小波变换在信号滤波中的应用

概要地叙述了小波、小波包理论,提出了一种用小波包分解和信号重构进行信号滤波的方法.先用小波包分解把信号分解到相邻的不同频率段上,然后用信号重构方法对各个频率段上的信号进行重构.以电机的振动信号为例,说明这种方法可以有效地用于信号的滤波.     

基于小波包分解的转子叶片裂纹故障特征分析

应用小波包分解能对信号高、低频部分局部细化并保留原信号的时域特征,因而具有良好的时频局部化特性,对非平稳信号进行有效识别的技术,从采集到的有叶片裂纹时的振动信号,通过选取适当的频段进行信号重构,提取叶片裂纹故障的特征,从而实现转子叶片裂纹故障诊断。     

基于小波变换的爆破振动信号分解与重构

针对非平稳爆破振动信号采用Fourier变换只能在时域或频域变换的问题,提出将时间和频率结合起来分析非平稳信号的方法--小波分析法.利用小波分析法对同一爆破振动信号采用不同小波基进行分析,说明了差异性.并通过不同的小波基对实测的爆破振动信号的分解与重构,以重构误差的大小确定最优小波基.     

基于小波包分解和支持向量机的石油套管缺陷智能识别

 针对石油套管缺陷超声无损检测(NDT)中缺陷回波的特点,提出了一种基于小波包分解和支持向量机(SVM)的缺陷智能识别新方法。分析了Gabor、小波和小波包3种信号时频变换分解方法的特点,并进行了基于3种方法生成的特征数据可分性比较,确定了小波包分解方法效果最好。根据SVM解决分类问题的原理,采用SVM法对3种时频分解提取的缺陷信号特征数据进行识别。试验表明,基于小波包分解局部熵的特征提取结合SVM模式智能识别的组合方法,可应用于石油套管上的4种典型缺陷的识别。     

对康明期发动机燃烧状况振动测点位置的研究

本文从理论上探讨了用机械振动分析法对康明期发动机的燃烧状况进行监测时，应出现的振动信号的时域，频域波形的情况，并以此为指导在实验中确定了振动的最佳测点位置。     

对康明斯发动机燃烧状况振动测点位置的研究

本文从理论上探讨了用机械振动分析法对康明斯发动机的燃烧状况进行监测时，应出现的振动信号的时域、频域波形的情况，并以此为指导在实验中确定了振动的最佳测点位置。     

板带轧机HAGC系统基于小波变换的传感器故障诊断

 提出了一种基于小波分析的HAGC系统传感器故障诊断方法。通过建立系统的MA模型，建立残差信号，利用离散小波变换对残差信号进行多分辨率分析。通过残差信号的故障高低频特性，提取故障敏感特征，实现了传感器故障时间、故障原因、故障程度的诊断。仿真研究表明，该方法凸现故障特征，时域定位准确，不但适用于突变故障，对渐变故障同样有效。     

基于小波理论的复杂机械振动信号降噪分析

基于小波理论,针对典型旋转往复式机械——汽车发动机振动信号进行了降噪分析研究,提出了适合这类振动信号降噪的分析方法,并通过对实验数据的分析验证了该方法的正确性和有效性.该研究为对汽车发动机振动信号进行降噪处理,进而对其实施故障诊断提供了可靠的保障.     

谐波小波解调法在齿轮箱故障诊断中的应用

在研究谐波小波频段分解与Hilbert解调分析的基础上,提出了基于谐波小波包变换的解调分析法,并详述了其具体实现过程.该方法根据齿轮箱频谱特征与齿轮箱故障特征频率的理论计算值,确定所需提取的特征啮合分量,并在此基础上选择谐波小波包分解层数与所需提取的频带带宽;通过傅里叶变换及其反变换提取出相应的特征啮合分量,然后借助Hilbert算子对提取出的啮合分量进行包络解调分析.采用这种方法对存在磨损及点蚀故障的齿轮箱振动信号进行了分析,结果表明,基于谐波小波包的包络解调法具有精确提取任意频段调制信息的能力,能够为齿轮箱故障源的准确定位及故障程度提供可靠的判断依据.     

小波包分解在超声检测缺陷回波信号处理中的应用

介绍了一种基于小波包分解的缺陷回波多尺度空间能量特征提取方法，对焊缝超声回波信号进行了特征提取，并采用基于距离的类别可分性判据对提取结果进行了评价。     

Roll Eccentricity Compensation Based on AntiAliasing Wavelet Analysis Method

 Roll eccentricity is an important factor causing thickness variations during hot strip rolling and might define the limit of strip thickness control accuracy. An improved multi resolution wavelet transform algorithm was proposed to compensate for the roll eccentricity. The wavelet transform method had good localization characteristics in both the time and frequency domains for signal analysis; however, the wavelet method had a frequency aliasing problem owing to the less than ideal cut off frequency characteristics of wavelets. This made its component reconstruction of an inaccurate signal. To eliminate inherent frequency aliases in the wavelet transform, fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) were combined with the Mallat algorithm. This synthesis was described in detail. Then, the roll eccentricity component was extracted from rolling force signal. An automatic gauge control (AGC) system added with a multi resolution wavelet analyzer was designed. Experimental results showed that the anti aliasing method could greatly restrain the inverse effect of eccentricity and the thickness control accuracy was improved from ±40 μm to ±15 μm.     

