基于频域窗函数的短时傅里叶变换及其在机械冲击特征提取中的应用

提取冲击特征是实现轴承、齿轮故障识别的关键,但是容易受到噪声和其他无关成分的干扰。区别于经典的时频分析方法如短时傅里叶变换和同步压缩小波变换,提出基于频域窗函数的短时傅里叶变换法。利用最大相关峭度反卷积的方法对振动信号进行滤波,使信号的质量得到提高;通过频域窗函数,实现二维时频平面中时间的精准定位和冲击特征的准确识别,进一步锐化了复杂多组分信号的时频脊线。利用所提方法对数值仿真信号和实际轴承故障信号进行分析,验证了所提方法的有效性。     

小波变换在薄膜表面图像信号降噪中的应用

目的：当前,以原子力显微镜为代表的扫描探针显微镜设备可以获取纳米尺度薄膜样品的表面图像,但这些图像存在不同程度的噪声,影响图像质量和信息判断。为了更准确获取这些薄膜表面状态,需要对薄膜样品表面图像数据和信息进行降噪处理。方法结合AFM等设备成像特点以及小波变换的时频局域性特点,在介绍小波变换基本理论和噪声来源分析基础上,提出了一种多层小波分解去噪算法。传统的信号理论是建立在傅里叶变换基础上的,而傅里叶变换作为一种全局性的变化,其有一定的局限性,无法同时表述信号在时域和频域的局部性质,而这些局部特征恰恰是非平稳信号性质最关键的部分。小波变换保留了窗口傅里叶变换局部化的优点,改变了窗口傅里叶变换窗口函数大小固定的缺点。结果原始图像信号的频率在0 Hz到4000 Hz都有分布。通过小波变换后,信号波形更光滑,频谱在500 Hz到2000 Hz之间分布。结论将小波变换应用于薄膜表面图像信号降噪中,通过实验证明通过小波变换可以有效去除信号中的噪声部分。     

基于小波奇异性检测技术的滚动轴承早期故障诊断研究

轴承早期故障的检测与诊断是实现安全生产、预防恶性事故的有效手段。用高精度加速度传感器采集轴承振动信号,采用小波软阈值降噪法剔除测试过程的噪声,提高采集信号的信噪比。基于小波变换奇异值检测技术,探讨了提取淹没在噪声背景中的早期故障特征的方法,同时指出了传统傅里叶变换的不足。研究表明,该方法是有效的,所提取的故障特征频率与理论计算的故障特征频率基本相同。研究结果为轴承早期故障检测与诊断提供了新途径。     

钢管虚拟超声检测信号处理技术

针对表面粗糙的钢管进行超声波检测时存在的噪声高、信噪比低的问题,利用自行搭建的虚拟超声检测信号处理系统,对表面粗糙钢管的超声检测信号分别进行了基于传统傅里叶变换的频谱分析和FIR滤波以及基于小波变换的时频分析和小波去噪。通过对比试验发现,基于小波变换的信号处理技术可以对表面粗糙钢管的超声检测达到更好的去噪效果。     

钢板孔洞涡流检测信号的频谱分析研究

通过对振动噪声和钢板孔洞信号的频谱特征研究与FFT频谱分析，利用短时傅里叶变换：（STFT）进行钢板孔洞的检测实验表明，频谱分析作为常规幅度检测方法的重要补充，可以与信号的幅度分析结合在一起，使信号的识别更加准确与可靠。     

齿轮箱点蚀的在线故障诊断系统

基于傅里叶变换的谱分析具有一定的局限性,是一种全局变换,而小波具有很好的时频分辨能力,将Hilbert变换和小波分析方法结合起来对齿轮箱点蚀故障进行了研究。以JZQ250型齿轮点蚀故障为分析对象,对测得的振动加速度信号进行小波变换和Hilbert变换。为了更好地进行人机交互,利用VC++的强大界面开发能力把其嵌入,实现对数据的分析,并设计一个规则库。结果表明:所设计的界面可行并可有效诊断故障。     

基于短时傅里叶变换和深度卷积神经网络的直升机齿轮箱故障诊断方法

齿轮箱作为直升机重要的传动机构,其运转的可靠性对保障直升机系统安全具有重要的作用.针对传统信号处理需要大量专家经验来识别故障类型的不便性和复杂性,为了实现直升机齿轮箱故障诊断,本研究提出一种基于短时傅里叶变换和深度卷积神经网络的故障诊断方法.首先,将采集到的直升机齿轮箱振动信号利用短时傅里叶变换绘制时频图,以提取振动信...     

