利用时频变换识别睡眠EEG中的基本模式

本文提出了一种基于时频变换识别睡眠EEG中基本模式的方法。首先从信号处理的角度，对国际上普遍采用的睡眠EEG分阶规则进行了分析，指出了利用功率谱方法分析睡眠EEG的不足。通过利用CWD（Choi-Williams）分布对一段EEG信号进行时频变换，得到该段EEG信号在各个时间上的频率分布，称其为一种瞬时频谱。通过检测这种瞬时频谱中的谱峰，并提出按照高频优先原则，识别相应时间里的EEG信号的基本模式。     

基于频率切片小波变换时频分析的齿轮故障诊断

为了在强噪声中有效提取齿轮的故障特征,提出了一种基于频率切片小波变换时频分析的齿轮故障诊断方法。先对信号进行频率切片小波变换,得到在全频带下的时频分布,然后在此基础上分割出含有故障特征的时频区域,再通过对该区域进行时频阈值滤波和逆变换重构分离出有效的故障特征。仿真实验和实测信号分析表明,这种方法可从噪声信号中分离出有效的特征分量,在齿轮故障诊断方面取得了较好的应用效果。     

基于小波分析的旋转机械振动信号定量特征研究

通过对机械振动信号的连续小波变换,利用小波滤波器良好的时频特性,研究了振动信号经过连续小波交换后的统计特征。在信号的特征提取中, 引入“灰度矩”并把一阶矩作为定量指标。对8种典型故障信号的研究表明,这种方法能够简单有效地提取信号的特征,区分振动故障。     

采样信号时频特性的复合材料损伤检测

一种时频数据处理方法被应用于复合材料损伤检测。根据小波变换的框架重构理论和小波变换时频相空间理论,提取了信号的时频域特征,通过比较原信号的时频空间和小波变换相空间的相同部分,得到了能反映原信号同样时频特征的小波级数展开项和,应用Ｇｒａｍ－ｄｓｃｈｍｉｄｔ正交化方法,按照一定准则,对所得到的小波级数展开项的线性组合进行正交处理,用代数数值方法,从已知采样数据分布集合得到了对应于曲线本身时频特征,经过     

基于图结构的轴承故障诊断北大核心

滚动轴承故障振动信号具有非线性、非平稳的特征,在轴承早期破损阶段,即使轴承表面出现了损伤,故障产生的振动信号仍然表现得非常微弱,再加上大量噪声的影响,仅从时域和频域很难发现故障特征,给故障检测造成了较大的难度。针对轴承振动信号的特点,将短时Fourier变换与图模型相结合,提出了一种基于图模型的时频分析方法,利用短时Fourier变换得到信号的时频图,选取每一时刻频谱图中各主频的幅值构建图模型,通过图模型的相似性对比检测轴承故障并通过主频幅值的变化量确定故障频率。     

基于分级概率成像的大电机定子绝缘损伤识别

为了正确评估大电机定子绝缘老化程度,提出了一种基于导波复合特征的分级概率成像损伤检测方法。通过提取损伤前后Lamb波信号之间的相关系数,利用全局概率成像初步获取绝缘损伤的分布区域和损伤程度。通过提取Lamb波散射信号波包传播时间和峰值特征,采用局部概率成像方法进一步表征损伤的局部特征。通过对两种损伤概率成像结果进行图像融合获得定子绝缘损伤识别结果。最后,对不同的典型绝缘损伤进行了损伤检测实验。结果表明:利用复合特征和分级概率成像方法可以识别出定子绝缘损伤位置和损伤程度,能够为大电机定子绝缘故障诊断提供更加有效的参考信息。     

基于小波变换的隐Markov模型离心泵故障诊断方法研究

根据离心泵故障振动信号的特点,本文提出了一种结合小波变换与隐Markov模型(HMM)的离心泵故障诊断方法。小波变换具有多分辨率分析并且在时频两域都具有表征信号局部特征能力的特点,利用Daubechies小波对振动信号进行一维8尺度的小波分解,然后从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进行训练,其中输出概率最大的状态即是离心泵的运行状态,从而实现离心泵的故障诊断。最后通过2BA-6A离心泵试验系统验证了该方法的有效性。     

