可变模态分解算法及其在天然气管道泄漏检测中的应用

对可变模态分解算法在天然气管道泄漏信号检测中的应用做了研究。通过对其噪声鲁棒性、过度分解和不饱和分解特性的分析,并与经验模态分解和小波分解进行对比,证明可变模态分解算法在天然气管道泄漏信号检测与分析方面的可行性和有效性,并且可变模态分解具有更好的噪声鲁棒性和泄漏信号检测效果。     

基于非线性短时傅里叶变换阶次跟踪的变速行星齿轮箱故障诊断

针对变速行星齿轮箱信号频率模糊且受噪声影响的问题,提出了基于非线性短时傅里叶变换（NLSTFT）无键相阶次跟踪与变分模态分解的故障诊断方法。用NLSTFT算法估计信号瞬时频率,对其积分获得瞬时相位曲线,通过重采样得到角域信号;利用NCOGS算法对角域信号降噪,采用VMD算法进行角域信号模态分解,通过各模态分量信号包络谱解调实现故障诊断。实验结果表明,新方法计算效率高、鲁棒性好,提高了变转速行星齿轮箱故障诊断性能。     

基于CEEMD-VMD-SIST算法的sEMG信号降噪方法

针对基于表面肌电信号(sEMG)的手势识别中,由于传统降噪算法对sEMG信号高频部分分解不当或存在频率混叠现象使得对含噪sEMG信号降噪效果不佳而导致手势识别精度大大降低的问题,提出使用基于互补集合经验模态分解(CEEMD)与变分模态分解(VMD)组合的滑动区间软阈值(SIST)降噪算法(CEEMD-VMD-SIST)对含噪sEMG信号进行降噪处理;使用CEEMD将含噪信号分解为从高频到低频的多个不同本征模态函数(IMF),根据自相关系数客观界定后续降噪模态分量范围,对选中的模态分量采用VMD的SIST方法进行分解降噪并与部分剩余模态分量进行重构;从实验结果中可以看出,在不同信噪比下,所提算法的降噪性能与传统降噪方法相比,信噪比与均方根误差均有明显改善,可以更大程度上保留信号的有用信息,即所提算法的降噪性能更佳。     

虚拟加工环境中标准化通信的研究与实现

通过在虚拟加工环境中实现基于MMS（制造报文规范）的标准化通令系统,探讨了虚拟制造平台实现网络集成的方法。介绍了MMS协议的内容,提出了系统的总体框架,并详细描述了基于MMS协议的通信支持系统与虚拟设备相连的服务接口系统的实现方法、框架结构和程序执行流程。     

基于VMD和柔性形态学的输电线路故障测距方法

针对输电线路故障测距和信号中存在噪声干扰的问题,提出了基于VMD和柔性形态学去噪技术的输电线路故障测距方法。首先利用变分模态分解(Variational Modal Decomposition,VMD)对信号进行分解,在分解过程中自适应的去除一部分噪声;然后,通过平均柔性形态学滤波器进一步的去除残余干扰;最后使用柔性形态边缘检测有效放大信号奇异点,再设定输出阈值减少噪声背景对信号突变点的影响,可以很清晰的得到故障发生时刻。实验表明此方法有很好的噪声鲁棒性,在抑制了噪声干扰的同时放大了行波特征信号,能够有效检测输电线路故障行波波头,获得更高的检测精度。     

微波毫米波段真空微电子器件中的电子学问题

叙述了微波毫米波真空微电子器件中阵列场发射阴极、输入和输出电路等核心部件的研究进展，讨论了克服电子横向速度分量，噪声，电导损失及发射不均匀性的方法，并对该器件内的空间电荷和阴极失效等问题进行了分析．     

A novel method for wave focusing investigation in metamaterial half‐space and slab lens structures

Approximate image currents for the Sommerfeld half‐space problem with vertical electric and magnetic current sources above double negative half‐space is found using the discrete image theory and Laplace transform. This method would found its applications in investigation of dipole radiation above metamaterial half‐space and in describing the focusing mechanism of metamaterial lenses. In order to efficiently consider the effects of metamaterial half‐space, discrete image dipoles are defined by proposing an exponential series for reflection coefficient. Unknown constants (the loci of images) in the argument of the exponential function are determined by equating the coefficients of the Taylor series for exact reflection coefficient with those of the proposed approximate series. Thus, the calculation of integral with oscillating integrand reduces to the solution of polynomial equation for the zeros of polynomials. As an application for the proposed approach, the field focusing in double negative is investigated. Thanks to the fast and accurate methods for finding the zeros of polynomials, it is shown that in addition to its good accuracy, this method decreases the computational time for field calculations in comparison with the exact methods. It is seen that for the negative refractive index beside −1, the complex images approach to infinity and have negligible effect on the scattering field of dipole. For the other negative indexes the complex image blurs the real image. Copyright © 2015 John Wiley & Sons, Ltd.     

采用自适应VMD与能量管理控制的混合储能平抑风电波动策略

采用燃料电池/电解槽/储氢罐/超级电容构建混合储能系统来平抑风电功率的波动性,实现风电平滑并网.针对风电功率的波动特性,结合风电并网波动率标准,提出自适应VMD算法,实现风电功率的自适应分解,得到风电并网功率和混合储能系统功率指令,根据燃料电池和电解槽出力需求,结合超级电容的荷电状态和储氢罐的储氢状态,提出一种能量管理控制策略,实现储能系统内部功率分配.算例结果表明,所提算法能自适应实现风电功率的最优分解,所提控制策略能完成储能系统内部功率的合理分配并有效地平滑风电出力波动,同时保证超级电容的荷电状态、储氢罐的储氢状态工作在合理区间.     

Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance

In the gear fault diagnosis, the emergence of periodic impulse components in vibration signals is an important symptom of gear failure. However, heavy background noise makes it difficult to extract the weak periodic impulse features. Therefore, the paper presents an impact fault detection method of gearbox by combining variational mode decomposition (VMD) with coupled underdamped stochastic resonance (CUSR) to extract the periodic impulse features. First, the adaptive VMD is presented to decompose the vibration signal into several intrinsic mode functions (IMFs), which can automatically determine the appropriate mode number according to the correlation kurtosis (CK) of decomposition results and extract the sensitive IMF component containing the main fault information. Next, the adaptive CUSR method is developed to analyze the selected sensitive IMF component, and the optimal system parameters are obtained by the genetic algorithm using the CK index as optimization objective function. Finally, the periodic impulse features are extracted by the output signal of CUSR system accurately. Experiments and engineering application verify the effectiveness and superiority of the proposed adaptive VMD-CUSR method for extracting the periodic impulse features in gear fault diagnosis compared to other methods.     

基于AE信号与VMD的滚动轴承故障诊断研究

针对风电机组主轴承在故障初期受轴系振动干扰而难以检测的问题,提出一种基于声发射信号时序均方幅值特征的变分模态分解时频分析方法。首先,采用分帧的预处理方法,对AE信号逐帧提取均方幅值特征,建立时序特征序列;其次,采用VMD时频分析方法对时序特征序列模态分解,计算得到表征故障的特征模态。理论分析和实验结果表明:该方法提取的特征对低速状态下轴承内圈裂纹故障具有明显的辨识度,能有效对低速状态下滚动轴承故障进行判别。