基于同步提取变换的旋转机械振动信号时频分析北大核心

为提升工业现场采集摩擦故障振动信号时频分辨率,采用小波变换、短时傅里叶变换、同步挤压变换和同步提取变换(SET)4种时频分析算法对多分量仿真信号和旋转机械的摩擦故障振动信号进行时频分析,并对同步提取变换算法原理进行详细介绍。通过实验与其他3种算法Renyi熵值进行比较。结果表明:针对多分量仿真信号,利用SET算法可有效去除能量发散现象、混叠和端点效应,与原始多分量仿真信号的真实时频谱一致,在时间分辨率和频率分辨率上均实现了最优;针对现场摩擦故障振动信号,利用SET可以很好地抑制摩擦故障信号中的背景噪声,得到的时频图时频聚焦性较好,同时能量发散情况得到极大改善,在时频图中可以清晰地看出摩擦故障信号对应的频率。     

基于小波变换奇异点检测技术的滚动轴承早期故障诊断研究

轴承早期故障的检测与诊断是实现安全生产、预防恶性事故的有效手段。用高精度加速度传感器采集轴承振动信号,采用小波软阈值降噪法剔除测试过程的噪声,提高采集信号的信噪比。基于小波变换奇异值检测技术,探讨了提取淹没在噪声背景中的早期故障特征的方法,同时指出了传统傅里叶变换的不足。研究表明,该方法是有效的,所提取的故障特征频率与理论计算的故障特征频率基本相同。研究结果为轴承早期故障检测与诊断提供了新途径。     

解调同步压缩变换及其应用于变转速滚动轴承故障诊断

为实现对变转速下滚动轴承的故障诊断，提升设备服役的安全性，在传统基于短时傅里叶变换（STFT）的同步压缩变换（FSST）的理论框架上，针对STFT存在的调制成分造成其瞬时频率（IF）估计不准确、时频模糊的问题，提出一种解调短时傅里叶变换（DSTFT），即在STFT中引入解调算子消除调制对信号的影响，获得更加准确的IF估计值。在此基础上，推导基于DSTFT的解调同步压缩变换（DSST），可以通过时频平面变换系数的重排获得能量高度集中的时频表达。所提出的DSST方法具有IF估计准确、时频脊线能量集中性好等优点。通过数值模拟分析和试验台振动信号分析，验证所提方法在变转速滚动轴承故障诊断中的有效性。     

Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines

A fault signal diagnosis technique for internal combustion engines that uses a continuous wavelet transform algorithm is presented in this paper. The use of mechanical vibration and acoustic emission signals for fault diagnosis in rotating machinery has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The conventional diagnosis technology using acoustic and vibration signals already exists in the form of techniques applying the time and frequency domain of signals, and analyzing the difference of signals in the spectrum. Unfortunately, in some applications the performance is limited, such as when a smearing problem arises at various rates of engine revolution, or when the signals caused by a damaged element are buried in broadband background noise. In the present study, a continuous wavelet transform technique for the fault signal diagnosis is proposed. In the experimental work, the proposed continuous wavelet algorithm was used for fault signal diagnosis in an internal combustion engine and its cooling system. The experimental results indicated that the proposed continuous wavelet transform technique is effective in fault signal diagnosis for both experimental cases. Furthermore, a characteristic analysis and experimental comparison of the vibration signal and acoustic emission signal analysis with the proposed algorithm are also presented in this report.     

