基于希尔伯特振动分解的电压闪变检测新方法

提出将一种新的非平稳信号处理方法--希尔伯特振动分解(HVD)应用于电压闪变分析.针对工频载波中含有高次谐波的电压闪变信号,该方法根据闪变解析信号的瞬时频率表达式可以分解为工频值与高次谐波引起的快速变化不对称振荡高频值之和的特性,利用低通滤波器直接估计出工频载波瞬时频率,通过同步检测准确分离出工频载波电压闪变包络,无需预处理即消除了高次谐波的影响.同时该方法可以自适应地检测出闪变调制的频率和幅值等参数,与小波方法相比无需基函数.仿真表明,但在含高次谐波的情况下HVD方法能准确检测出闪变包络及其调制参数,与小波方法相比精度和运算效率更高.实验进一步证明了该方法的有效性和正确性.     

基于小波变换的电压闪变分析方法

随着小波变换在理论和方法上的不断完善和发展,在电力系统中得到了越来越广泛的应用.作为电能质量指标之一的电压闪变问题,目前的分析手段仍然是傅立叶变换.由于傅立叶变换本身不具备时域分析能力并存在很多缺陷,在应用小波变换之后,在电压闪变信号的检波与分解等方面都取得了很大的进步.实验证明,小波变换是一种更为有效的电压闪变分析方法.     

数学形态学滤波在电压闪变检测中的应用

对于有效的检测和控制电压闪变来说,检测精密度和有效性是非常重要的.本文提出了一种基于数学形态学滤波和希尔伯特变换的检测方法.通过希尔伯特变换,闪变信号包络可以被提取出来,包含在电压闪变信号中较高频率的谐波和其它噪声可以通过数学形态学滤波器消除.仿真分析结果表明了本方法的可行性、有效性,为电压闪变参数的提取提供了一种新方...     

基于TMS320C6713 DSP的IEC电压闪变仪

为实现电压闪变的精确测量,按照国际电工委员会（IEC）推荐的电压闪变仪功能和设计规范,在TMS320C6713数字信号处理器（DSP）平台上设计了采样率为400 Hz的IEC电压闪变仪,并按照IEC给出的电压闪变仪的校验标准,采用多个矩形波动信号进行了闪变值的校验,分析了电网谐波和白噪声对IEC电压闪变仪计算结果的影响,最后,针对白噪声干扰问题,提出了小波阈值去噪的方法,对输入信号进行去噪,校验结果表明基于TMS320C6713 DSP的IEC电压闪变仪测量结果较为准确。研究结果表明电网白噪声对闪变仪的精度干扰较大,电网谐波几乎无影响;经小波阈值去噪后,电压闪变仪的测量精度得到了显著提高。     

改进的希尔伯特-黄变换及其在滚动轴承故障诊断中的应用

希尔伯特黄变换(Hilbert-Huang transform,HHT)是一种优秀的时频分析方法,包含经验模态分解(Empirical mode decomposition,EMD)和希尔伯特变换(Hilbert transform,HT)两部分,通过EMD对信号进行分解,再对得到的内禀模态函数(Intrinsic mode function,IMF)进行HT解调。针对EMD存在的包络误差,模态混淆,端点效应以及HT易产生负频率等缺陷,提出一种改进的HHT方法(Improved HHT,IHHT),IHHT包括广义经验模态分解(Generalized EMD,GEMD)和改进的直接正交(Improved direct quadrature,IDQ)解调,GEMD通过定义多种均值曲线,从得到的多个IMF中选择最优分量,从而保证了每阶分量的优越性;再采用改进的经验调幅调频分解和IDQ方法对信号进行解调,提高了分解和解调的精确性、抑制了端点效应的产生。将提出的方法应用于滚动轴承故障诊断,分析结果表明IHHT是一种有效的信号处理方法。     

同步压缩小波与希尔伯特-黄变换性能对比

经验模式分解(empirical mode decomposition简称EMD)中包络均值代替信号实际均值的算法误差,使其在处理复杂多频振动信号时易出现模式混叠,引起分析误差。针对这一问题,采用同步压缩小波变换(synchrosqueezed wavelet transform,简称SWT)根据时间-尺度平面中各元素模的大小,对平面内的能量进行重新分配,通过映射关系将时间-尺度平面转化为时间-频率平面,获得频率曲线更加集中的时频表达。这一方法的正交性与算法自身良好的数据驱动性降低了模式混叠引起的时频分析误差,多组分仿真信号时频特征提取证明了SWT的优异时频特性,利用旋转机械不对中振动位移信号进行了实测数据分析。结果表明,SWT能够精确描述谐波信号的频率构成,且所获时频能量分布集中,时、频域定位精度高,为机械设备的状态监测与故障诊断提供了一种新的时频分析手段。     

