Search for the 531 day-period wobble signal in the polar motion based on EEMD

Abstract. In this study, we use a nonlinear and non-stationary time series analysis method, the ensemble empirical mode decomposition method (EEMD), to analyze the polar motion (PM) time series (EOP C04 series from 1962 to 2013) to find a 531 day-period wobble (531 dW) signal. The 531 dW signal has been found in the early PM seires (1962–1977) while cannot be found in the recent PM seires (1978–2013) using conventional analysis approaches. By the virtue of the demodulation feature of EEMD, the 531 dW can be confirmed to be present in PM based on the differences of the amplitudes and phases between different intrinsic mode functions. Results from three sub-series divided from the EOP C04 series show that the period of the 531 dW is subject to variations, in the range of 530.9–524 d, and its amplitude is also time-dependent (about 2–11 mas). Synthetic tests are carried out to explain why the 531 dW can only be observed in recent 30-years PM time series after using EEMD. The 531 dW is also detected in two longest available superconducting gravimeter (SG) records, which further confirms the presence of the 531 dW. The confirmation of 531 dW existence could be significant in establishing a more reasonable Earth rotation model and may effectively contribute to the prediction of the PM and its mechanism interpretation.

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Search for the 531-day-period wobble signal in the polar motion based on EEMD

Nonlinear Processes in Geophysics ◽

10.5194/npg-22-473-2015 ◽

2015 ◽

Vol 22 (4) ◽

pp. 473-484 ◽

Cited By ~ 1

Author(s):

H. Ding ◽

W. Shen

Keyword(s):

Time Series ◽

Polar Motion ◽

Superconducting Gravimeter ◽

Ensemble Empirical Mode Decomposition ◽

Stationary Time Series ◽

Analysis Method ◽

Intrinsic Mode Functions ◽

Conventional Analysis ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method

Abstract. In this study, we use a nonlinear and non-stationary time series analysis method, the ensemble empirical mode decomposition method (EEMD), to analyze the polar motion (PM) time series (EOP C04 series from 1962 to 2013) to find a 531-day-period wobble (531 dW) signal. The 531 dW signal has been found in the early PM series (1962–1977), but cannot be found in the recent PM series (1978–2013) using conventional analysis approaches. By virtue of the demodulation feature of EEMD, the 531 dW can be confirmed to be present in PM based on the differences of the amplitudes and phases between different intrinsic mode functions. Results from three sub-series divided from the EOP C04 series show that the period of the 531 dW is subject to variations, in the range of 530.9–524 days, and its amplitude is also time-dependent (about 2–11 mas). Synthetic tests are carried out to explain why the 531 dW can only be observed in recent 30-year PM time series after using EEMD. The 531 dW is also detected in the two longest available superconducting gravimeter (SG) records, which further confirms the presence of the 531 dW. The confirmation of the 531 dW existence could be significant in establishing a more reasonable Earth rotation model and may effectively contribute to the prediction of the PM and its mechanism interpretation.

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

10.5194/egusphere-egu21-10609 ◽

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

AbstractMost 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.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.&#160;Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought

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Trends analysis of precipitation data over the tropical South-West Indian Ocean (SWIO) basin using the Ensemble Empirical Mode Decomposition (EEMD) method

MAUSAM ◽

10.54302/mausam.v67i2.1341 ◽

2021 ◽

Vol 67 (2) ◽

pp. 423-430

Author(s):

K. BOODHOO ◽

M. R. LOLLCHUND ◽

A. F. DILMAHAMOD

Keyword(s):

