State inspection for transmission lines based on independent component analysis

Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation. Independent component analysis (ICA) is a method for finding underlying factors or components from multivariate statistical data based on dimension reduction methods, and it is applicable to extract the non-stationary signals. FastICA based on negentropy is presented to effectively extract and separate the vibration signals caused by human activity in this paper. A new method combined empirical mode decomposition (EMD) technique with the adaptive threshold method is applied to extract the vibration pulses, and suppress the interference signals. The practical tests demonstrate that the method proposed in the paper is effective in separating and extracting the vibration signals.     

基于EMD广义能量的列车车轮故障诊断技术

针对列车车轮故障诊断,研究基于经验模态分解(EMD)广义能量法诊断技术。首先对钢轨振动信号进行经验模态分解,选取出有效本征模函数分量并赋予权重系数,然后求出各分量的能量加权和作为该信号的EMD广义能量值,最后确定出正常车轮的EMD广义能量安全域阈值,判断车轮的故障状态。采用仿真的正常及故障车轮的钢轨振动信号进行实验,验证提出的方法对正常和故障车轮的识别准确率达到90%以上。     

合武客专无砟轨道曲线段不平顺谱分析

以合武客运专线动检车实测轨道不平顺数据为统计样本,采用轨道不平顺变化率和线性插值方法以及经验模态分解法对轨道不平顺异常值和趋势项进行处理,并运用经典周期图法对统计样本进行空间谱估计,同时采用最小二乘法对轨道不平顺功率谱进行拟合。研究结果表明,不平顺功率谱谱线普遍低于美国6级谱,在长波波段能量较低,但存在周期性成分,同时温度力对无缝线路影响显著。缓和曲线与圆曲线的分析结果都表明内轨平顺状况优于外轨。     

Automatic artist recognition of songs for advanced retrieval

Automatic recognition of artists is very important in acoustic music indexing, browsing, and contentbased acoustic music retrieving, but synchronously it is still a challenging errand to extract the most representative and salient attributes to depict diversiform artists. In this paper, we developed a novel system to complete the reorganization of artist automatically. The proposed system can efficiently identify the artist's voice of a raw song by analyzing substantive features extracted from both pure music and singing song mixed with accompanying music. The experiments on different genres of songs illustrate that the proposed system is possible.     

基于自适应奇异值标准谱和EMD的柴油机故障诊断

针对柴油机多发故障,提出了自适应奇异值标准谱和经验模态分解(Empirical Mode Decomposition,EMD)相结合的故障诊断模型。通过计算平均最近邻域发散度和奇异值标准谱的方法自适应地选择奇异值分解的嵌入维数和重构阶数,提高了奇异值分解降噪的精度。对降噪后的信号进行EMD分解,并利用调整余弦相似度标准提取反映信号真实特征的主固有模态函数(Intrinsic Mode Function,IMF),进而提取故障特征参数。将此模型应用于F3L912柴油机进气门漏气、单缸失火和多缸失火等故障的诊断,通过提取峭度和过零率作为故障特征,获得了较高的故障分类准确率。     

基于EMD柴油机气阀声发射诊断技术试验研究

以4120SG柴油机为研究对象,通过故障模拟试验测试了柴油机在气阀正常状态、气阀漏气状态下的缸盖声发射信号,对燃烧段声发射信号进行经验模态分解(Experience Mode Decomposition,EMD),计算了各个本征模分量(Intrinsic Mode Function,IMF)的能量分布及EMD能量熵。研究表明声发射信号能量主要集中在前3个IMF分量内,随着气阀漏气故障严重程度的增加,代表最高频IMF1分量能量相对增加,而能量熵H减少。通过自定义特征参数,提高了声发射应用于柴油机气阀漏气诊断方法的灵敏度。     

EMD小波阈值去噪在海工物探中的应用

本文以海洋工程地质地球物理模型和我公司完成的海洋工程地震勘探数据为依托,综合分析了小波变换、EMD处理、基于EMD小波阈值去噪处理等数据处理技术对于海洋地震勘察数据处理的作用,论述了基于经验模态分解的小波阈值去噪方法在浅海地震勘探数据处理中对于压制噪声、提高信噪比和分辨率的作用。该方法对于细化地震勘探的数据成果,实现物探成果的精细化地质分层具有积极意义。     

An Image Retrieval Method Based on Color and Texture Features

The technique of image retrieval is widely used in science experiment, military affairs, public security,advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the characteristics of color, texture, shape and space relationship. This paper introduced an image retrieval algorithm, which is based on the matching of weighted EMD(Earth Mover‘s Distance) distance and texture distance. EMD distance is the distance between the histograms of two images in HSV(Hue, Saturation, Value) color space, and texture distance is the L1 distance between the texture spectra of two images. The experimental results show that the retrieval rate can be increased obviously by using the proposed algorithm.     

基于经验模态分解的惯性测量单元加速度信号处理

对惯性测量单元(IMU)输出的加速度信号进行频域分析,采用基于连续均方根误差准则的经验模态分解(EMD)去噪方法,将加速度信号低频成分滤除,以降低轨道状态检测中噪声信号对测量精度的影响.对去噪后的加速度信号进行连续两次积分处理,可获得载体的空间运动轨迹.     

信号预处理对桥梁颤振导数识别结果影响分析

采用经验模态分解(EMD)对桥梁颤振导数识别信号进行预处理,分离其固有模态函数,消除其中的高频噪声和低频非平稳趋势项;分析了不同阻尼自由振动信号的信噪比与信号长度的关系;研究了苏通大桥主梁节段模型颤振导数对信号长度和消除信号均值、高频噪声、低频趋势项的敏感性.结果表明,借助EMD剔除信号中高频噪声和低频趋势项可以提高桥梁颤振导数的识别精度;颤振导数识别结果与是否消除信号均值和信号长度都有不可忽略的关系.