Acoustic and seismic time series analysis using ensemble empirical mode decomposition

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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New method for nonlinear and nonstationary time series analysis: empirical mode decomposition and Hilbert spectral analysis

10.1117/12.381681 ◽

2000 ◽

Cited By ~ 20

Author(s):

Norden E. Huang

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Time Series Analysis ◽

Empirical Mode Decomposition ◽

Nonstationary Time Series ◽

New Method ◽

Series Analysis ◽

Mode Decomposition

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Postural time-series analysis using Empirical Mode Decomposition and second-order difference plots

2009 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2009.4959639 ◽

2009 ◽

Cited By ~ 14

Author(s):

Ram Bilas Pachori ◽

D. Hewson ◽

H. Snoussi ◽

J. Duchene

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Empirical Mode Decomposition ◽

Second Order ◽

Order Difference ◽

Series Analysis ◽

Mode Decomposition

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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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Application of MEEMD-ARIMA combining model for annual runoff prediction in the Lower Yellow River

Journal of Water and Climate Change ◽

10.2166/wcc.2019.271 ◽

2019 ◽

Vol 11 (3) ◽

pp. 865-876 ◽

Cited By ~ 1

Author(s):

Xianqi Zhang ◽

Wei Tuo ◽

Chao Song

Keyword(s):

Time Series ◽

Empirical Mode Decomposition ◽

Yellow River ◽

Arima Model ◽

Ensemble Empirical Mode Decomposition ◽

Annual Runoff ◽

Lower Yellow River ◽

The Lower Yellow River ◽

Mode Decomposition ◽

Runoff Prediction

Abstract The prediction of annual runoff in the Lower Yellow River can provide an important theoretical basis for effective reservoir management, flood control and disaster reduction, river and beach management, rational utilization of regional water and sediment resources. To solve this problem and improve the prediction accuracy, permutation entropy (PE) was used to extract the pseudo-components of modified ensemble empirical mode decomposition (MEEMD) to decompose time series to reduce the non-stationarity of time series. However, the pseudo-component was disordered and difficult to predict, therefore, the pseudo-component was decomposed by ensemble empirical mode decomposition (EEMD). Then, intrinsic mode functions (IMFs) and trend were predicted by autoregressive integrated moving average (ARIMA) which has strong ability of approximation to stationary series. A new coupling model based on MEEMD-ARIMA was constructed and applied to runoff prediction in the Lower Yellow River. The results showed that the model had higher accuracy and was superior to the CEEMD-ARIMA model or EEMD-ARIMA model. Therefore, it can provide a new idea and method for annual runoff prediction.

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