基于经验模态分解方法的拉曼光谱信号处理研究

经验模态分解(EMD)方法是一个以信号内在物理频率成分为对象的自适应时频分析方法,而常见的非平稳信号分析方法,比如小波分析,它需要选择小波基,不同小波基的分析结果不同;拉曼光谱信号是典型的非线性和非平稳信号,EMD方法充分地保留了信号本身的非线性和非平稳的特征,适应于拉曼光谱信号的分析。实验在自行研制的拉曼光谱测试平台上获得了原始的拉曼光谱信号,并通过经验模态分解将信号分解成不同频率的10个本征模式分量,信号能量集中在750cm^-1和1500cm^-1左右,最后进行了频率成分分析和去噪处理,并和小波分析方法进行了对比,验证了EMD方法的有效性和实用性,该方法在拉曼光谱信号分析中有较好的应用前景。     

EMD-based random decrement technique for modal parameter identification of an existing railway bridge

Vibrational measurement data are often nonstationary and modal parameter identification based on these data is of practical value for structural health monitoring and condition assessment. The empirical mode decomposition (EMD) is a most recent tool for analysis of nonstationary signals. An EMD-based random decrement (RD) technique is presented to identify modal parameters from monitoring vibrational data. The nonstationary measurement data are first decomposed into a series of quasi-stationary intrinsic mode functions (IMFs) by EMD. The RD technique is then applied to the selected IMFs to obtain the free-decay response. The modal frequencies and damping ratios are finally identified from the free-decay response by minimizing the error between the measured free-decay responses and the predicted responses from a parametric model. The present method is applied to extract the modal parameters of the Nanjing Yangtze River Bridge from the measured responses. The identification result is compared to those from finite element analysis as well as from the experimental result identified with the peak-picking (PP) method. In addition, the modal frequencies of the bridge loaded with heavy trains are also identified and compared to the ‘empty’ bridge. The EMD-based random decrement (RD) technique provides an effective and promising tool for modal parameter identification for large bridges and other structures.     

基于分形维数的PMSM局部退磁故障诊断

为了实现永磁同步电机(PMSM)永磁体局部退磁故障诊断,首先建立计及永磁体局部退磁故障的PMSM数学模型,实现永磁体局部退磁故障电气特性的定性与定量描述。在此基础上,提出了集成自适应基波提取和经验模态分解(EMD)于一体,基于分形维数的永磁体局部退磁故障诊断方法。最后,通过PMSM驱动系统稳态和动态运行工况对所提出的永磁体局部退磁故障诊断方法进行仿真测试和实验验证,综合系统仿真和实验验证结果证实了该方法的可行性和有效性。     

超声信号的改进EMD阈值方法降噪研究

在进行工业超声无损检测的过程中,由于环境、仪器等原因产生噪声,对后续缺陷分析等工作形成一定干扰,不利于完成后期数据分析。因此为了使信号的信噪比有所提高,提出了一种改进的经验模态分解阈值降噪算法。同时对比小波软阈值降噪法与经验模态分解清除重复间隔阈值的方法,在MATLAB仿真软件中建立超声回波数学模型并进行算法验证。实验结果表明,经验模态分解降噪方法优于小波阈值降噪,改进后的经验模态分解阈值降噪方法在信噪比、均方误差、光滑度3个方面均有所提高,达到了较好的降噪效果。     

修正阈值的经验模态分解去噪算法研究

针对传统的经验模态分解(EMD)降噪方法容易丢失高频部分有用信号的不足,提出了一种修正阈值的EMD去噪方法。通过考察每个IMF的平滑度来判断其中噪声强度并以此修正用传统方法求得的阈值,再用修正的阈值对IMF进行阈值处理,实现对每个IMF的降噪,进而实现信号去噪的目的。在Matlab构造的超声信号的模型中分别添加3种不同强度的噪声模拟实际的噪声干扰,并进行去噪实验,结果表明修正阈值的EMD去噪方法对不同噪声环境下超声信号均有理想的去噪效果。     

Zero-Sequence Voltage-based Method for Determination and Classification of Unloaded Overhead Line Operating Conditions at the Moment of Energization

Overhead line energization can be performed under normal or faulty conditions. The latter indicates an occurrence of a fault along the line that exists at the moment of energization. This can lead to significant over-voltages that could endanger proper line operation. The issue gets more complex to protective devices when it comes to high-resistance faults, which impair the ability of relays to react promptly and accurately. Consequently, installation of additional detection devices becomes necessary. This paper proposes a novel method that properly detects and classifies line operating conditions at the moment of energization. The method is designed to be useful for utilities and it can be considered as a low-cost, fast, and accurate detection and classification approach, suitable for dealing with both low-resistance and high-resistance faults. Through comprehensive mathematical modeling it was found that both normal and faulty conditions during line energization are accompanied by the zero-sequence voltages of specific characteristics. The differences between zero voltage sequences are reflected in harmonic content, magnitudes of dominant frequencies, and their phase angles in regard to supply voltage. These findings are taken as the method's detection and classification criteria. The validity of the proposed model is verified by simulations and by field measurements.     

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。     

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。     

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。