scholarly journals A Study of Nonstationary Wind Effects on a Full-Scale Large Cooling Tower Using Empirical Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
X. X. Cheng ◽  
J. Dong ◽  
Y. Peng ◽  
L. Zhao ◽  
Y. J. Ge
Keyword(s):  
Empirical Mode Decomposition ◽  
Cooling Tower ◽  
Time History ◽  
Field Measurements ◽  
Wind Pressure ◽  
Wind Effects ◽  
Intrinsic Mode Functions ◽  
Low Frequencies ◽  
Mode Decomposition ◽  
Pressure Time

Wind effects on structures obtained by field measurements are often found to be nonstationary, but related researches shared by the wind-engineering community are still limited. In this paper, empirical mode decomposition (EMD) is applied to the nonstationary wind pressure time-history samples measured on an actual 167-meter high large cooling tower. It is found that the residue and some intrinsic mode functions (IMFs) of low frequencies produced by EMD are responsible for the samples’ nonstationarity. Replacing the residue by the constant mean and subtracting the IMFs of low frequencies can help the nonstationary samples become stationary ones. A further step is taken to compare the loading characteristics extracted from the original nonstationary samples with those extracted from the processed stationary samples. Results indicate that nonstationarity effects on wind loads are notable in most cases. The passive wind tunnel simulation technique based on the assumption of stationarity is also examined, and it is found that the technique is basically conservative for use.

SIMULATION OF EARTHQUAKE RECORDS BY MEANS OF EMPIRICAL MODE DECOMPOSITION AND HILBERT SPECTRAL ANALYSIS

2014 ◽  
Vol 08 (01) ◽  
pp. 1450002 ◽  
Author(s):  
ABDOLLAH BAGHERI ◽  
AMIR A. FATEMI ◽  
GHOLAMREZA GHODRATI AMIRI
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
Time History ◽  
Response Spectra ◽  
Frequency Function ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Earthquake Records ◽  
Mode Decomposition ◽  
Time Histories

One of the most important problems in the design of earthquake resistance structures at sites with no strong ground motion data is the generation and simulation of earthquake records. In this paper, an effective method based on Hilbert–Huang transform for the simulation of earthquake time histories is presented. The Hilbert–Huang transform consists of the empirical mode decomposition and Hilbert spectral analysis. Earthquake time histories decompose via empirical mode decomposition to obtain the intrinsic mode functions of earthquake time history. Any of intrinsic mode functions is simulated based on the proposed method for simulation. The ground frequency function of the presented model is estimated using Hilbert spectral analysis for the simulation of earthquake accelerograms. The proposed method has been applied to three earthquake records to demonstrate the efficiency and reliability of the approach. The obtained results of simulating method by comparison between pseudo-acceleration and pseudo-velocity response spectra of actual and the average of simulated time histories for these three earthquakes reveal that the simulated earthquake time histories well preserve the significant properties and the nonstationary characteristics of the actual earthquake records. The results indicated that there is a good accord between the response spectra of simulated and genuine time histories.

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

scholarly journals Empirical Mode Decomposition in Discrete Time Signals Denoising

2019 ◽  
Vol 16 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Zoltán Germán-Salló
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Discrete Wavelet ◽  
Intrinsic Mode Functions ◽  
Nonlinear Signal Processing ◽  
Mode Decomposition ◽  
Time Signals ◽  
Nonlinear Signal ◽  
Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

<p>Abstract</p><p>Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.</p><p>In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.</p><p> </p><p>Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought</p>

scholarly journals Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

Acoustic Signals Analysis Based on Empirical Mode Decomposition and Spectrum Analysis Technique

2010 ◽  
Vol 40-41 ◽  
pp. 91-95 ◽  
Author(s):  
Yan Li Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Acoustic Signals ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Marginal Spectrum ◽  
Rock Interface ◽  
Coal Rock ◽  
Instantaneous Energy

A method to analyze the acoustic signals collected in fully-mechanized caving face is presented in this paper. Through analyzing the marginal spectrum and frequency spectrum of intrinsic mode functions obtained by empirical mode decomposition, acoustic signals’ frequency and amplitude characteristics are gotten, that is, high frequency signals about 1000Hz ~2800Hz are produced when the top coal is combined with gangue. Furthermore, the acoustic signals’ instantaneous energy spectrums in the frequency range of 1000Hz ~2800Hz can be used to identify the coal-rock interface.

Adaptive Ultrasound Tissue Harmonic Imaging Based on an Improved Ensemble Empirical Mode Decomposition Algorithm

2020 ◽  
Vol 42 (2) ◽  
pp. 57-73
Author(s):  
Suya Han ◽  
Yufeng Zhang ◽  
Keyan Wu ◽  
Bingbing He ◽  
Kexin Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Second Harmonic ◽  
Ensemble Empirical Mode Decomposition ◽  
Harmonic Imaging ◽  
Motion Artifacts ◽  
Intrinsic Mode Functions ◽  
Tissue Harmonic Imaging ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Harmonic Components

Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.

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