EMD和SVM在刀具故障诊断中的应用

与传统方法相比,声发射传感器在刀具故障诊断方面有很大的优势。将声发射传感器应用于刀具切削过程中,提出了基于经验模态分解（EMD）和支持向量机（SVM）的刀具故障诊断方法。该方法首先对标准化的声发射信号进行经验模态分解,将分解后的有限个固有模态函数（IMF）通过一定的削减算法增强故障类型特征,把每个IMF和残余项的能量以及整个信号的削减比作为特征向量,最后将特征向量输入支持向量机进行训练和测试,判断刀具的故障类型。通过对某一刀具的故障诊断结果进行分析,验证了该方法的实用性和有效性。     

基于经验模态分解的燃烧不稳定性分析

实验研究了在常温常压条件下贫燃预混旋流火焰的燃烧不稳定性,发生燃烧不稳定性时其压力脉动表现为非平稳信号.利用一种基于经验模态分解(EMD)的希尔伯特-黄变换(HHT)算法对在当量比分别为0.71和0.80工况下的压力脉动信号进行了时频分析.针对压力脉动信号进行经验模态分解,选取主要的固有模态函数(IMF),对IMF通过HHT变换得到瞬时频率并进行统计分析.结果表明:在当量比为0.71时,压力信号呈间歇式的脉动,其振型为拍振;在当量比为0.80时,脉动压力信号则呈现出极限环振型.在基于EMD的HHT变换中,IMF体现了燃烧不稳定性的固有模态且具有自适应性强的特点.      

基于经验模分解的PPP时间比对数据消噪方法

精密单点定位(PPP)时间比对数据会受到观测噪声的影响,因此对时间比对数据进行消噪是一项重要工作。提出一种基于经验模分解(EMD)的PPP时间比对数据消噪方法,并将该方法与Vondrak滤波方法的消噪效果进行对比。结果表明,两种方法的消噪效果相当,均能有效滤除PPP时间比对数据中的随机噪声,明显改善时间比对的稳定度。     

基于经验模分解的PPP时间比对数据消噪方法

精密单点定位(PPP)时间比对数据会受到观测噪声的影响,因此对时间比对数据进行消噪是一项重要工作。提出一种基于经验模分解(EMD)的PPP时间比对数据消噪方法,并将该方法与Vondrak滤波方法的消噪效果进行对比。结果表明,两种方法的消噪效果相当,均能有效滤除PPP时间比对数据中的随机噪声,明显改善时间比对的稳定度。     

一种用于空间群目标分辨的滑动窗轨迹跟踪算法

针对大多数现有的微多普勒分析理论难以解决空间群目标的监测与识别问题,本文提出一种基于提取目标运动特征的弹道中段群目标分辨方法。首先建立了多个具有滑动散射中心的旋转对称目标模型并得到其m-D曲线,在此基础上,利用形态学图像处理方法抑制一维距离像旁瓣,然后提出了一种滑动窗轨迹跟踪的方法分离出各散射点相互交叉的m-D曲线,再对分离结果进行经验模式分解(Empirical-Mode Decomposition, EMD),最后通过提取能够反映目标运动特征的固有模态函数(Intrinsic Mode Function ,IMF),实现了群目标分辨。仿真实验校验了所提方法的可行性和鲁棒性。     

Tide variation monitoring based improved GNSS-MR by empirical mode decomposition

Global Navigation Satellite System multipath reflectometry (GNSS-MR) technology has great potential for monitoring tide level changes. GNSS-MR techniques usually extract signal-to-noise ratio (SNR) residual sequences using quadratic polynomials; however, such algorithms are affected considerably by satellite elevation angles. To improve the stability and accuracy of an SNR residual sequence, this study proposed an SNR signal decomposition method based on empirical mode decomposition (EMD). First, the SNR signal is decomposed by EMD, following which the SNR residual sequence is obtained by combining the corresponding intrinsic mode function with the frequency range of the coherent signal. Second, the Lomb–Scargle spectrum is analyzed to obtain the frequency of the SNR residual sequence. Finally, the SNR frequency is converted into the tide height. To verify the validity of the SNR residual sequence obtained by EMD, the algorithm performance was assessed using multigroup satellite elevation angle intervals with measured data from two station, SC02 in the United States and RSBY in Australia. Experimental results demonstrated that the accuracy of the improved algorithm was improved in the low-elevation range. The improved algorithm demonstrated high monitoring accuracy, and the effective number was not less than 80% of the total in SC02, which means it could effectively describe the trend of the tide with accuracy of approximately 10 cm, meanwhile, the RMS error of RSBY could be reduced by 30 cm, to the maximum extent. The EMD method effectively expands the range of available GNSS-MR elevations, avoids the loss of effective information, enhances considerably the utilization rate of GNSS data, and improves the accuracy of GNSS-MR tide level monitoring.     