基于迭代广义同步压缩变换的时变工况行星齿轮箱故障诊断

同步压缩变换在分析频率恒定的单分量信号时改善时频可读性的效果显著,而在分析多分量频率时变信号时存在时频模糊现象,为了解决这一问题,提出迭代广义同步压缩变换方法。通过迭代广义解调分离出各单分量成分,并将时变频率变换为恒定频率。应用同步压缩变换精确估计瞬时频率和时频分布幅值。将各单分量的时频分布叠加获得信号的时频分布。该方法有效改善了同步压缩变换在分析频率时变信号时的时频可读性,并且将其推广应用于多分量信号。应用该方法有效识别了时变工况下行星齿轮箱振动信号的频率组成及其时变特征,准确诊断了齿轮故障。     

小波变换在故障诊断中表征信号奇异点的应用

在工业控制系统中,设备故障信号多是突变性的。采用小波变换这种全新的时频分析方法能有效检测系统信号的奇异点,进而提取出故障信息。实验证明,小波变换在故障诊断中具有优越性。     

一种基于波信号能量谱相关系数的损伤定位方法

基于波信号能量谱的相关系数,采用了两种策略分别计算传感线路的损伤特征参数(Damage index,DI),策略一:计算一条传感线路的感应波信号能量谱在基准和检测两种状态下的相关系数,并把该系数作为该条传感线路的DI。策略二:首先计算一条传感线路的激励波与感应波信号的相关系数,并把检测状态的相关系数相对于基准状态的相关系数的变化量作为该条传感线路的DI。在检测一个具有加强筋的复合材料板结构时,结合所计算的DI和损伤定位方法获取锥形孔损伤的概率分布图。试验结果表明策略二比策略一定位损伤的精度更高。这是由于策略二有效地避免了基准状态和检测状态的激励波信号的微弱差异所导致的损伤识别误差。     

Pleasure detection by online brain-computer interface

This paper seeks to elucidate the design and implementation of an instrumentation amplifier, filters, LabVIEW-based spike detection, and automatic spike counting to detect pleasure sensation in the rat using invasive BCI. This method involved sites related to pleasure, and after acquiring signals from the ventral pallidum, facial motor cortex, and orbitofrontal cortex, these signals were analyzed to assess the pleasure sensation in the rat. The results illustrated a decreased spike rate in the motor and orbitofrontal cortices and an increased spike rate in the ventral pallidum during pleasure. The pleasure detection experiment was conducted four times to obtain the mean values of spike rates. The motor cortex had 9 spikes/s, the orbitofrontal cortex had 18 spikes/s and the ventral pallidum had 34 spikes/s. The correlation coefficient is above 78%, effectiveness of the experiment.     

宽频带记录信号的锋电位检测法

为了考察深部脑刺激的高频电刺激（HFS）期间各种神经元单体的动作电位发放活动,排除电刺激诱发的群峰电位的干扰,设计了一种窗口检测新算法,直接用于检测宽频带记录信号中的锋电位。并且,利用仿真数据和大鼠海马CA1区实验记录数据验证此算法的有效性。结果表明,新算法的锋电位检出率显著大于常规阈值法,而误检率则显著小于阈值法。该算法对于高频刺激期间的仿真数据的锋电位检出率可达95%,误检率则仅为4%;对于7只大鼠的顺向高频刺激实验记录数据的平均锋电位检出率为88 ± 1.4%,而误检率为4.6 ± 1.1%。总之,新窗口法可以正确检测高频刺激期间的锋电位,用于研究各种神经元单体在电刺激期间的不同响应活动,为深入揭示深部脑刺激的神经网络机制提供了有用的新工具。     

