基于小波变换方法的材料小阻尼识别

基于连续小波变换实现了对振动系统的小阻尼识别。该方法基本原理为通过对系统自由衰减响应的小波系数取极大获取小波脊,借助小波脊理论确定系统的各阶模态频率与指数衰减系数iζωni,采取求中值的方法获取衰减稳定段的参数,进而识别出各阶模态阻尼比。与HT法相比,该方法具有较高的稳定性和识别精度,仿真结果表明,无噪声时识别误差为0.023%,30%噪声时识别的最大误差为3.49%,而且研究中发现阻尼识别的误差与信号采样频率相关。进行了LY12材料的阻尼测试试验,根据试验测试数据对阻尼识别的结果,证明了本文方法的有效性。     

基于贝叶斯频谱估计的频率分辨率研究

研究了贝叶斯频谱估计的基本理论和基本方法,分别对频率扫描法和频差扫描法进行了研究和计算。仿真结果显示,采用加窗方法可有效提高频率扫描法的频率识别精度。在采样信号相同的条件下,频率扫描法的频率采样点数为1000,得到的频率分辨率为1%。差频扫描法的采样点数为100,得到的频率分辨率为0.1%。贝叶斯频谱分析中模型的选取是决定性的因素。     

近红外可穿戴设备中脉搏波的呼吸率检测

针对可穿戴设备中光电容积脉搏波检测呼吸速率准确性不高和实时性不够的问题,提出了一种基于时频谱的自适应信号分解算法。该算法采用瞬时中心频率估计方法获得脉搏波时频谱和瞬时心率估计值,对脉搏信号进行相干解调提取呼吸信号成分,进而利用呼吸信号成分检测呼吸速率。实验结果表明,与传统连续小波变换方法相比,本文提出的自适应信号分解算法的呼吸速率计算时间提高了84.68%。通过中位数误差及四分位距误差的方差分析,表明该算法比连续小波分解算法和自回归模型算法具有更好的计算精度,中位数误差均值分别提高了96.001%和97.978%,四分位距误差均值分别提高了75.014%和52.732%。     

基于希尔伯特变换的核电站松动部件定位方法

针对当前的核电站松动部件定位方法在估算信号到达时间差时存在起始点难以准确判定、误差大、抗噪声性能差的缺点,提出一种基于希尔伯特变换的时间差估计方法,从而根据时间差估算松动部件位置实现定位。希尔伯特变换可提取信号轮廓线,以轮廓线的第一个峰值到达时间作为能量最大弯曲波到达传感器的时间;分析信号的主频率可以计算出弯曲波的传播速度,根据波速和信号到达不同传感器之间的时间差使用三角形定位法实现松动部件的定位分析。搭建平板试验台,在不同信噪比条件下和不同方法之间进行对比试验,结果显示这种方法的平均估计偏差为34.1 mm,在信噪比为1 dB时仍然能够得到比较准确的结果,具有定位精度高、抗噪声性能好、运算速度快等特点。     

循环谱与随机森林融合改进频谱检测算法

针对低信噪比下无线认知网络的主用户频谱检测问题,提出了一种基于循环谱和随机森林的检测方法。该方法首先将信号变换到循环谱域,计算信号非零循环频率最大时的信号循环谱,并以循环谱的均值和方差构成特征向量。然后,基于随机森林构建无线认知网络主用户信号频谱检测模型,并分别构建正负样本信号进行模型训练。最后利用训练完成的模型检测无线认知网络主用户信号。仿真实验结果表明,该方法能够有效实现主用户信号频谱检测,且检测性能优于目前常用算法。     

基于样本熵的改进小波包阈值去噪算法

为了更好地消除噪声对被测振动信号的干扰,分析了样本熵算法与噪声的关系,提出了一种基于样本熵的改进小波包阈值去噪算法。在阈值函数方面,该方法利用样本熵作为特征参数,对含噪信号小波包系数的噪声分布进行表征,且依据此特征参数值对阈值函数进行改进,使其能够根据信号的小波包系数受噪声影响的情况进行自适应的调整;在阈值选取方面,定义去噪后信号与原始信号之差作为噪声信号的估计,利用样本熵作为判别依据,选取使得噪声估计的样本熵值最大的阈值作为最优阈值。该方法与其他方法进行对比,结果表明,该方法能够有效地去除噪声且更好地还原信号的频率特征,是一种更为优越的去噪算法。     

