Research on Feature Extraction for Ultrasonic Echo Signal Based on EEMD Approach

2013 ◽  
Vol 321-324 ◽  
pp. 1311-1316 ◽  
Author(s):  
Jian Ming Yu ◽  
Ze Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Thin Plates ◽  
Echo Signal ◽  
Stationary Characteristic ◽  
Bonding Quality ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Ultrasonic Echo

The bonding quality of composite materials have a critical influence on the quality of the product in modern industry, while the current technology can only make judgments on bonding and de-bonding instead of quantitative evaluation of different de-bonding degrees. We present HHT method to extract features of echo signals used for quantitative recognition of bonding quality of thin plates. For the non-stationary characteristic of the ultrasonic echo signal, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) are put forward to decompose the signal and calculate its energy torque. The HHT method highlights the time-frequency performance of echo signals effectively. The simulated signals verify that EEMD has more excellent decomposition performance than EMD, that is, EEMD diminishes the mode mixing to some extent generated from EMD decomposition.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

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 Radar Signal Emitter Recognition Based on Combined Ensemble Empirical Mode Decomposition and the Generalized S-Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Bin Liu ◽  
Youqian Feng ◽  
Zhonghai Yin ◽  
Xiangyu Fan
Keyword(s):  
Empirical Mode Decomposition ◽  
Prior Information ◽  
Ensemble Empirical Mode Decomposition ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Mode Decomposition ◽  
S Transform ◽  
Emitter Recognition ◽  
Generalized S Transform

Present radar signal emitter recognition approaches suffer from a dependency on prior information. Moreover, modern emitter recognition must meet the challenges associated with low probability of intercept technology and other obscuration methodologies based on complex signal modulation and must simultaneously provide a relatively strong ability for extracting weak signals under low SNR values. Therefore, the present article proposes an emitter recognition approach that combines ensemble empirical mode decomposition (EEMD) with the generalized S-transform (GST) for promoting enhanced recognition ability for radar signals with complex modulation under low signal-to-noise ratios in the absence of prior information. The results of Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise are reported. The results verify that EEMD suppresses the occurrence of mode mixing commonly observed using standard empirical mode decomposition. In addition, EEMD is shown to extract meaningful signal features even under low SNR values, which demonstrates its ability to suppress noise. Finally, EEMD-GST is demonstrated to provide an obviously better time-frequency focusing property than that of either the standard S-transform or the short-time Fourier transform.

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

scholarly journals Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation

2018 ◽  
Vol 50 (2) ◽  
pp. 498-516 ◽  
Author(s):  
Mohammad Rezaie-Balf ◽  
Ozgur Kisi ◽  
Lloyd H. C. Chua
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Support Vector ◽  
Pan Evaporation ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Testing Stage ◽  
Adaptive Time ◽  
Two Hybrid

Abstract Accurate prediction of pan evaporation (PE) is one of the crucial factors in water resources management and planning in agriculture. In this research, two hybrid models, self-adaptive time-frequency methodology, ensemble empirical mode decomposition (EEMD) coupled with support vector machine (EEMD-SVM) and EEMD model tree (EEMD-MT), were employed to forecast monthly PE. The EEMD-SVM and EEMD-MT were compared with single SVM and MT models in forecasting monthly PE, measured between 1975 and 2008, at Siirt and Diyarbakir stations in Turkey. The results were evaluated using four assessment criteria, Nash–Sutcliffe Efficiency (NSE), root mean square error (RMSE), performance index (PI), Willmott's index (WI), and Legates–McCabe's index (LMI). The EEMD-MT model respectively improved the accuracy of MT by 36 and 44.7% with respect to NSE and WI in the testing stage for the Siirt station. For the Diyarbakir station, the improvements in results were less spectacular, with improvements in NSE (1.7%) and WI (2.2%), respectively, in the testing stage. The overall results indicate that the proposed pre-processing technique is very promising for complex time series forecasting and further studies incorporating this technique are recommended.

Identification of Power Quality Disturbances Based on EEMD and TEO

2013 ◽  
Vol 433-435 ◽  
pp. 469-476 ◽  
Author(s):  
Song Jun Wang ◽  
Qing Fen Liao ◽  
Di Chen Liu ◽  
Yu Tian Zhou ◽  
Bin Kun Xu ◽  
...  
Keyword(s):  
Power Quality ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Ensemble Empirical Mode Decomposition ◽  
Good Time ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Power Quality Disturbance ◽  
Stationary Signals ◽  
Power Quality Disturbances

The empirical mode decomposition (EMD) is a good time-frequency analysis method, which can deal with nonlinear and non-stationary signals. Aiming at improving modal aliasing problem brought by the traditional EMD, white noise is introduced into the improved aided analysis algorithm namely ensemble empirical mode decomposition (EEMD), instantaneous amplitude and frequency can be obtained by using teager energy operator (TEO), which is adopted to identify the type of power quality disturbance. The anti-aliasing of EEMD and real-time detection of TEO are verified by the signal simulation in Matlab. Simulation and experimental results show that the proposed algorithm can detect and locate power quality disturbances accurately and quickly, with excellent detection effects.

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