改进希尔伯特-黄变换的滚动轴承故障诊断

针对希尔伯特-黄变换中经验模态分解方法存在的端点效应和虚假固有模态函数的问题,提出一种改进希尔伯特-黄变换方法并将此方法应用于滚动轴承故障诊断中。首先,利用最小二乘支持向量机和镜像延拓相结合的方法来抑制端点效应;其次,采用敏感固有模态函数选择算法选出反映故障特征的敏感固有模态函数;最后,利用敏感固有模态函数的包络谱进行故障诊断。通过仿真分析和应用实例可看出,该方法能够有效提取出滚动轴承故障信号的特征信息并准确诊断出引起滚动轴承的故障原因。     

电力能效测评系统中电压闪变检测的研究

针对电力能效测评系统中闪变数据的检测和传输方法实时性差的问题,设计了一种无线通信的闪变监测系统。基于国际电工委员会(IEC)推荐的平方检测法,WJZ-VI型电能监测仪实现监测点数据实时采集处理。根据现场要求,采用GPRS技术进行实时数据无线传输。搭建了Simulink仿真模型,仿真得到S(t)值达到了IEC相关标准。研究结果表明,该闪变监测系统能够有效的并入电力能效测评系统中,实时采集、处理以及无线传输闪变数据,从而为后期的闪变控制及故障检测提供技术保障,有较大的推广应用价值。     

基于希尔伯特-黄变换方法的信号奇异性检测与仿真

介绍了希尔伯特-黄变换(HHT)方法的分析步骤、特点和应用范围,并对信号奇异性检测效果进行了仿真分析,并和短时傅里叶变换(STFT)、魏格纳-威利分布(WVD)、小波变换(WT)等分析方法进行了比较。结果表明,希尔伯特-黄变换方法具有良好的局部适应性和在信号奇异性检测方面的有效性及优越性。     

基于希尔伯特－黄变换的往复运动摩擦力信号分析

往复运动中的摩擦力信号往往是非平稳信号,采用经验模式分解法可以自适应地分解这些非线性、非平稳信号,合理地提取其信号特征。应用希尔伯特－黄变换方法,分析不同往复速度和载荷条件下往复运动产生的摩擦力信号,并提取时频段能量比及其标准差,研究其润滑状态特征。结果表明：通过希尔伯特－黄变换并结合能量比分析,可以很好地反映出往复运动过程中润滑状态的变化;在一定载荷下,随着往复速度的增加,摩擦力频率成分趋于平稳,能量比标准差则逐渐减小;在一定往复速度下,随着载荷的增加,润滑状态变差,消耗的能量随之增大,能量比标准差逐渐减小;和往复速度相比,载荷对摩擦力频率分布影响相对较小。     

运用Hilbert—Huang变换的振动测试分析系统

讨论了基于LabVIEW的振动信号分析系统的设计过程,并将一种新的时频分析方法Hilbert—Huang变换应用到该系统。通过实验表明,利用Hilbert—Huang变换可以有效地实现信号的去噪处理。     

Wear detection in gear system using Hilbert-Huang transform

Fourier methods are not generally an appropriate approach in the investigation of faults signals with transient components. This work presents the application of a new signal processing technique, the Hilbert-Huang transform and its marginal spectrum, in analysis of vibration signals and faults diagnosis of gear. The Empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) and marginal spectrum are introduced. Firstly, the vibration signals are separated into several intrinsic mode functions (IMFs) using EMD. Then the marginal spectrum of each IMF can be obtained. According to the marginal spectrum, the wear fault of the gear can be detected and faults patterns can be identified. The results show that the proposed method may provide not only an increase in the spectral resolution but also reliability for the faults diagnosis of the gear.     

Bearing fault detection utilizing group delay and the Hilbert-Huang transform

Vibration signals measured from a mechanical system are useful to detect system faults. Signal processing has been used to extract fault information in bearing systems. However, a wide vibration signal frequency band often affects the ability to obtain the effective fault features. In addition, a few oscillation components are not useful at the entire frequency band in a vibration signal. By contrast, useful fatigue information can be embedded in the noise oscillation components. Thus, a method to estimate which frequency band contains fault information utilizing group delay was proposed in this paper. Group delay as a measure of phase distortion can indicate the phase structure relationship in the frequency domain between original (with noise) and denoising signals. We used the empirical mode decomposition of a Hilbert-Huang transform to sift the useful intrinsic mode functions based on the results of group delay after determining the valuable frequency band. Finally, envelope analysis and the energy distribution after the Hilbert transform were used to complete the fault diagnosis. The practical bearing fault data, which were divided into inner and outer race faults, were used to verify the efficiency and quality of the proposed method.     