基于DSP的CO2焊短路信号的小波检测

简单介绍了连续和离散小波变换理论,结合CO2气体保护焊电弧电压信号的特点从理论上论证了小波变换方法在波控CO2焊中检测短路信号并给出短路控制信号的可行性.通过真、假短路信号及噪声信号的频谱分析,利用理论定性分析和数学公式的推导讨论了小波变换消除假短路信号和噪声信号干扰的机理.利用Matlab中的小波分析工具箱对实测所得的电弧电压离散数字信号采用不同的小波基进行小波变换分析,从分析中选出一套切实可行的小波变换数字滤波器组.最后在DSP(数字信号处理器)上编程实现短路信号的实时检测,验证了基于DSP的小波变换提高短路信号检测的实时性、准确性及可靠性.     

Application of the wavelet transform and the enhanced Fourier spectrum in the impact echo test

The objective of this study is to develop a reliable and effective method to analyze the signal of the impact echo test. The impact echo test is a nondestructive testing technique for civil structures. In the test, the surface response of the target structure due to an impact is measured. Then, the Fourier transform is adopted to transform the signal from the time domain to the frequency domain. Owing to the multiple reflections induced by cracks, voids, or other interfaces, peaks will form in the Fourier spectrum. The frequencies of the peaks can then be used to determine the size of the structure or the location of the defect.

Several difficulties are encountered when applying the Fourier transform to impact echo data. Because the impact echo data are non-stationary and contains multiple reflections, ripples and multiple peaks appear in the Fourier spectrum, which may mislead the follow-up diagnosis. Furthermore, the existence of the high-energy surface wave and structural vibrations often complicates the spectrum and makes the data interpretation even more difficult.

To overcome these difficulties, this research adopts the wavelet transform in the analysis of impact echo data. Theoretically, the wavelet transform can avoid ripple and multiple-peak phenomena. Furthermore, the frequency range and time span of surface wave can be observed in the wavelet scalogram. However, the spectral resolution of the wavelet marginal spectrum is inferior to that of the Fourier transform. Therefore, two approaches are proposed in this paper. One is to combine the Fourier spectrum and the wavelet marginal spectrum to determine the precise location of the echo peak. The other is to take the product of the two spectra to establish the enhanced Fourier spectrum. As such, the interference in the Fourier spectrum is suppressed while the peak is enhanced. Numerical and experimental tests were performed to verify the effectiveness and reliability of the proposed approaches.     

滚动轴承微弱故障信号检测的研究

研究了信号局部奇异性在小波变换下的特性.根据故障信号和噪声的局部奇异性在小波变换下模极大值在不同尺度上的传播特性不同的特点,利用小波分解重构算法,对滚动轴承振动信号进行了分解、去噪、重构和谱分析.实验表明,小波减噪方法非常适于滚动轴承微弱故障信号的检测.     

基于CWT-CNN的离心泵轴承故障识别方法

针对传统的轴承故障诊断方法在面对强噪声和非平稳信号识别时特征提取过度依赖先验知识和专家经验等问题,结合传统的信号处理方法和深度学习算法提出一种基于CWT-CNN的离心泵轴承故障识别方法。连续小波变换(CWT)将原始的1D振动信号转化为故障特征信息更丰富的2D时频图,2D时频图再输入到卷积层完成特征的自动提取,最后SoftMax层完成故障识别。经过西储大学公开轴承数据集和实验室搭建的离心泵振动轴承采集实验台验证,该方法的故障识别准确率均能达到90%以上。     

基于小波变换的声发射模拟信号特性分析

小波变换作为一种新的信号处理方法,可对任一时刻、任一频段的声发射信号进行时频分析.通过基于Gaussian方程的Gabor小波及其Fourier变换,研究了Q235B钢板中三种声发射模拟信号的特点及三种信号的区分和识别.试验表明,基于小波变换的信号处理方法对于研究实际声发射信号的特点、声发射源的特征和定位以及传感器选择有一定的帮助.     

On-line detection of the breakage of small diameter drills using current signature wavelet transform

This paper presents on-line tool breakage detection of small diameter drills by monitoring the AC servo motor current. The continuous wavelet transform was used to decompose the spindle AC servo motor current signal and the discrete wavelet transform was used to decompose the feed AC servo motor current signal in time–frequency domain. The tool breakage features were extracted from the decomposed signals. Experimental results show that the proposed monitoring system possessed an excellent on-line capability; in addition, it had a low sensitivity to change of the cutting conditions and high success rate for the detection of the breakage of small diameter drills.