scholarly journals Output-Only Modal Analysis Based on Improved Empirical Mode Decomposition Method

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shiqiang Qin ◽  
Qiuping Wang ◽  
Juntao Kang
Keyword(s):  
Modal Analysis ◽  
Empirical Mode Decomposition ◽  
Modal Parameters ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Random Decrement Technique ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Output Only ◽  
Emd Method

The output-only modal analysis for bridge structures based on improved empirical mode decomposition (EMD) is investigated in this study. First, a bandwidth restricted EMD is proposed for decomposing nonstationary output measurements with close frequency components. The advantage of bandwidth restricted EMD to standard EMD is illustrated by a numerical simulation. Next, the modal parameters are extracted from intrinsic mode function obtained from the improved EMD by both random decrement technique and stochastic subspace identification. Finally, output-only modal analysis of a railway bridge is presented. The study demonstrates the mode mixing issues of standard EMD can be restrained by introducing bandwidth restricted signal. Further, with the improved EMD method, band-pass filter is no longer needed for separating the closely spaced frequency components. The modal parameters extracted based on the improved EMD method show good agreement with those extracted by conventional modal identification algorithms.

Application of EMD to Integrated Signal Trend Extraction

2012 ◽  
Vol 591-593 ◽  
pp. 2072-2076 ◽  
Author(s):  
Ye Qu Chen ◽  
Wen Zheng ◽  
Xie Ben Wei
Keyword(s):  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Data Driven ◽  
Pass Filter ◽  
Effective Implementation ◽  
Trend Extraction ◽  
Mode Decomposition ◽  
Calculation Results ◽  
High Pass ◽  
Emd Method

Huang’s data-driven technique of Empirical Mode Decomposition (EMD) is presented, and issues related to its effective implementation are discussed. Integrating signal directly will produce a trend, it will cause distortion and interfere with the calculation results. This paper discusses the reasons that cause the integrated signal trend, compares the different methods for extracting trend. The traditional steps use the linear fitting and a high-pass filter to remove low frequency signal to extract trend. This paper uses Empirical Mode Decomposition (EMD) method to extract integrated signals trend, discussed the advantages of Empirical Mode Decomposition (EMD) method in this case, proves that Empirical Mode Decomposition (EMD) has a good application in integrated signal trend extraction.

APPLICATION OF THE EMPIRICAL MODE DECOMPOSITION METHOD TO GROUND-ROLL NOISE ATTENUATION IN SEISMIC DATA

2013 ◽  
Vol 31 (4) ◽  
pp. 619 ◽  
Author(s):  
Luiz Eduardo Soares Ferreira ◽  
Milton José Porsani ◽  
Michelângelo G. Da Silva ◽  
Giovani Lopes Vasconcelos
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Low Frequency ◽  
High Amplitude ◽  
Seismic Line ◽  
Seismic Processing ◽  
Mode Decomposition ◽  
Ground Roll ◽  
Fortran 90 ◽  
Emd Method

ABSTRACT. Seismic processing aims to provide an adequate image of the subsurface geology. During seismic processing, the filtering of signals considered noise is of utmost importance. Among these signals is the surface rolling noise, better known as ground-roll. Ground-roll occurs mainly in land seismic data, masking reflections, and this roll has the following main features: high amplitude, low frequency and low speed. The attenuation of this noise is generally performed through so-called conventional methods using 1-D or 2-D frequency filters in the fk domain. This study uses the empirical mode decomposition (EMD) method for ground-roll attenuation. The EMD method was implemented in the programming language FORTRAN 90 and applied in the time and frequency domains. The application of this method to the processing of land seismic line 204-RL-247 in Tacutu Basin resulted in stacked seismic sections that were of similar or sometimes better quality compared with those obtained using the fk and high-pass filtering methods.Keywords: seismic processing, empirical mode decomposition, seismic data filtering, ground-roll. RESUMO. O processamento sísmico tem como principal objetivo fornecer uma imagem adequada da geologia da subsuperfície. Nas etapas do processamento sísmico a filtragem de sinais considerados como ruídos é de fundamental importância. Dentre esses ruídos encontramos o ruído de rolamento superficial, mais conhecido como ground-roll . O ground-roll ocorre principalmente em dados sísmicos terrestres, mascarando as reflexões e possui como principais características: alta amplitude, baixa frequência e baixa velocidade. A atenuação desse ruído é geralmente realizada através de métodos de filtragem ditos convencionais, que utilizam filtros de frequência 1D ou filtro 2D no domínio fk. Este trabalho utiliza o método de Decomposição em Modos Empíricos (DME) para a atenuação do ground-roll. O método DME foi implementado em linguagem de programação FORTRAN 90, e foi aplicado no domínio do tempo e da frequência. Sua aplicação no processamento da linha sísmica terrestre 204-RL-247 da Bacia do Tacutu gerou como resultados, seções sísmicas empilhadas de qualidade semelhante e por vezes melhor, quando comparadas as obtidas com os métodos de filtragem fk e passa-alta.Palavras-chave: processamento sísmico, decomposição em modos empíricos, filtragem dados sísmicos, atenuação do ground-roll.