一种评估风电场电压闪变的实用方法

为解决风电场并网引起的并网点电压闪变评估问题,将风电机组出力曲线拟合和电网络理论应用到其中.风电场根据电网络理论可以等效为一台风力发电机及其相关阻抗.在分析相关文献的基础上,探讨了风电场网络的建模和等效问题,从理论上证明了容量较小的风电场不会引起较大的电压闪变;详细地描述了IEC61400-21风电场电能质量评估标准,该标准包括在运行时和切换操作时对电压闪变的评估;同时简略描述了相关国家标准中对电压闪变限值的要求;通过使用IEC61400-21和IEC61000-4-15对之开展了分析,提出了一种评估风电场电压闪变的实用方法.最后,以某风电场为例,使用该方法进行了电能质量评估;并基于国家标准,对评估值作出了评价.研究结果表明,该风场运行时短时电压闪变值只有0.009 185,切换操作时产生的短时电压闪变值更小,远远低于限值0.8.     

基于Hilbert-Huang变换的QRS波检测算法研究

心电图(ECG)被广泛应用于心脏疾病诊断,而ECG中QRS复合波的检测是对ECG进行处理和分析的基础.本文提出一种新的QRS复合波检测算法.该算法利用Hilbert-Huang变换(HHT)中的经验模式分解(EMD)将心电信号自适应地分解为一组称为内禀模态函数(IMF)的波动模态成分.然后,结合软阈值去噪方法,构造出有利于QRS复合波检测的检测层,利用模极大值和QRS复合波特征点之间的对应关系,实现了对QRS复合波的检测.经MIT-BIH心率失常数据库验证,该算法对QRS复合波检测的平均准确率达到了99.34%,平均灵敏度为99.77%,平均阳性预测率为99.56%.     

Optimization of the End Effect of Hilbert-Huang transform (HHT)

In fault diagnosis of rotating machinery, Hilbert-Huang transform(HHT) is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF(Intrinsic Mode Function). To counter such problems in HHT, a new method is put forward to process signal by combining the generalized regression neural network(GRNN) with the boundary local characteristic-scale continuation(BLCC).Firstly, the improved EMD(Empirical Mode Decomposition) method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the generated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain,frequency domain and related parameters of HilbertHuang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method can effectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accurately.     

基于Prony算法的电压闪变检测方法研究

基于常用电压闪变测量方法的调查研究,在电压闪变的在线检测和分析中引入了Prony算法.首先由输入信号序列构造了一个样本矩阵,再利用SVD算法对样本矩阵进行奇异值分解,得到了样本矩阵的有效秩,然后根据最小二乘法估算出信号各频率分量的特征参数,最后利用解调制原理准确地得到了信号的包络.针对有谐波源的电压闪变问题,对包络解调进行了修正,从而保证在有谐波源情况下也能有效获取电压闪变信号的包络.仿真实验结果表明,该方法具有高分辨率、高精度等优点,并验证了其准确性和有效性.     

Feature extraction of AE characteristics in offshore structure model using Hilbert-Huang transform

This paper addresses an application of recently developed signal processing tool based on the Hilbert-Huang transform (HHT) to characterize the acoustic emission (AE) signals released from the offshore structure model. The AE signals from the cracks in the welded steel nodes of the offshore structure model are collected during the tensile testing in water and the simulated AE signals are also collected in the offshore structure model. Instantaneous frequency and energy features based on local properties of the AE signals are then extracted using the Hilbert-Huang transform method. In order to demonstrate the advantage of the AE feature extraction techniques based on Hilbert-Huang transform, the conventional AE analysis is also provided side-by-side for comparison. The results verify that the method based on HHT better characterizes the AE signals than the classical AE techniques. It can be concluded that the AE signal analysis based on HHT is an effective tool to extract the features and this opens perspectives for crack recognition in offshore structures.     

基于小波包及Hilbert-Huang变换的数控铣削颤振诊断技术

为了提高产品加工质量,根据试验测得铣削系统颤振稳定域,制定并采集数控铣削振动信号,以保证采集信号的准确性;融合小波包变换与希尔伯特黄变换,从能量频域分布与幅值概率统计分布两方面提取信号特征值,其中小波包降噪作为信号前置处理能有效降低环境噪声干扰的影响,提高经验模式分解的精度;建立基于模糊支持向量机的颤振诊断模型,将振动信号分为平稳铣削信号、微弱颤振铣削信号、颤振铣削信号及刀具磨损铣削信号。实验结果表明,该模型具有良好的铣削振动信号辨识与诊断能力,预测准确率达97.3%,为数控铣削加工振动信号的准确辨识与诊断提供了一种新方法。