Time Series ◽

Indian Ocean ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

South West Indian Ocean ◽

South West ◽

Mode Decomposition ◽

West Indian Ocean ◽

Eemd Method

In this paper, we propose the use of the Ensemble Empirical Mode Decomposition (EEMD) method in the analysis of trends in climate data. As compared to existing traditional methods, EEMD is simple, fast and reliable. It works by decomposing the time-series data into intrinsic mode functions until a residual component is obtained which represents the trend in the data. The dataset considered consists of satellite precipitation estimates (SPE) obtained from the Tropical Rainfall Measuring Mission (TRMM) for the tropical South-West Indian Ocean (SWIO) basin recorded during the periods January 1998 to December 2013. The SWIO basin spans from the latitudes 5° S to 35° S and the longitudes 30° E to 70° E and comprises of part of the east coast of Africa and some small island developing states (SIDS) such as Comoros, Madagascar, Mauritius and Reunion Island. The EEMD analysis is carried out for summer, winter and yearly time series of the SPE data. The results from the study are presented in terms of intrinsic mode functions (IMFs) and the trends. The analysis reveals that in summer, there is a tendency to have an increase in the amount of rainfall, whereas in winter, from 1998 to 2004 there has been an initial increase of 0.0022 mm/hr/year and from there onwards till 2013 a decrease of 0.00052 mm/hr/year was noted.

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The Mechanical Fault Diagnosis Method Based on EMD and Time Series Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.2330 ◽

2014 ◽

Vol 602-605 ◽

pp. 2330-2333 ◽

Cited By ~ 1

Author(s):

Jun Ma ◽

Shi Hai Zhang

Keyword(s):

Time Series ◽

Fault Diagnosis ◽

Time Series Analysis ◽

Mechanical Vibration ◽

Vibration Signal ◽

Vibration Monitoring ◽

Analysis Method ◽

Series Analysis ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method

It is the precondition of vibration fault diagnosis technology that appropriate signal analysis method is applied to separate mechanical fault character message from vibration monitoring signal. Based on the characteristics of multi-exciting, multi-model, non-stationary, nonlinear of the complex mechanical vibration signal, the EMD (Empirical Mode Decomposition) method is firstly applied to decompose the most refined IMF (Intrinsic Mode Function) components of vibration monitoring signal, and then the time series analysis method is applied to estimate power spectrums of IMF components and separate the fault character messages. The feasibility and advantage of the associated method are proved by analyzing the diesel engine crankshaft vibration monitoring signal in the paper.

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Potato price analysis of Delhi market through ensemble empirical mode decomposition

Bhartiya Krishi Anusandhan Patrika ◽

10.18805/bkap147 ◽

2019 ◽

Vol 34 (01) ◽

Author(s):

Kapil Choudhary ◽

Girish Kumar Jha ◽

Rajeev Ranjan Kumar

Keyword(s):

Time Series ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Agricultural Commodities ◽

Intrinsic Mode Functions ◽

Price Series ◽

Mode Decomposition ◽

Non Linear ◽

Agricultural Price ◽

Commodities Prices

Agricultural commodities prices depends on production, unnecessary demand, production uncertainty, market flaws etc. Due to these factors agricultural price series are non-stationary and non-linear in nature. Therefore analyzing agricultural commodities prices is considered as a challenging task. The traditional stationary approach of time series is unable to capture non-stationary and non-linear properties of agricultural price series. Non-stationary and non-linear properties present in the price series may be accurately analyzed through empirical mode decongation (EMD). In this technique, the original time series decomposed into intrinsic mode functions and residue. One of the major limitation of EMD is the presence of the mode mixing. To overcome this limitation of the EMD, we use ensemble empirical mode decomposition (EEMD). Using this technique in this study, Delhi market potato prices have been analyzed.

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The Fault Pattern Identification of Hydraulic Pump Based on the Sensitive Component STSA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.790 ◽

2014 ◽

Vol 635-637 ◽

pp. 790-794

Author(s):

Yu Kui Wang ◽

Hong Ru Li ◽

Peng Ye

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Empirical Mode Decomposition ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Hydraulic Pump ◽

Mode Decomposition ◽

Novel Method ◽

Symbolic Time Series Analysis

A novel method which is based on ensemble empirical mode decomposition (EEMD) and symbolic time series analysis (STSA) was proposed in this paper. Firstly, the vibration signal of hydraulic pump was decomposed into a number of stationary intrinsic mode functions (IMFs). Secondly, the sensitive component was extracted. Finally, the relative entropy (RE) was extracted from the sensitive components and they were used as the indicator to distinguish the faults of hydraulic pump. The research results of actual testing vibration signal demonstrated the rationality and effectiveness of the proposed method in this paper.