Frequency decomposition and phase synchronization of the visual evoked potential using the empirical mode decomposition

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Byuckjin Lee ◽  
Byeongnam Kim ◽  
Sun K. Yoo
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Function ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Posterior Lobe ◽  
Time Frequency ◽  
Brain Functions ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Visual Evoked

AbstractObjectivesThe phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.MethodsWe analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.ResultsIn response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.ConclusionsThe VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.

Two-dimensional empirical mode decomposition by finite elements

2006 ◽  
Vol 462 (2074) ◽  
pp. 3081-3096 ◽  
Author(s):  
Y Xu ◽  
B Liu ◽  
J Liu ◽  
S Riemenschneider
Keyword(s):  
Finite Element ◽  
Linear Combination ◽  
Empirical Mode Decomposition ◽  
Spline Interpolation ◽  
Basis Functions ◽  
Two Dimensional ◽  
Element Analysis ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Mode Decomposition

Empirical mode decomposition (EMD) is a powerful tool for analysis of non-stationary and nonlinear signals, and has drawn significant attention in various engineering application areas. This paper presents a finite element-based EMD method for two-dimensional data analysis. Specifically, we represent the local mean surface of the data, a key step in EMD, as a linear combination of a set of two-dimensional linear basis functions smoothed with bi-cubic spline interpolation. The coefficients of the basis functions in the linear combination are obtained from the local extrema of the data using a generalized low-pass filter. By taking advantage of the principle of finite-element analysis, we develop a fast algorithm for implementation of the EMD. The proposed method provides an effective approach to overcome several challenging difficulties in extending the original one-dimensional EMD to the two-dimensional EMD. Numerical experiments using both simulated and practical texture images show that the proposed method works well.

Hilbert-Huang Transform Based Modal Analysis of Structures

2008 ◽  
Author(s):  
Jeng-Wen Lin ◽  
Hung-Jen Chen ◽  
Jeng-Yuan Lin
Keyword(s):  
Modal Analysis ◽  
Signal Reconstruction ◽  
Data Preprocessing ◽  
Structural Safety ◽  
Structural Parameter ◽  
Measured Signal ◽  
Reconstruction Technique ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Hilbert Huang Transform

This paper presents a Hilbert-Huang transform based signal reconstruction technique for the modal analysis of structural systems using vibration measurements. The original measured signal is initially undergone a well defined band-pass filter in order to solve the mode confounding problem. After the data preprocessing, each mode of the signal is reconstructed via the proper selection of intrinsic mode functions (IMFs) that are derived from the empirical mode decomposition of the signal’s mode. Through the signal reconstruction mode by mode, the structural parameter such as natural frequency is accurately evaluated, whose accuracy depends on the criterion for selecting the IMFs using the developed component sifting process. Reliable evaluation of systems’ characteristics leads to accurate prediction of systems’ behaviors for structural safety purpose. In this study, data preprocessing is operated to alleviate the problems of mode mixing and noise contaminated signal, as well as to compare with the previous work.

scholarly journals HHT based analysis on seismic response recordings for a base-isolated building

2019 ◽  
Vol 277 ◽  
pp. 02021
Author(s):  
Fei Wang ◽  
Xiandong Kang ◽  
Ting Yan ◽  
Ying Liu
Keyword(s):  
Seismic Response ◽  
Dominant Frequency ◽  
Data Series ◽  
Structural Systems ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Emd Method

Hilbert-Huang transform (HHT) is proposed to process the seismic response recordings in an 8-story frame-shear wall base-isolated building. Empirical Mode Decomposition (EMD) method is first applied to identify the time variant characteristics and the data series can be decomposed into several components. Hilbert transform is well-behaved in identifying the frequency components. The first 5 intrinsic mode functions (IMFs) are decomposed with their different frequencies. The analytical function is reconstructed and compared with the original signal. They are extremely consistent in amplitude and phase. Based on the IMFs obtained, frequencies of the original signal are inferred at 5 Hz and 1.6 Hz. The higher frequency is regarded as the vibration excited by surface waves. 1.6 Hz is suggested as the dominant frequency of the building. Analysis indicates that HHT is accurate in extracting the dynamic characteristics of structural systems.

