共查询到19条相似文献,搜索用时 453 毫秒
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提出一种新的光电编码器速度测量方法,根据编码器角度测量误差信号中高频分量的渐进特性,通过对角度误差信号进行时频分析,提取编码器的旋转速度。利用功能强大的连续小波变换非平稳信号分析工具,提取角度误差分量的特征。基于连续小波变换,实现了一种称为迭代算法的小波脊提取方法,用于瞬时频率估计。实验结果表明,经过适当的时频分析处理后,角度误差的高频分量可以有效地用于光电编码器的速度测量。该方法能够有效减弱噪声和干扰对测量精度的影响。 相似文献
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瞬变信息提取与机器诊断 总被引:1,自引:0,他引:1
机器的二次信号往往因机器故障产生大量的冲击、摩擦以及运行转速的不稳定、负荷的变化导致非平稳信号的产生.对非平稳信号分析,付氏变换效果不佳,需要研究这类信号的局部时频特征,提取瞬变信息方能准确地诊断.本文介绍处理非平稳信号的新型工具——小波分析、短时付氏变换两种时频分析方法.最后用小波分析、短时付氏变换和付氏变换对机器的实测振动信号进行分析.说明了小波分析、短时付氏变换作为时频分析方法对处理非平稳信号比付氏变换优越. 相似文献
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为了动态、实时地测量光电编码器在变速转动情况下的细分误差,提出了一种莫尔条纹信号的非均匀采样分析与处理方法。利用傅里叶级数原理构造了实际情况下的莫尔条纹信号方程,根据编码器在不同转速下的实时采样,揭示了莫尔条纹信号的非均匀采样特征。鉴于信号采样的非均匀性,采用曲线拟合的最小二乘法重构莫尔条纹信号,利用离散傅里叶变换算法分析重构信号并求出波形参数。通过信号参数与细分误差的关系式,测量了编码器动态细分误差。采用该方法对21位绝对式光电编码器莫尔条纹信号进行了分析和处理,两次测试得到其动态细分极值误差为+3.21″、-4.69″和+3.45″、-4.81″。实验结果表明,该方法可以有效地分析和处理编码器在非匀速转动下产生的变频莫尔条纹信号,精确地测量编码器的动态细分误差,为工作现场编码器误差的实时检测与修正奠定了基础。 相似文献
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非平稳信号瞬时特征提取的谐波小波方法 总被引:3,自引:0,他引:3
分析了非平稳信号的瞬时特征(瞬时频率和瞬时相位),对非平稳信号进行谐波小波变换,建立非平稳信号的谐波小波系数与该信号的瞬时特征之间的关系,提出非平稳信号瞬时特征提取的谐波小波模型和提取方法.通过算例中的线性调频信号和应用实例中的轴承座振动信号的验证表明,该模型与方法具有较好的抗噪能力,对非平稳信号的瞬时特征具有更高的分析精度,能实现非平稳信号中特殊成分的瞬时特征提取.该方法能通过傅里叶变换实现其快速算法,具有算法简单、快速的特点,实现非平稳信号瞬时特征的实时分析. 相似文献
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非平稳振动信号分析中Hilbert-Huang变换的对比研究 总被引:2,自引:1,他引:1
Hilbert-Huang变换是一种信号分析新方法,特别适合于对非平稳信号进行分析。介绍该方法的基本理论,并利用它对一个典型的旋转机械非平稳振动信号进行分析。然后通过与利用短时傅里叶变换和小波变换所得到的分析结果的对比,研究Hilbert—Huang变换在分析一般非平稳振动信号中的优势和缺陷。最后结合实际应用中遇到的问题,简要论述Hilbert—Huang变换中的经验模态分解在分析频率成分非常靠近的复杂信号时的不足和原因。研究结果表明,Hilbert—Huang变换和其他方法相比,具有分辨能力强、自适应分解、物理意义清晰、信息完整、形式简洁和易于精确分析等优点;同时也存在具有端点效应、实时性稍差和难以将复杂信号中特别靠近的频率成分分解为独立的本征模分量的缺陷。 相似文献
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Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques 总被引:1,自引:0,他引:1
Time-frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults. 相似文献
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This paper describes a procedure for analyzing non-stationary (variable frequency content) periodic error signals obtained during accelerating or decelerating motion. This capability is important due to the recent interest in real-time compensation of periodic error for precision positioning systems. In order to apply the spatial Fourier transform to the non-stationary signal, the constant time interval signals are resampled by linear interpolation using a constant spatial interval. Numerical and experimental results are provided for constant acceleration and sinusoidal motion profiles. 相似文献