基于独立分量分析的脑电信号消噪

脑电信号中往往含有各种形式的噪声干扰信号。这些干扰成分包括眼电、心电伪迹以及工频干扰等。由于干扰信号和脑电信号在频域上相互重叠,因此用时域或频域滤波的方法难以有效地消除脑电信号中的干扰成分。独立分量分析(Independent Component Analysis,ICA)是20世纪90年代发展起来的一种新的盲源分离方法(Blind Source Separation,BSS),将ICA方法应用于实测脑电信号的处理,获得非常理想的消噪效果。     

FEATURE EXTRACTION OF VIBRATION SIGNALS BASED ON WAVELET PACKET TRANSFORM

A method is proposed for the analysis of vibration signals from components of rotating machines, based on the wavelet packet transformation (WPT) and the underlying physical concepts of modulation mechanism. The method provides a finer analysis and better time-frequency localization capabilities than any other analysis methods. Both details and approximations are split into finer components and result in better-localized frequency ranges corresponding to each node of a wavelet packet tree. For the purpose of feature extraction, a hard threshold is given and the energy of the coefficients above the threshold is used, as a criterion for the selection of the best vector. The feature extraction of a vibration signal is accomplished by computing the reconstruction signal and its spectrum. When applied to a rolling bear vibration signal feature extraction, the proposed method can lead to be very effective.     

基于变采样长度相关滤波的不平衡信号提取法

根据相关滤波提取信号幅值和相位的原理,定义了不平衡信号的近频噪声干扰度的概念,并构建了近频噪声干扰度模型,揭示了干扰度随频率差变化的分布规律;进而提出了一种基于变采样长度相关滤波的不平衡信号提取法,在短数据情况下根据MUSIC谱确定幅值最大的近频干扰信号频率,结合近频干扰度分布规律合理选择采样长度提取不平衡信号幅值和相位。电主轴动平衡测量实验和仿真结果表明：与传统定采样长度相关滤波相比,所提方法受信噪比波动影响小,抗近频干扰能力强,在采样长度更短时提取的不平衡信号幅值和相位的方差更小。     

气体管道泄漏模态声发射时频定位方法

针对声发射信号频散特性导致基于时延估计的气体管道泄漏定位误差大的问题,提出一种基于模态声发射时频分析的泄漏定位方法。该方法采用平滑伪Wigner-Ville时频分布对两泄漏信号的互相关函数进行时频分析,利用互相关函数的时频谱可同时提取泄漏信号的时间延迟和与之对应的频率;然后根据泄漏声发射信号的主导模态的频散曲线即可确定该频率对应的声速,利用实时确定的声速和时间延迟并根据两传感器之间的距离即可确定泄漏点的位置。实验结果表明,采用时频分析的气体管道泄漏定位误差与互相关相比减少了6倍。所提出的模态声发射时频定位方法能有效抑制泄漏信号的频散,提高泄漏信号的相关性,从而更适合用于声发射管道泄漏定位。     

Methods of de-noising the low frequency electromagnetic data

The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on ...     

A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery

In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal–noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.     

Cleaning high-frequency velocity profile data with autoregressive moving average (ARMA) models

High resolution velocity profiling instruments have enabled a new generation of turbulence studies by greatly increasing the amount and quality of simultaneous velocity measurements that can be obtained. As with all velocity profiling instruments, however, the collected data are susceptible to erroneous spikes and poor quality time series that must be corrected or removed prior to analysis and interpretation of the results. In the current study, ARMA models are investigated for their potential to provide a comprehensive approach to data cleaning. Specific objectives are to: i) describe the cleaning algorithms and their integration with an existing open-source Matlab toolbox, ii) test the algorithms using a range of published data sets from two different instruments, and iii) recommend metrics to compare cleaning results. The recommended approach to detect and replace outliers in profiled velocity data is to use a spatial ‘seasonal’ filter that takes advantage of information available in neighboring cells and a low order ARMA model. Recommended data quality metrics are the spike frequency and the coefficients of the model. This approach is more precise than the most common current despiking method, offers a seamless method for generating replacement values that does not change the statistics of the velocity time series, and provides simple metrics with clear physical interpretation that can be used to compare the quality of different datasets.