scholarly journals Empirical Mode Decomposition in 2-D space and time: a tool for space-time rainfall analysis and nowcasting

2005 ◽  
Vol 9 (3) ◽  
pp. 127-137 ◽  
Author(s):  
S. Sinclair ◽  
G. G. S. Pegram
Keyword(s):  
Empirical Mode Decomposition ◽  
Spatial Scales ◽  
Low Frequency ◽  
Two Dimensions ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Rainfall Analysis ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Temporal Persistence

Abstract. A data-driven method for extracting temporally persistent information, at different spatial scales, from rainfall data (as measured by radar/satellite) is described, which extends the Empirical Mode Decomposition (EMD) algorithm into two dimensions. The EMD technique is used here to decompose spatial rainfall data into a sequence of high through to low frequency components. This process is equivalent to the application of successive low-pass spatial filters, but based on the observed properties of the data rather than the predetermined basis functions used in traditional Fourier or Wavelet decompositions. It has been suggested in the literature that the lower frequency components (those with large spatial extent) of spatial rainfall data exhibit greater temporal persistence than the higher frequency ones. This idea is explored here in the context of Empirical Mode Decomposition. The paper focuses on the implementation and development of the two-dimensional extension to the EMD algorithm and it's application to radar rainfall data, as well as examining temporal persistence in the data at different spatial scales.

Combining Pole Placement and Online Empirical Mode Decomposition Methods to Adaptive Active Control of Structural Vibration

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
S. H. Momeni Massouleh ◽  
S. A. Hosseini Kordkheili ◽  
H. Mohammad Navazi ◽  
H. Bahai
Keyword(s):  
Empirical Mode Decomposition ◽  
Active Control ◽  
Pole Placement ◽  
Structural Vibration ◽  
Control Technique ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Optimal Gains ◽  
Excitation Frequencies ◽  
Emd Method

Using a combination of the pole placement and online empirical mode decomposition (EMD) methods, a new algorithm is proposed for adaptive active control of structural vibration. The EMD method is a time-frequency domain analysis method that can be used for nonstationary and nonlinear problems. Combining the EMD method and Hilbert transform, which is called Hilbert–Huang transform, achieves a method that can be implemented to extract instantaneous properties of signals such as structural response dominant instantaneous frequencies. In the proposed algorithm, these estimated instantaneous properties are utilized to improve the pole-placement method as an adaptive active control technique. The required active control gains are obtained using a genetic algorithm scheme, and optimal gains are calculated corresponding to preselected excitation frequencies. An algorithm is also introduced to choose excitation frequencies based on online EMD method resolution. In order to investigate the efficiency of the proposed method, some case studies that include a discrete model, continuous samples of beam and plate structures, and experimental cantilevered beam are carried out, and the results of the proposed method are compared with the preset (nonadaptive) optimal gains conditions.

scholarly journals Transmissibilty-Based Operational Modal Analysis for Flight Flutter Testing Using Exogenous Inputs

2012 ◽  
Vol 19 (5) ◽  
pp. 1071-1083 ◽  
Author(s):  
Christof Devriendt ◽  
Tim De Troyer ◽  
Gert De Sitter ◽  
Patrick Guillaume
Keyword(s):  
Modal Analysis ◽  
Frequency Domain ◽  
Experimental Modal Analysis ◽  
Operational Modal Analysis ◽  
Modal Parameters ◽  
Input Output ◽  
Output Only ◽  
Flutter Testing

During the recent years several new tools have been introduced by the Vrije Universiteit Brussel in the field of Operational Modal Analysis (OMA) such as the transmissibility based approach and the the frequency-domain OMAX concept. One advantage of the transmissibility based approach is that the ambient forces may be coloured (non-white), if they are fully correlated. The main advantage of the OMAX concept is the fact that it combines the advantages of Operational and Experimental Modal Analysis: ambient (unknown) forces as well as artificial (known) forces are processed simultaneously resulting in improved modal parameters. In this paper, the transmissibility based output-only approach is combined with the input/output OMAX concept. This results in a new methodology in the field of operational modal analysis allowing the estimation of (scaled) modal parameters in the presence of arbitrary ambient (unknown) forces and artificial (known) forces.

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