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基于小波变换的车轮力传感器信号的去噪研究 总被引:1,自引:0,他引:1
在汽车道路试验中,通过多维车轮力传感器(WFT)可以测量每个轮所受的各维力和力矩。在测量过程中,信号会不可避免地受到各种噪声的干扰,而且,在将测量数据从车轮坐标系转换到车辆坐标系时,车轮转角的误差使测量结果产生了更严重的噪声。这些宽带随机噪声严重影响了车辆性能的分析。小波分析是一种信号的时间-尺度分析方法,特别适合于非平稳信号的分析,具有多分辨率分析特性,而且在时频两域都具有表征信号局部特征的能力。针对车轮力信号的特点,在MATLAB环境下编程进行车轮力信号小波变换去噪研究,试验结果表明,在选择了适当的小波基本函数和阈值的情况下,采用小波变换的闻值去噪方法对多维车轮力信号进行去噪处理,可以取得良好的效果。 相似文献
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Seung Kyu Lee Yong Woo Kim Man Hoi Koo Hak In Gimm Hong Hee Yoo 《Journal of Mechanical Science and Technology》2010,24(12):2395-2400
Fast Fourier transform (FFT) has been widely used to analyze distribution patterns of frequency components in dynamic response
signals. Given a stationary dynamic response signal, a fixed frequency distribution pattern can be obtained efficiently using
FFT. If the system of concern is not stationary, however, the frequency distribution pattern varies with time, and the variation
in that pattern cannot be effectively determined via FFT. To overcome this weakness, time-frequency dual-domain signal analysis
methods such as wavelet transform and Hilbert-Huang transform (HHT) have been introduced. HHT has been shown to be particularly
effective in analysis of non-stationary signals obtained from non-linear as well as linear systems. In the present study,
the transient characteristics of a composite panel undergoing high-velocity impact were investigated. The composite panel,
along with the colliding bullet, were modeled using the finite element method. To verify the reliability of the analysis model,
an impact experiment was carried out, which proved that the model provides reliable, similar-to-experimental results. 相似文献
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非平稳及多奇异点的调频料位测量雷达回波中包含虚假回波及噪声,影响料位回波信号检测,导致料位测量精度不高.本文提出了一种基于广义S变换和奇异值分解的料位回波检测与校正方法.首先,将料位变化视作低速运动目标,将料位回波信号与雷达发射信号进行混频解调,并根据回波信号的频率分布特点对广义S变换窗口的变化趋势进行调节.之后对其变换所得到的二维时频系数矩阵利用奇异值分解方法重构系数矩阵,并对其进行广义S逆变换,得到校正后的回波信号.实验结果表明:该方法能够准确检测料位回波信号,在抑制噪声的同时能最大限度保留信号的细节特征,减少虚假回波干扰.料位测量误差不超过4.01%,测量精度可达到0.40%F.S. 相似文献
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隐Markov模型是一个双随机过程,适用于动态过程的时间序列的建模并具有强大的时序模式分类能力,特别适合非平稳、重复再现性不佳的信号分析;小波变换具有多分辨率分析的特点,在时频两域都具有表征信号局部特征的能力。文中将小波变换和隐Markov模型相结合,提出基于小波变换的HMM状态识别法,利用Daubechies小波进行8尺度的小波分解,然后从小波分解结构中提取一维信号的低频系数作为特征向量,将其输入到各个状态HMM来进行训练,其中输出概率最大的状态即是机组运行状态,从而实现状态的识别,实验结果表明该方法很有效。 相似文献
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针对滚动轴承故障诊断中存在的非平稳故障信号的特征提取困难这一难题,提出利用同步压缩小波变换(SWT)对故障信号的监测数据进行处理的方法。首先对信号进行连续小波变换(CWT),其次对小波变换系数进行同步压缩变换(SST),然后对SST系数进行自适应阈值去噪,之后在有效信号数据的频率中心附近进行积分提取,最后用提取到的有效信号进行重构。对实测的滚动轴承故障信号进行处理验证,结果表明,SWT具有较高的信号提取精度以及降噪能力,同时具有较高的时频分辨率,能够将故障信号转换为高分辨率的时频谱,弥补了CWT在这方面的不足。 相似文献