FFT+FT离散频谱校正法参数估计精度

研究用FFT谱连续细化傅里叶变换分析法进行离散频谱校正时的参数估计误差。分析无噪声情况下频率﹑相位﹑幅值的估计误差随细化倍数的变化规律,估计精度随细化倍数的增大而提高,当细化倍数大于40时,最大估计误差几乎可忽略不计。在高斯白噪声的影响下,细化后频谱序列最大值找错的概率随细化倍数的增加而增加,综合考虑频率分辨率对频率估计精度的影响及频谱序列最大值找错的概率,提出用归一化频率估计综合误差和归一化频率估计最大可能误差两个指标评价此校正法对频率的估计精度,并基于此给出不同信噪比条件下的最优细化倍数。采用非线性最小二乘拟合法对噪声影响下的FFT谱连续细化傅里叶变换分析校正法进行改进,通过仿真模拟验证改进后该校正方法具备更高的校正精度和抗噪能力。     

基于MSP430的变点数FFT算法研究与实现

介绍了一种基于MSP430单片机和变采样点数的FFT算法在涡街流量计中的应用。针对涡街流量信号的特点,在测定的频率范围内采用变换采样点数的FFT算法,并由实时测定和计算出的频率区域确定下次的采样点数。通过数据验证和误差分析,证明了变换采样点数的FFT算法可行,加强了流量计抑制噪声的能力,同时最大限度满足了实时性的要求,从而增加了流量计的性价比。     

2GSPS数字存储示波器插值算法

数字存储示波器在快时基档位工作时,由于实时采样率不够,仅能采集信号的少数样点,为了重构信号,必须进行插值。通过编程和仿真比较了三次样条插值、分段线性插值、三次插值、最邻近插值对几种不同波形的恢复结果,并计算和分析了相关的均方误差。考虑实际待测的信号的不可知性和复杂性,并权衡精度与速度,当待插信号的点数小于100点时,选用三次样条插值;当待插信号的点数不小于100点时,采用三次插值法。Matlab仿真表明,设计结果的各项性能指标均达到指定要求,设计过程简便易行。     

基于可触发环形振荡器的高精度时间间隔测量

提出了一种新的高精度时间间隔测量方法,该方法利用代表事件的短脉冲去触发一个高速环形振荡电路,产生一个与该事件同步的时钟信号,该时钟信号随后被用作模-数转换器(ADC)的采样时钟去采样一个正弦参考信号。因此,两个事件之间的时间间隔被映射成正弦参考信号上的两个点之间的初始相位差,随后对有限数量的样本进行全相位快速傅里叶变换(ap FFT)运算,准确地计算出这个初始相位差,进而可以准确获得两个事件之间的时间间隔。该测量方法降低了工程实现的难度,当正弦参考信号的频率为10 MHz,ADC的采样频率为133 MHz、分辨率为12 bits,ap FFT运算点数为4 096时,可以获得约2. 8 ps rms的单次测量精度和约1ps的时间分辨率,误差分布接近正态,实验结果与基于理论分析的误差范围一致。     

从光电容积图中提取脉搏率、呼吸率和心率的新方法

光电容积描记图(PPG)是一种利用光学技术无创检测人体心血管脉搏波的方法。PPG信号来源于MIMIC数据库,它含有许多生理信息。本文提出了将集合经验模态分解(EEMD)、倒谱、快速傅里叶变化和过零点检测相结合的方法,从PPG中可靠地估算脉搏率(PR)、心率(HR)和呼吸频率(RR)。首先,将PPG信号通过EEMD分解为有限个固有模态函数(IMF)。因为EEMD有自适应滤波特性,所以估算不同的生理参数时,可以用不同的IMF分量来重构信号。其次,估算脉搏率时,舍去低频含有伪迹的IMF,再通过过零点检测可以得到瞬时脉搏率。然后,估算心率时,用1 Hz~1.67 Hz(60次/分钟~100次/分钟)的IMF来重构信号,再求倒谱,选取反映心脏活动的频带来得到心率。最后,估算呼吸率时,用0.05 Hz~0.75 Hz(3次/分钟~45次/分钟)的IMF来重构信号,然后对呼吸信号求快速傅里叶变化得到频谱图,寻找频谱图中的峰值得到呼吸率。对来自MIMIC数据库的53个成人PPG信号进行了仿真。仿真结果表明使用该综合信号处理方法提取的生理参数与实际生理参数一致,且该方法有运算量小,精确度高的优点(误差不超过1.17%)。     

PHASE DIFFERENCE CORRECTION METHOD FOR PHASE AND FREQUENCY IN SPECTRAL ANALYSIS

A new method, phase difference corrections method is developed to correct the frequency and phase of spectrum peak. The continuous time-domain signal is separated into two segments and fast Fourier translation (FFT) is carried out for them, respectively. The frequency and phase are corrected using the phase difference of corresponding discrete spectral lines. Furthermore, the amplitude can also be rectified using the formula of window function spectrum. This method, with good adaptability, high speed and accuracy, is theoretically simple. It can resolve the frequency by means of phase difference directly without the formula of window function. Simulation shows that the single-component frequency, phase and amplitude of theoretical signal can be corrected satisfactorily, with frequency error less than 0.0002 frequency resolution, phase 0.1° and amplitude 0.0002. If the signal involves noise, the mean corrected errors are less than 0.001 frequency resolution, 1° for phase, and 0.01 for amplitude, respectively, and the maximum corrected errors of one segment are less than 0.01 frequency resolution, 1° and 0.03, respectively.     

