Parameters of Exterior Ballistic Feature Points Extraction in Radar Measurement Data by EMD

The problem of measuring exterior ballistic feature points is always difficult to solve and it is essentiale on exterior ballistic measurement. By analysis of radar reflection characteristics and non-stationary echo signals of exterior ballistic feature points, the echo data of exterior ballistic feature points is measured by using the continuous wave radar. The parameters of feature points are extracted by the empirical mode decomposition method (EMD) of Hilbert-Huang transform (HHT) spectrum analysis technique. The radar echo signal model and EMD extraction model are established to analyze the exterior ballistic mutation point detection and EMD extraction method of aliasing echo signal. Typical feature point parameters of exterior ballistic in rocket flight tests are carried out and the effectiveness of the method is verified. A new method of measuring the parameters of exterior ballistic feature point is therefore presented.     

嵌入式EMD研究

单片机技术日趋成熟,生活中嵌入式技术设备给大家的学习和生活带来便利。产品的高经济效益促进EMD技术发展,本文对EMD系统的结构和EMDB的需求、特点进行论述。     

An imbalance fault detection method based on data normalization and EMD for marine current turbines

This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method.     

基于经验模式分解的支持向量数据描述用于机械设备状态评估研究

机械行业中的大型关键设备一般没有足够故障数据作为其运行状态的参考,对于这类设备的监测研究就更为重要。文中利用机械设备正常运行时的信息作为样本,利用EMD自适应分解采集到的数据,作为SVDD单值分类器的输入来判断机械设备运行状态,经滚动轴承实验,得到了较好的运行状态评估效果。     

基于经验模态分解的桥式起重机振动模态分析

以机械系统模态分析理论为基础，分析了桥式起重机简支梁模型振动特性，提出了基于经验模态分解的桥式起重机模态分析方法。简支梁振动响应通过经验模态分解处理得到固有模态函数，分析表明固有模式函数与振动模式函数具有较高的一致性。最后通过简支梁振动实验验证了桥式起重机EMD分析方法的可实现性。基于EMD的桥式起重机模态分析方法对于在用、维修和改造桥式起重机的模态分析具有重要意义。     

Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

Wind speed is the major factor that affects the wind generation, and in turn the forecasting accuracy of wind speed is the key to wind power prediction. In this paper, a wind speed forecasting method based on improved empirical mode decomposition (EMD) and GA-BP neural network is proposed. EMD has been applied extensively for analyzing nonlinear stochastic signals. Ensemble empirical mode decomposition (EEMD) is an improved method of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each signal is taken as an input data to the GA-BP neural network model. The final forecasted wind speed data is obtained by aggregating the predicted data of individual signals. Cases study of a wind farm in Inner Mongolia, China, shows that the proposed hybrid method is much more accurate than the traditional GA-BP forecasting approach and GA-BP with EMD and wavelet neural network method. By the sensitivity analysis of parameters, it can be seen that appropriate settings on parameters can improve the forecasting result. The simulation with MATLAB shows that the proposed method can improve the forecasting accuracy and computational efficiency, which make it suitable for on-line ultra-short term (10 min) and short term (1 h) wind speed forecasting.     

Adaptive noise cancellation based on EMD in water-supply pipeline leak detection

In water-supply pipeline leak detection and location, both the leak signals and blurred noises are closely related to the pipeline states and surroundings and most of the conventional noise-cancellation methods have to depend on the empirical parameters of either signals or noises. EMD (Empirical Mode Decomposition) is an adaptive signal decomposition method and is exclusive of base functions. A signal is decomposed into several IMFs (Intrinsic Mode Functions) in EMD, then the noise in a signal can be cancelled through removing uncorrelated IMFs. The existing EMD noise cancellation methods need to know the characteristics of either the wanted signal or the noise for rebuilding the noise-removed signal. However the characteristics of leak signals and noises are not fixed in various pipeline conditions, so the existing EMD noise cancellation methods can’t be directly applied in water-supply pipeline leak detection. This paper proposes an adaptive noise cancellation method based on EMD, in which the IMFs that don’t or less contain the components related to the leak can be removed through the cross-correlation between the IMFs and another signal collected at the either side of a suspect leak. In simulation analysis, the adaptive noise cancellation method can increase the SNRs (Signal to Noise Ratios) of leak signals as high as 16 dB. In processing practical pipeline vibro-acoustic signals, with the proposed method the peak of adaptive time delay estimate of leak signals, which determines the location of a leakage, becomes more distinguished, and thus the error of leakage location is improved.     

通过磁化提高电解二氧化锰放电性能

介绍了J.Leddy和J.P.Tesene进行的通过磁化作用改变电解二氧化锰在碱性锌锰电池中的放电机理的试验,实验结果表明磁化可提高碱锰电池放电性能和可充性,讨论了该项技术的应用领域和市场前景。     

振动模态固有频率和阻尼比的EMD识别方法

针对机械系统固有频率和阻尼比的识别问题,提出了基于经验模式分解(EMD)的模态参数识别方法.该方法首先对脉冲激励下机械系统的位移响应进行了EMD分解,确定与该系统的各阶模态对应的固有模式函数(IMF),分别对各阶IMF进行希尔伯特变换以得到各自的瞬时幅值和瞬时相位曲线,并对所得曲线进行线性拟合,最后根据拟合曲线的参数来...     

基于EMD和分形的齿轮箱特征提取技术研究

齿轮箱故障诊断实质上是一个特征提取和状态识别的过程,在旋转机械研究中,其特征频率往往是已知或者可以计算的,而实际测量的机械振动信号往往含有故障特征以外的频率成分,为了避免多余信息的干扰,提出了结合EMD和分形几何算法的齿轮箱有效频率分量的特征提取方法。实验表明,该方法可以有效地计算出不同齿轮故障的特征值,实现了定量表征。