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1.
Contrary to most signal decomposition methods that usually decompose an original signal into a series of components simultaneously, a novel approach based on repeated extraction of Maximum Energy Component (MEC) is proposed. The approach starts from determination of the MEC referring to the estimated Power Spectral Density (PSD) function, and then represents the MEC by employing an exponential function to fit the original signal. By defining a stopping criterion based on two adjacent estimated PSDs, each MEC can be accurately extracted with an improved performance throughout the entire signal decomposition. To verify the proposed method, a single degree-of-freedom system subject to harmonic loads has been examined. Numerical results show that the analytical response can not only be decomposed into four MECs corresponding to the excitation and the system, respectively, but also provide an accurate estimation of natural frequency and damping ratio of the system. Meanwhile, by observing results from the Ensemble Empirical Mode Decomposition (EEMD), Variational Mode Decomposition (VMD) and Prony based on state-space model (Prony-SS), an improved decomposition accuracy has been achieved from the proposed approach. Furthermore, experimental data from the Norwegian Deepwater Programme and two sets of field-test data from one fixed offshore platform and an offshore wind turbine have been used to demonstrate the correctness of the developed signal decomposition method. It is noted that divergence in results by Prony-SS can be observed when a very large model order is used, while the proposed method provides the better decomposition and reconstruction of signals. 相似文献
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In contrast to Fourier transform,wavelet transform is especially suitable for transient analysis because of its time-frequency characteristics with automatically-adjusted window lengths.Research shows that wavelet transform is one of the most powerful tools for power system transient analysis.The basic ideas of wavelet transform are presented in the paper together with several power system applications,It is clear that wavelet transform has some clear advantages over other transforms in detecting.analyzing,and identifying various types of power system transients. 相似文献
3.
基于经验模式分解的钢轨波浪弯曲不平顺提取方法 总被引:5,自引:3,他引:2
钢轨波浪弯曲不平顺隐藏在轨道不平顺中。采用小波分析和经验模式分解相结合的方法,对钢轨波浪弯曲不平顺进行识别和提取分析。利用对称双正交小波对轨检车监测到的车体垂向加速度响应和高低轨道不平顺信号进行小波变换,滤除波长1 m以下成分后,基本可以保证用经验模式分解得到的第1个固有模态函数包含全部钢轨弯曲不平顺信息。对提取得到的钢轨波浪弯曲不平顺的分析表明,不同钢轨上存在的弯曲不平顺不同,钢轨存在的波浪弯曲不平顺是波长在3 m附近变化的准周期不平顺;钢轨波浪弯曲不平顺是引起车体颤振的原因。建议在提速线路和客运专线上应限制波浪弯曲钢轨上道。 相似文献
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推导和证明了Haar小波连续小波变换时间和尺度的周期性,指出这种周期性可以提取出信号中的周期成分,并且使谐波和冲击信号在时间-尺度域中有不同的图形特征。因此根据这些特性可以识别齿轮裂纹产生的冲击和刚度变化,仿真和汽车变速器齿轮故障分析验证了Haar小波变换的有效性和实用性。 相似文献
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This paper studies the assignment of long-distance passenger traffic on a highway corridor network. First, we propose a traditional model for the long-distance traffic assignment considering interactions with local commuter traffic. It addresses the effect of local networks on highway corridors. An iterative algorithm is developed to solve for the exact solution. Then, to address the potential computational issues that arise therein, a decomposition method is proposed by introducing a new concept of corridor elasticity. An assignment procedure for long-distance passenger traffic is developed accordingly. Numerical tests show that the proposed decomposition method makes significant improvements in computational performance at a small loss of optimality. This decomposition method well approximates the exact assignment from the traditional formulation, especially when the highway corridors are near-saturation. The proposed decomposition method appears practical for application. 相似文献
10.
《Maritime Policy and Management》2012,39(8):957-966
ABSTRACTMaritime shipping necessitates flexible and cost-effective port access worldwide through the global shipping network. This paper presents an efficient method to identify major port communities, and analyses the network connectivity of the global shipping network based on community structure. The global shipping network is represented by a signless Laplacian matrix which can be decomposed to generate its eigenvectors and corresponding eigenvalues. The largest gaps between the eigenvalues were then used to determine the optimal number of communities within the network. The eigenvalue decomposition method offers the advantage of detecting port communities without relying on a priori assumption about the number of communities and the size of each community. By applying this method to a dataset collected from seven world leading liner shipping companies, we found that the ports are clustered into three communities in the global container shipping network, which is consistent with the major container trade routes. The sparse linkages between port communities indicate where access is relatively poor. 相似文献