基于方向小波变换的边缘检测

讲述了方向小波在图像处理中的应用,介绍了传统小波变换边缘检测方法,详细介绍了方向小波变换边缘的方法步骤,最后通过实例比较得出了方向小波变换比传统的小波变换在图像边缘检测中更优越。     

小波变换在旋转机械故障检测中的应用研究

详述了Fourier变换与小波变换的本质区别,分析了Fourier变换和短时Fourier变换应用于故障检测的不足;介绍了小波变换及其应用于故障检测的优点;指出了小波变换应用于故障检测的理论和方法。     

小波变换在弱信号检测中的应用研究

介绍了小波变换原理,将小波变换用于弱信号检测并与传统的傅里叶变换方法进行了比较,仿真结果表明,小波变换能有效提高信噪比。     

基于小波变换的信号去噪研究

介绍了小波变换理论,系统地研究了小波变换在信号处理尤其是信号滤波去噪方面的应用。根据不同类型的噪音．给出了基于不同小波变换的滤波算法并且对基于小波变换的滤波原理进行了分析。     

基于线调频小波变换的旋转机械故障诊断

小波变换在设备故障信号处理中得到广泛地应用,然而,小波变换只能消除白色噪声,对有色噪声不起作用.线调频小波变换统一了短时Fourier变换和小波变换的时频分析,是信号的时间-频率-尺度变换,能根据信号的特点自适应生成新的时频窗口.它不仅具有小波变换良好的时频局部性特点,而且它的时频窗口比小波变换的时频窗口更加灵活.本文应用线调频小波变换对旋转机械故障信号进行消噪,效果明显.     

基于连续小波变换的汽轮发电机组轴系扭振及其主动控制模拟试验的研究

根据连续小波变换具有较二进离散小波变换和小波包变换更精细的尺度分辨率的特点,提出了适用于工程界的小波变换结果的表述方法,研究了小波分析方法在汽轮发电机组轴系振动及其主动控制模拟试验中的适用性问题。通过对汽轮发电机组轴系振动及其主动控制模拟试验数据的小波分析,得到了较为满意的结论。     

基于小波变换的信号奇异性检测的研究

介绍小波变换的基本理论以及基于小波变换模极大值的信号奇异性检测方法,并在MATLAB下进行了仿真实验。实验表明,小波变换有着傅立叶变换无法比拟的良好效果。     

小波变换在故障诊断中的应用

小波变换是一种日益获得广泛应用的信号分析方法。介绍了小波变换基本原理和利用小波变化来检测信号的奇异特征的原理,证实了小波变化在检测奇异信号方面的有效性。结果表明基于小波变换的去噪方法是非常有效的。     

小波分析在信号处理中的应用研究

小波分析是对傅里叶变换的继承和发展,首先介绍了小波理论产生的实际背景和小波变换的基本原理,特别是多尺度分析理论。最后,通过实验实现了分形信号的多尺度连续小波变换。     

图像小波分解的系数分析及在图像处理中的应用

介绍小波变换的特点及图像小波分解的方法 ,对Cameraman图经小波变换后的系数进行了分析 ,通过实例说明小波变换在图像处理中的应用。     

THEORY AND APPLICATION OF WAVELET ANALYSIS INSTRUMENT LIBRARY

Some new theory and algorithms on wavelet analysis are proposed, including continuous wavelet transform (CWT), discrete wavelet transform (DWT), wavelet package transform (WPT), wavelet denosing and mother wavelet selection, etc. Using the component-based hierarchy mode, the platform for virtual instrument (VI) is constructed, and the functions such as data sampling, data analysis and data present, etc are provided. Subsequently, the wavelet analysis library is designed and developed. The library consists of expert system, experienced database, development platform and abundant wavelet analysis functional module, which together implement general and special wavelet analysis in the field of mechanical engineering, energy source, transportation and biomedicine, etc. Finally, the wavelet analysis virtual instrument library is applied to detect fault called engine knock. Experimental result indicates that the wavelet analysis virtual instrument library can efficiently solve the engineering problem such as detecting engine knock.     

