基于连续小波变换的信号检测技术与故障诊断

通过分析指出,连续小波变换具有很强的弱信号检测能力,非常适合故障诊断领域。从参数离散到参数优化系统研究了连续小波变换的工程应用方法,建立了“小波熵”的概念,并以此作为基小波参数的择优标准。论文最后把连续小波技术应用在滚动轴承滚道缺陷和齿轮裂纹的识别中,诊断效果十分理想。     

NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING

Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform （DT-CWT） is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio （SNR） than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals.     

小波变换在齿轮局部缺陷诊断中的应用

采用连续小波变换对齿轮振动信号进行分析,检测到齿轮出现局部缺陷时,其振动信号中的幅值突变点。通过对试验数据的分析,说明这种方法可以有效地应用于齿轮的局部缺陷诊断中,并且不需要对初始信号进行时域同步平均。     

基于自适应复平移Morlet小波的轴承包络解调分析方法

针对滚动轴承的传统包络解调分析技术需要人工选择参数的缺点,提出一种自适应包络解调分析方法。该方法针对轴承故障在振动信号中表现为冲击衰减波形的特点,采用复平移Morlet小波实现冲击特征波形的自动提取。同时,基于小波系数峭度值最大的优化策略,给出Morlet小波基函数的中心频率和包络因子的优化方法,从而实现与冲击特征成分的最优匹配,获得较好的包络信号。对模拟信号和实际轴承故障数据的应用分析表明,该方法通过对基函数波形的优化匹配,可以有效地解调出弱故障特征分量,效果优于普通的复平移Morlet小波变换,适合于轴承的早期故障特征提取。     

Hilbert-Huang变换在齿轮裂纹故障诊断中的应用

提出一种基于Hilbert-Huang变换的齿轮裂纹故障诊断的新方法。Hilbert-Huang变换是先把时间序列信号,用经验模态分解方法分解成不同特征时间尺度的固有模态函数,然后经过Hilbert变换获得信号时频分布的一种信号处理新方法,将Hilbert-Huang变换应用于齿轮箱中齿轮故障诊断的研究。齿轮故障实验信号的研究结果表明,Hilbert-Huang变换时频分析方法能有效诊断齿轮的齿根裂纹故障。     

双树复小波和MED在齿轮箱早期故障诊断中的应用

针对齿轮箱轴承早期故障特征信号微弱且受环境噪声影响严重,故障特征信息难以识别的问题,提出了双树复小波变换(dual-tree complex wavelet transform,DT-CWT)和最小熵反褶积(minimum entropy deconvolution,MED)的故障诊断方法。首先对采集到的振动信号进行双树复小波分解,得到几个不同频段的分量,由于噪声的干扰,从各个分量的频谱中很难对故障做出正确的判断。然后对包含故障特征的分量进行最小熵反褶积滤波处理以消除噪声影响,凸显故障特征信息。最后对滤波后的信号进行Hilbert包络谱分析,即可从中准确地识别出轴承的故障特征频率。通过齿轮箱轴承故障模拟实验和工程应用实例分析验证了该方法的有效性与优越性。     

PARAMETERS OPTIMIZATION OF CONTINUOUS WAVELET TRANSFORM AND ITS APPLICATION IN ACOUSTIC EMISSION SIGNAL ANALYSIS OF ROLLING BEARING

Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform (CWT) has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm,an opti-mization strategy for the waveform parameters of the mother wavelet is proposed with wavelet en-tropy as the optimization target. Based on the optimized waveform parameters,the wavelet scalogram is used to analyze the simulated acoustic emission (AE) signal and real AE signal of rolling bearing. The results indicate that the proposed method is useful and efficient to improve the quality of CWT.     

小波变换在摆式列车倾摆控制系统故障诊断中的应用研究

小波变换在故障诊断中得到较广泛的应用，但采用不同的小波，分析结果往往会有很大差异。对常用的正交、半正交、双正交小波提取信号特征的能力进行分析比较，表明半正交B样条小波因具有线性相位和采用较长的分解系列，而具有较好的局部化特性和较小的变换误差，是摆式列车倾摆控制系统故障诊断中的较佳小波基。提出一种新的确定故障诊断阈值的方法，并通过实验证明了方法的有效性，为小波变换在摆式列车实车倾摆控制系统故障诊断中的应用提供理论依据。     

DISCRETE WAVELET TRANSFORM FOR DENOISING RAMAN SPECTRA OF HUMAN SKIN TISSUES USED IN A DISCRIMINANT DIAGNOSTIC ALGORITHM

In this work, we applied the discrete wavelet transform (DWT) method as a denoising tool for dispersive Raman spectra of skin samples, and we compared the results obtained with the low-order polynomial fitting in a discriminating model based on principal components analysis (PCA). We used a set of 50 Raman spectra of skin tissue fragments diagnosed as normal (N) (25 spectra) and basocellular cell carcinoma (BCC) (25 spectra). A denoising procedure using DWT and its inverse was employed, and the resulting spectra were compared to denoising using low-order polynomial fitting and adjacent averaging smoothing. The tissue spectral profile showed changes in the intensity of bands below 1400 cm^?1 for DWT compared to the denoising by polynomial and smoothing. By applying PCA and Mahalanobis distance in both groups processed, we verified that the filtering method does not alter significantly the discrimination of N and BCC tissues. However, the DWT denoising presented an interesting result, which showed the main components after decomposition of the Raman signal used in the reconstruction.