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Monthly Streamflow Forecasting Using EEMD-Lasso-DBN Method Based on Multi-Scale Predictors Selection

Water ◽

10.3390/w10101486 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1486 ◽

Cited By ~ 7

Author(s):

Haibo Chu ◽

Jiahua Wei ◽

Jun Qiu

Keyword(s):

Time Series ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Streamflow Forecasting ◽

Multi Scale ◽

Mode Decomposition ◽

Monthly Streamflow ◽

Residual Series ◽

The Usa ◽

Monthly Streamflow Forecasting

For the inherent characteristics of a raw streamflow times series and the complicated relationship between multi-scale predictors and streamflow, monthly streamflow forecasting is very difficult. In this paper, an method was proposed integrating the ensemble empirical mode decomposition (EEMD), least absolute shrinkage and selection operator (Lasso) with deep belief networks (DBN) for forecasting monthly streamflow time series, which is EEMD-Lasso-DBN (ELD) method. To develop the ELD model, the raw streamflow time series was resolved into different elements, including intrinsic mode functions (IMFs) and residue series, using the EEMD technique. The predictors of each IMF element and residue were screened using the Lasso technique from a large number of candidate predictors, respectively. Then, the DBN models were built to simulate the complex relationship between the resolved elements and the selected predictors, respectively. The predicted results of the IMFs and residual series were assembled as an ensemble forecast for the raw streamflow time series and were compared with the other models. The monthly streamflow series from Tennessee, in the USA, were investigated using the ELD method. It was found that each IMF has different characteristics and physical meaning, corresponding to different predictors. The proposed ELD model can significantly improve the accuracy of monthly streamflow forecasting.

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Adaptive complementary ensemble EMD and energy-frequency spectra of cryptocurrency prices

International Journal of Financial Engineering ◽

10.1142/s2424786321410085 ◽

2021 ◽

pp. 2141008

Author(s):

Tim Leung ◽

Theodore Zhao

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Original Time Series ◽

Frequency Spectra ◽

Mode Decomposition ◽

Original Time ◽

Instantaneous Frequencies ◽

Instantaneous Energy

In this study, we study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.

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The extraction of informative vibration components of bearings by different adaptive methods

International Scientific and Technical conference "The Progressive Technics, Technology and Engineering Education" ◽

10.20535/2409-7160.2021.xxii.238175 ◽

2021 ◽

Author(s):

Serhii Mykhalkiv

Keyword(s):

Empirical Mode Decomposition ◽

Decomposition Method ◽

Adaptive Methods ◽

Adaptive Method ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method ◽

Adaptive Noise ◽

Impulse Modulation

It was suggested to select the best adaptive method after proper comparative researches, for the extraction of informative vibration components of bearings. The description and drawbacks of empirical mode decomposition method were presented, and the properties of improved ensemble empirical mode decomposition method and complete ensemble empirical mode decomposition with adaptive noise method were highlighted. A simulated additive signal contained impulse, modulation components and two sinusoids. The extracted intrinsic mode functions were the decomposition results of the first two adaptive methods, which failed to separate impulse and modulation components. Meanwhile, the intrinsic mode functions of the third adaptive method had separately impulse and modulation components, and the method proved to be effective in the separation of the vibration components during the vibrodiagnostics of bearings and gearboxes of the industrial equipment.

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Analysis method of the cavitation vibration signals in poppet valve based on EEMD

Advances in Mechanical Engineering ◽

10.1177/1687814021998114 ◽

2021 ◽

Vol 13 (2) ◽

pp. 168781402199811

Author(s):

Beibei Li ◽

Qiao Zhao ◽

Huaiyi Li ◽

Xiumei Liu ◽

Jichao Ma ◽

...

Keyword(s):

Ensemble Empirical Mode Decomposition ◽

Center Frequency ◽

Analysis Method ◽

Vibration Signals ◽

Poppet Valve ◽

Mode Decomposition ◽

Marginal Spectrum ◽

Cavitation Intensity ◽

Eemd Method ◽

Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

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