一种改进的谐波小波及其在转子故障诊断中的应用

在Newland谐波小波的基础上,提出一种改进的谐波小波,该改进的谐波小波具有对称的盒形频谱,其伸缩和平移生成的小波族构成L2(R)空间的正交基,在保持Newland谐波小波的优良性质基础上,信号分解与重构更为简单。利用仿真算例研究改进的谐波小波对微弱局部信号及强噪声背景下的有用信号提取能力,并与DB8小波分析进行比较,结果表明,改进的谐波小波具有更好的信号提取效果。针对转子故障信号特征频率通常为转速频率的分数及整数倍特点,采用该改进的谐波小波提取转子故障信号中的倍频分量,利用实测的不平衡、不对中、碰摩及油膜涡动四种转子故障试验数据,提取了故障信号中的0.5、1.0、1.5、2.0、2.5、3.0、3.5、4.0、4.5及5.0等倍频分量,结果验证了方法的有效性。     

基于参数方程的小波基自适应选择

研究了小波基的参数表达问题,得到包含不同参数的小波基的两个参数方程,提出通过对参数方程中参数的搜索来实现信号的自适应小波基分解。从不同应用角度定义了评价小波基分解效果的两个适应度函数,针对适应度与参数的非线性关系,提出了一种改进的遗传算法对小波基参数方程中的参数进行搜索,同时利用适应度函数对搜索到的小波的分析效果进行评价,当适应度达到最大值时就可得到最佳小波基。利用这一算法实现了一个铣削力信号的自适应小波基分解,并与Daubechies小波的分解结果进行了对比,结果表明自适应小波基能够更充分地分离出信号中的有用信息。     

Higher-density dyadic wavelet transform and its application

This paper proposes a higher-density dyadic wavelet transform with two generators, whose corresponding wavelet filters are band-pass and high-pass. The wavelet coefficients at each scale in this case have the same length as the signal. This leads to a new redundant dyadic wavelet transform, which is strictly shift invariant and further increases the sampling in the time dimension. We describe the definition of higher-density dyadic wavelet transform, and discuss the condition of perfect reconstruction of the signal from its wavelet coefficients. The fast implementation algorithm for the proposed transform is given as well. Compared with the higher-density discrete wavelet transform, the proposed transform is shift invariant. Applications into signal denoising indicate that the proposed wavelet transform has better denoising performance than other commonly used wavelet transforms. In the end, various typical wavelet transforms are applied to analyze the vibration signals of two faulty roller bearings, the results show that the proposed wavelet transform can more effectively extract the fault characteristics of the roller bearings than the other wavelet transforms.     

汽车方向盘转角试验数据去噪的小波基选择

针对在采用小波去噪方法对含有噪声的汽车方向盘转角试验数据进行去噪时小波基的选择问题,探索研究了一种适用于汽车方向盘试验数据去噪的小波基选择方法。首先,结合小波基参数特性以及汽车方向盘转角试验数据对处理效果的要求,归纳出适用于汽车方向盘转角试验数据处理的小波基特点,得出Daubechies(dbN)小波基和Symlet(symN)小波基适用于汽车方向盘转角试验数据处理的结论;然后,引入重构因子来评价各阶数下小波基的处理效果,从而确定小波基的阶数,并以某车型进行双移线试验时采集的方向盘转角试验数据为例,计算并比较了db2～db20和sym2～sym8共26个小波基的重构因子大小,得出db5、db6、sym4和sym5小波基较适用于该试验数据的结论;最后,对所选择的小波基进行了处理效果的验证。结果表明:用该方法选出的小波基对该试验数据有较好的处理效果。     

Best wavelet basis for wavelet transforms in acoustic emission signals of concrete damage process