Scale analysis of pre- and post-midnight ESF bubbles at storm time and quiet time

This paper adopts a scale analysis technique to investigate the properties of intermediate-scale plasma structures observed by ROCSAT-1 in the equatorial F-region. A procedure of scale analysis that is developed via the empirical mode decomposition (EMD) method of Hilbert–Huang transform (HHT) technique allows the mutually correlated components in velocity, density and relative density gradient to be identified and extracted. Comparing the three parameters, good match in wave form is found for density and velocity in the scales between kilometers and hundred meters (few kilometers to 300 m). It implies that there are electric fields proportional to density fluctuation −δn/n in the form similar to what is expected for the generalized Rayleigh–Taylor instability. We find that such a one-to-one match holds for various pre- and post-midnight ESF bubbles during quiet and storm times. It, therefore, means that spatial structures of electric field in the intermediate-scale (300 m to few kilometers) correlates to the density structures in a manner of δE ∝  −δn/n that is not necessarily depending on the driving mechanism of ESF bubbles, although it is known that ESF bubbles can be driven by different mechanisms under different space weather conditions. In smaller scales (300–50 m), fluctuation patterns of density and velocity do not correlate to each other any more, the good match is then found in the density gradient ∇_xn/n and velocity. It is known as the manifestation of the Boltzmann relation. We note that the GRT instability related relationship δV_z ∝  −δn/n for irregularities in scale of kilometers holds only for ESF bubbles that occur within ±5 dip latitude, while the Boltzmann relation (δV_z proportional to ∇_xn/n) holds for small-scale irregularities without such a limitation.     

金刚石纳米薄膜法向热导率的分子动力学模拟

结合卫星“微型核”的特点，用分子动力学模拟的方法研究了金刚石晶体的热导率与其厚度和温度的依变关系。采用平衡分子动力学的方法（EMD）模拟了晶向（001）的金刚石纳米薄膜法向热导率；采用非平衡分子动力学（NEMD）方法模拟了晶向（111）的金刚石法向热导率。模拟的结果表明：金刚石纳米薄膜的法向热导率显著小于对应大体积晶体的实验值，并随着厚度的增加而增加；在模拟范围内法向热导率与薄膜厚度呈近似线性关系；薄膜热导率在模拟厚度为2．05334nm时随着温度的增加而增加；在模拟厚度为2．874676nm时，则随着温度的增加而下降。     

多层清除重复间隔阈值的EMD去噪及其应用

为了更好地提升经验模式分解(empirical mode decomposition,EMD)对于Mie散射激光雷达信号的去噪效果,提出了一种多层清除重复间隔阈值(EMD-MCIIT)的EMD去噪方法.首先,对多层固有模态函数(intrinsic mode function,IMF)进行去噪,应用alter函数对信号分量进行改变.然后,应用重复间隔阈值(EMDIIT)对信号进行滤波.通过信号仿真以及Mie散射激光雷达真实回波信号的实验进行验证,与传统的EMD阈值方法进行比较,结果表明该方法可以有效地去噪,信噪比有了较大的提升,因此具有很好的应用前景.     

基于EMD及PNN的航天器振动环境分析

针对航天器非平稳随机振动信号模态频率密集的特点,提出了基于经验模式分解EMD (Empirical Mode Decomposition)的多分量过程神经网络PNN(Process Neural Network)自回归模型.通过EMD对原始时间序列进行分解, 使之成为一组不同尺度的局部正交本征模函数IMF(Intrinsic Mode Functions),利用PNN对每个IMF分别进行时变参数分析并以此确定其时变自功率谱密度,对所有分量的时变自功率谱密度通过叠加进行重构, 以此得到原始信号的时变自功率谱密度.仿真结果和实例分析表明:和传统的时频分析法相比,该方法直接使用信号数据,避免了相关估计计算,减小了计算工作量;无交叉干扰项,提高了信号的时频分布特性,具有较高的时频分辨率;对各工况下航天器的振动信号能有效的进行分析,具有较强的信号特征提取能力.