共查询到16条相似文献，搜索用时 106 毫秒
1.
应用经验模式分解（EMD）将恒电量瞬态响应信号分解为不同时间尺度的内在模函数（IMF）分量。去除其中的小时间尺度的干扰噪声分量。然后经过拉普拉斯变换获得恒电量频谱以研究电化学腐蚀过程。 相似文献
2.
3.
针对全波形三维激光测绘雷达(LiDAR)在数字地形测量中如何降低背景噪声问题,提出了一种基于经验模态分解(EMD)和小波阈值的自适应降噪方法。在扫测的地形信号经EMD分解后,计算内蕴模式函数(IMF)与经过2/3阶重构的扫测信号之间的互相关函数,从而改善小波阈值自适应地对IMF中的高频噪声成分进行滤除。实验结果表明,与EMD重构降噪法、小波阈值降噪法和传统的EMD-小波联合降噪法比较,这种方法在对全波形LiDAR回波信号的噪声剔除和地物信号保留方面具有明显的优势,降噪后信号的误差能缩小10%~20%,波形相关性能提升5%~20%,信噪比能提升20%~40%。 相似文献
4.
5.
本文采用经验模式分解 (EMD)提取信号的内在模函数 (IMF) ,并利用希尔伯特变换对所得IMF进行包络分析 ,提取机械故障特征。与直接对原信号进行包络分析相比较 ,该方法提取的机械故障特征更明显。数值模拟和对故障轴承振动信号分析表明了该方法的有效性。 相似文献
6.
基于EMD与神经网络的机械故障诊断技术 总被引：2，自引：0，他引：2
经验模式分解 (EMD)是分析非线性、非平稳信号的有力工具 ,它将信号分解为突出了原信号的不同时间尺度的局部特征信息的内在模函数 (IMF)分量。本文通过将各 IMF分量输入到 BP网络中进行训练学习和故障诊断 ,比直接输入原信号可以提高 BP网络对故障诊断的准确率 ,而且减少了训练时间。 相似文献
7.
采用径向基函数神经网络法延拓原始数据序列,有效抑制了EMD分解中出现的端点发散效应,从而实现准确的EMD分解。利用该方法对中国近海验潮站的月平均海平面资料进行处理,分解得到的内在模函数分量代表了海平面各种周期性变化。通过EMD分解得到的总体自适定趋势项为非线性变化,比以往趋势项提取方法更有优势,它反映了在资料长度内海平面的长期升降情况。数据序列越长,该方法所能分解出来的IMF成分越多,可分辨的频率越小。 相似文献
8.
9.
本文采用经验模式分解 (EMD)与小波变换相结合的方法分析非平稳机械故障信号的奇异性 ,进行机械故障诊断。与直接对原信号进行小波分析相比较 ,该方法提取的奇异性特征明显。数值模拟和对故障轴承的振动信号分析表明了该方法的有效性。 相似文献
10.
11.
EMD方法和Hilbert谱分析法的应用与探讨 总被引：10，自引：0，他引：10
利用EMD方法对海浪观测资料进行处理 ,通过在数据两端的“平衡位置”处分别附加平行直线段的方法进行端点抑制 ,分解出 1 0个内在模函数和 1个剩余趋势项 ,再对各内在模函数进行Hilbert变换 ,得到波浪的Hilbert谱。对所得结果的分析表明 ,各模态在Hilbert谱中的分布趋势和Fourier谱中谱线的变化趋势是一致的 ,第一模态的中心频率与Fourier谱的谱峰频率相对应 ;EMD方法是对非线性、非平稳过程数据进行距平化的好方法 ,距平化的过程和消除趋势项的处理是统一的。 相似文献
12.
The frequency attenuation gradient method can provide important information for hydrocarbon detection. In this paper, a method using Complete Ensemble Empirical Mode Decomposition (CEEMD), Hilbert transform and the least-squares curve-fitting is proposed for seismic attenuation estimation as an effective frequency attenuation gradient estimation approach. We first use CEEMD to obtain the different Intrinsic Mode Functions (IMFs), which have a narrow band and can enhance the physical meaning of instantaneous attributes trace by trace. The time-frequency spectrum, which is computed using a Hilbert transform of each IMF, is represented as a spectrum with a single-peak that has narrow side lobes, which is conducive to frequency attenuation gradient estimation. Second, for each time sample, the frequency-amplitude spectrum of each IMF trace is extracted from the time-frequency spectrum to conduct the attenuation gradient computation. Then, the logarithm operation is performed for each IMF trace. Due to the very narrow bands of some IMFs in some seismic traces, a variable frequency window is adopted along the IMF trace according to the local data characteristics. Finally, the attenuation gradient for each IMF in a seismic trace can be computed using least-squares fitting. A different IMF reflects a seismic trace with a different spatiotemporal scale and can highlight different geologic and stratigraphic information. The correlation weighted average operation is used to highlight some useful details in seismic trace and obtains the attenuation gradient for each seismic trace. Field data examples demonstrate our method and its effectiveness. The proposed method can stably estimate the frequency attenuation gradient. 相似文献
13.
We use the Hilbert–Huang transform (HHT) for the spectral analysis of a North Sea storm that took place in 1997. We look at the contribution of the different Intrinsic Mode Functions (IMF) obtained using the Empirical Mode Decomposition algorithm, and also compare the Hilbert Marginal Spectra and the classical Fourier Spectra for the data set and the corresponding IMFs. We find that the number of IMFs needed to decompose the data and the energy associated to them is different from previous studies for different sea conditions by other authors. A tentative reason for this may lie in the difference in the sampling rate used. 相似文献
14.
A method based on empirical mode decomposition (EMD) and time-varying autoregressive (TVAR) model is proposed here to identify the modal parameters of time-varying systems, such as the Floating Production Storage and Offloading (FPSO) single point mooring system. For the EMD–TVAR method, the original signal is decomposed into a finite number of ‘intrinsic mode functions’ (IMFs) by the EMD. Each IMF can be represented as a TVAR model. Then, the time-varying modal parameters i.e., instantaneous frequency (IF) and modal dumping, can be obtained by the basis functions expansion method. The proposed EMD–TVAR method has good results in two experiments compared with the Huang–Hilbert transformation and Short Time Fourier Transform method, and it has been used to analysis the modal parameters of FPSO single point mooring system successfully. The system's time-varying characteristic and its frequency distribution can be known from the modal analysis results. 相似文献
15.
16.
Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition （EMD） technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions （IMFs）. In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper anew EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region.Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithmidentifies and extracts the reference signals against various ambient noises. Significant SNR improvement is alsoachieved for underwater acoustic signals. 相似文献