Roll Eccentricity Compensation Based on Anti-Alias-sing Wavelet Analysis Method

Roll eccentricity is an important factor causing thickness variations during hot strip rolling and might define the limit of strip thickness control accuracy. An improved multi-resolution wavelet transform algorithm was proposed to compensate for the roll eccentricity. The wavelet transform method had good localization characteristics in both the time and frequency domains for signal analysis; however, the wavelet method had a frequency-aliasing problem owing to the less than ideal cut-off frequency characteristics of wavelets. This made its component reconstruction of an inaccurate signal. To eliminate inherent frequency aliases in the wavelet transform, fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) were combined with the Mallat algorithm. This synthesis was described in detail. Then, the roll eccentricity component was extracted from rolling force signal. An automatic gauge control (AGC) system added with a multi-resolution wavelet analyzer was designed. Experimental results showed that the anti-aliasing method could greatly restrain the inverse effect of eccentricity and the thickness control accuracy was im-proved from ±40 μm to ±15 μm.     

Wavelet Transform Analysis of Open Channel Wake Flows

Wavelet transform analysis offers a new approach to signal processing through its ability to decompose signals in both time and frequency. As such, it is more suited to nonstationary and intermittent signals than traditional Fourier analysis. The first part of this paper provides an introduction to the theory and signal processing properties of both continuous and discrete wavelet transform analysis. An account is then given of the application of wavelet transform analysis to a variety of experimental open channel wake flows. Feature location is undertaken using a continuous wavelet transform, and both turbulent statistical analysis and thresholding of the turbulent signal components are undertaken using a discrete wavelet transform.     

High resolution time-frequency analysis of otoacoustic emissions

High resolution time-frequency analysis of OAE signals evoked by stimuli of different strength was performed by means of the Matching Pursuit algorithm. The method relies on adaptive decomposition of a signal into waveforms of well-defined frequency and time localization. Energy of OAE as a function of time and frequency was evaluated for stimuli strength of 35-80 dB SPL. Dynamic characteristics of the signal were constructed. For strong stimuli decrease of the power of high frequency components was found. Matching Pursuit proved to be a method which offers high resolution parametrisation of OAE in time-frequency space and provides excellent possibilities of investigation of the signal generation mechanisms.     

基于小波包分析的AOD炉喷溅预报特征信息提取

研究了用小波包分析方法从炉口音频信号中提取AOD炉喷溅预报特征信息的方法。采用db10（小波基函数）小波对喷溅发生前的特征信号进行4层小波包分解,结合快速傅里叶变换法及小波尺度谱进行时频特征分析,并研究了其各频带分解信号的能量比例特点。结果表明,喷溅前40s信号的主频值较正常信号有明显降低,0~312Hz与312~625Hz频段信号能量值比例变化显著。而且低频重构信号可以极好地滤除多种现场干扰,说明该时频特征可以作为准确预报喷溅的特征向量。最后,通过实验确定了8个特征向量值并分别与喷溅或正常信号的特征向量进行相关性比较,验证得出相关度0.95可作为喷溅预报的判定阈值。从而实现了喷溅预报特征信号的准确提取并可转化为计算机容易识别的数值特征。     

Asynchronous sequential stimulation. A new signal treatment for cochlear implants

Cochlear implants use a fixed or FO dependent stimulation rate, whatever the sound analysed (vowel or consonant) and its analysis procedure (fixed filters or FFT). We present a procedure which varies as a function of the nature of the input signal. This method is based on FFT analysis using a variable with analysis window. Short windows are applied on the transient part of the signal, providing poor frequency resolution but good time resolution; they accurately permit to follow the speech signal in time during its fast temporal variations. Large windows are applied on the stationary parts of the signal, providing better frequency resolution, but poor time resolution. Transient parts of the speech order this window switching; they are detected using the statistical properties of the FFT; moreover the narrow windowing is coupled with an increase of the stimulation frequency. This strategy has been implemented using the Digisonic cochlear implant software, and clinically assessed on 6 regular cochlear implant users, owing to a consonant-vowel-consonant test. This Asynchronous-Interleaved-Stimulation (AIS) strategy provides the patients with better discrimination than fixed window FFT analysis. The coding protocol are described and results presented.     

基于经验模态分解方法的拉曼光谱信号处理研究

经验模态分解(EMD)方法是一个以信号内在物理频率成分为对象的自适应时频分析方法,而常见的非平稳信号分析方法,比如小波分析,它需要选择小波基,不同小波基的分析结果不同;拉曼光谱信号是典型的非线性和非平稳信号,EMD方法充分地保留了信号本身的非线性和非平稳的特征,适应于拉曼光谱信号的分析。实验在自行研制的拉曼光谱测试平台上获得了原始的拉曼光谱信号,并通过经验模态分解将信号分解成不同频率的10个本征模式分量,信号能量集中在750cm^-1和1500cm^-1左右,最后进行了频率成分分析和去噪处理,并和小波分析方法进行了对比,验证了EMD方法的有效性和实用性,该方法在拉曼光谱信号分析中有较好的应用前景。