It is critical to select the best wavelet basis for implementing wavelet algorithms. Firstly, the mathematical characteristics of the eight kinds of wavelet basis commonly used in the field of engineering are summarized and compared based on the analysis of the features of acoustic emission signals from concrete damage process. Secondly, wavelet basis suitable for acoustic emission signals in concrete damage are given by the theoretical analysis. Finally, the best wavelet basis for wavelet transforms in acoustic emission signals of concrete damage process are obtained by calculating the energy entropy. The results show that the energy entropy can be used to select the wavelet basis for acoustic emission signals during the process of concrete damage. Among them, the wavelets of Symlets can be used as the optimum wavelet basis.     

Haar wavelet for machine fault diagnosis

Continuous wavelet transform (CWT) is a kind of time–frequency analysis method commonly used in machine fault diagnosis. Unlike Fourier transform, the wavelet in CWT can be selected flexibly. In engineering application, there is a problem of how to select a suitable wavelet. At present, the selecting method mainly depends on the waveform similarity between the signal required to extract and the wavelet. This method is imperfect. For example, Haar wavelet possesses the rectangular waveform in its supporting field and dissimilarity to any component in the machine signal. It is rarely used in machine diagnosis. However, the time–frequency periodicity of Haar wavelet continuous wavelet transform (HCWT) should be useful in revealing the features in signals. In addition, Haar wavelets under different scales have good low-pass filter characteristic in frequency domain, particularly under larger scales, and that can allow HCWT to detect the lower frequency signal. These merits are presented in this paper and applied to diagnose three types of machine faults. Furthermore, in order to verify the effect of Haar wavelet, the diagnosis information obtained by HCWT is compared with that by Morlet wavelet continuous wavelet transform (MCWT), which is popular in machine diagnosis. The results demonstrate that Haar wavelet is also a feasible wavelet in machine fault diagnosis and HCWT can provide abundant graphic features for diagnosis than MCWT.     

基于小波分析的齿轮箱振动信号消噪处理

基于小波变换具有良好时频分析的特点,介绍了小波变换用于去除噪声的原理和方法,建立齿轮箱齿轮人工点蚀故障模拟实验台,通过Matlab对故障振动信号进行小波分析,并且和没有经过信号消噪就进行小波分解进行对比,从而得出对含噪声的非平稳信号进行消噪,再进行小波分解可以较好的提取齿轮点蚀故障特征频率的结论。     

基于软阈值和小波模极大值重构的信号降噪

软阈值小波降噪是一种常用的非平稳信号特征提取方法.为了改进软阈值小波降噪法的性能,提出一种基于软阈值和二进小波变换模极大值的新小波降噪方法.首先,对信号进行二进小波变换,再对小波系数进行软阈值处理;然后,选择由信号产生的小波系数模极大值点;最后,用交替投影算法重建信号.理论分析表明,该方法能有效地降低软阈值小波降噪法的误差下界.仿真试验表明,该方法提高了降噪结果的信噪比,且较好地保留了信号中的奇异性.将该方法和二进小波变换软阈值降噪法结合起来,应用于滚动轴承故障振动信号降噪.结果表明,该方法能有效地提取到信号中的冲击特征.     

小波包熵在设备性能退化评估中的应用

开展设备性能退化评估研究,是制定主动设备维护策略、降低设备维护费用的基础。在设备性能退化过程中,信号成份会逐渐复杂化。本文提出利用小波包熵监测信号的复杂性变化,从而为设备性能退化评估提供可靠的特征向量。为了研究性能退化过程中振动信号的小波包熵的变化规律,使用裂纹转子动力学模型模拟了转子裂纹逐渐增加的过程,并使用仿真数据计算了各个状态下的小波包能量熵和小波包奇异值熵值。分析结果表明,随着转子性能退化程度的加深,小波包熵值逐渐增加,且对于性能恶化的突变较为敏感。