,得到连接界面的位移和力向量,再由结点上位移连续及力平衡条件,求得连接界面各个自由度上的动刚度。在识别过程中,将动刚度分离为实部和虚部,以便于连接刚度和阻尼的独立识别。为了提高识别精度,选择一定的频率范围,在每一频率下求得连接界面的刚度和阻尼,最后求出统计平均值作为界面的连接参数。数值仿真算例表明本方法具有良好的识别精度和数值稳定性,是一种极有潜力的参数辨识方法。