Hilbert-Huang Transform (HHT) transient analysis of composite panel undergoing high-velocity impact

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.     

ARMA MODELS FOR MODAL ANALYSIS: EFFECT OF MODEL ORDERS AND SAMPLING FREQUENCY

Modal analysis is usually conducted in the frequency domain. If frequency domain methods work very well when damping is low, noise level is low and natural frequencies are not too much closed, these methods however by requiring an averaging of the samples, are always time-consuming. On the other hand, time-series analysis are very attractive because samples with very short length are sufficient, but these time-series methods are actually not user friendly. This paper compares the accuracy of three auto-regressive moving average methods (recursive least-squares, output error and corrected covariance matrix methods) for identifying modal parameters of mechanical systems. These methods are applied to industrial structures both from numerical simulations and from experimental measurements. It is shown that these methods are very sensitive to the sampling frequency and that an optimal sampling frequency must be selected in order to have confidence in the identification. When sampling frequency chosen is too high, the order of the model must be increased which leads to a lack of accuracy. A sampling frequency selected approximately between three and ten times the maximal frequency of interest was revealed as acceptable while oversampling led to false results. The output error method was the less accurate one for the studied cases, especially for the damping rate identification. Elsewhere the corrected covariance matrix method revealed as the more accurate, the less sensitive to sampling frequency and the more stable method according to the selected order. The corrected covariance matrix method was applied to a complex industrial application with completely unknown dynamic behaviour and number of degrees of freedom. The application of the corrected covariance matrix method allowed to find the number of frequencies in a specific bandwidth and furthermore to identify the modal parameters.     

Anti-Noise Performance of the FT Continuous Zoom Analysis Method for Discrete Spectrum

As a discrete spectrum correction method,the Fourier transform(FT) continuous zoom analysis method is widely used in vibration signal analysis,but little effort had been made on this method's anti-noise performance.It is widely believed that the analysis accuracy of the method can be substantially improved by increasing the zoom multiple,however,with the zoom multiple increases,the frequency estimation accuracy may decline sometimes in practices.Aiming at the problems above,this paper analyzes the sources of frequency estimation error when a harmonic signal mixed with and without noise is processed using the FT continuous zoom analysis.According to the characteristics that the local maximum of the zoom spectrum may be wrongly selected when the signal is corrupted with noise,the number of wrongly selected spectrum lines is deduced under different signal-to-noise ratio and local zoom multiple,and then the maximum frequency estimation error is given accordingly.The validity of the presented analysis is confirmed by simulations results.The frequency estimation accuracy of this method will not improve any more under the influence of noise,and there is a best zoom multiple,when the zoom multiple is larger than the best zoom multiple;the maximum frequency estimation error will fluctuate back and forth.The best zoom multiple curves under different signal-to-noise ratios given provide a theoretical basis for the choice of the appropriate zoom multiples of the FT continuous zoom analysis method in engineering applications.     

小波降噪技术在柴油机故障诊断中的应用

在柴油机的故障诊断中,对其振动信号的分析是柴油机故障诊断的重要手段,但是由于噪声的干扰而使诊断的精度大打折扣,而传统的傅里叶低通滤波降噪方法对于柴油机这样的非平衡复杂振动信号不能起到好的降噪作用。本文采用小波降噪技术,将柴油机缸盖振动信号进行小波多尺度分解,然后分别进行阈值处理,最后进行小波重构,剔除柴油机的噪声信号,提高信号的信噪比,获得了比较理想的效果。     

振动故障信号的软件积分研究与应用

分析了常用软件积分方法及其误差产生的原因，采用基于时域插值和FFT变换的波形修正算法进行改进。在时域插值后积分，利用最小二乘法拟合并去除积分后信号波形的趋势曲线；在频域使用FFT变换滤除积分后信号的趋势成分，修正积分后的曲线。汽轮发电机组振动分析表明，基于FFT变换的波形修正算法得到了较好的效果，为判断机组运行状态、进行故障诊断提供了依据。     

小波分析在光激光拾硅微谐振传感器微弱信号检测中的应用

光激光拾硅微谐振传感器在实际测量中光拾信号非常微弱，往往淹没在噪声中，无法识别其谐振频率。根据光拾信号与噪声的频率特性，采用小波多分辨分析方法将噪声与信号分离，并采用双频法识别系统，研究了采样频率与系统谐振频率的关系，在两个最佳采样频率下达到准确识别谐振频率、固有频率与阻尼比。