scholarly journals Quantification of Dynamic Properties of Pile Using Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Feng Xiao ◽  
Gang S. Chen ◽  
Wael Zatar ◽  
J. Leroy Hulsey
Keyword(s):  
Empirical Mode Decomposition ◽  
Dynamic Properties ◽  
Ensemble Empirical Mode Decomposition ◽  
Frozen Soil ◽  
Frozen Ground ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Pile Response ◽  
Emd Method ◽  
Mode Mixing

This paper investigated dynamical interactions between pile and frozen ground by using the ensemble empirical mode decomposition (EEMD) method. Unlike the conventional empirical mode decomposition (EMD) method, EEMD is found to be able to separate the mode patterns of pile response signals of different scales without causing mode mixing. The identified dynamic properties using the EEMD method are more accurate than those obtained from conventional methods. EEMD-based results can be used to reliably and accurately characterize pile-frozen soil interactions and help designing infrastructure foundations under permafrost condition.

An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Shi-Chang Du ◽  
Tao Liu ◽  
De-Lin Huang ◽  
Gui-Long Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Decomposition ◽  
Critical Parameters ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Two Parameters ◽  
Mixing Problem ◽  
Mode Mixing

The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical parameters (i.e., the amplitude of added white noise and the number of ensemble trials) are not selected appropriately. A novel EEMD method with optimized two parameters is proposed to solve the mode mixing problem in vibration signal decomposition in this paper. In the proposed optimal EEMD, the initial values of the two critical parameters are selected based on an adaptive algorithm. Then, a multimode search algorithm is explored to optimize the critical two parameters by its good performance in global and local search. The performances of the proposed method are demonstrated by means of a simulated signal, two bearing vibration signals, and a vibration signal in a milling process. The results show that compared with the traditional EEMD method and other improved EEMD method, the proposed optimal EEMD method automatically obtains the appropriate parameters of EEMD and achieves higher decomposition accuracy and faster computational efficiency.

scholarly journals On the Use of Ensemble Empirical Mode Decomposition for the Identification of Bridge Frequency from the Responses Measured in a Passing Vehicle

2019 ◽  
Vol 4 (2) ◽  
pp. 32 ◽  
Author(s):  
Licheng Zhu ◽  
Abdollah Malekjafarian
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Noise ◽  
Profile Measurement ◽  
Fe Method ◽  
The Road ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Mixing Problem ◽  
Mode Mixing

In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is created using the finite element (FE) method in Matlab. The EMD is employed to decompose the signals measured on the vehicle to their main components. It is shown that the bridge component of the response measured on the vehicle can be extracted using the EMD method. The influence of some factors, such as the road roughness profile and measurement noise, on the results are investigated. The results suggest that the EMD shows good performance under those conditions, but the accuracy of the results may still need to be improved. It is shown that in some cases, the EMD may not be able to decompose the signal effectively and includes mode mixing. This results in inaccuracies in the identification of bridge frequencies. The use of the ensemble empirical mode decomposition (EEMD) method is proposed to overcome the mode mixing problem. The influence of factors such as road profile, measurement noise and vehicle velocity are investigated. It is numerically demonstrated that employing the EEMD improves the results compared to the EMD.

Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

2017 ◽  
Vol 09 (02) ◽  
pp. 1750004 ◽  
Author(s):  
Pawel Rzeszucinski ◽  
Michal Juraszek ◽  
James R. Ottewill
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Diagnostic Information ◽  
Optimal Parameters ◽  
Vibration Data ◽  
Mode Decomposition ◽  
Ultrasound Signal ◽  
Diagnostic Capabilities ◽  
Mixing Problem ◽  
Mode Mixing

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.

An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

2014 ◽  
Author(s):  
Wei Guo
Keyword(s):  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Similarity Criterion ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Combination Technique ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Eemd Method ◽  
Effective Signal

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.

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

MAUSAM ◽  
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.  

scholarly journals Morphological Filter-Assisted Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaohang Zhou ◽  
Deshan Shan ◽  
Qiao Li
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Measured Signal ◽  
Morphological Filter ◽  
Structuring Element ◽  
Mode Decomposition ◽  
Additional Mode ◽  
Adaptive Ability ◽  
Mode Mixing

In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to improve the adaptive ability of morphological filter (MF). Then, the adaptive MF was introduced into EMD to remove the superfluous white noise components to improve the decomposition results. Based on the contributions of MF in a single EMD process, the MF-EEMD was proposed by combining EEMD with MF to suppress the mode mixing problems. Finally, an analog signal and a measured signal were used to verify the feasibility of MF-EEMD. The results show that MF-EEMD significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD.

scholarly journals Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China

2021 ◽  
Vol 14 (1) ◽  
pp. 15
Author(s):  
Peng Xue ◽  
Huiyu Liu ◽  
Mingyang Zhang ◽  
Haibo Gong ◽  
Li Cao
Keyword(s):  
Empirical Mode Decomposition ◽  
Net Primary Productivity ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Ecological Engineering ◽  
Ensemble Empirical Mode Decomposition ◽  
Vegetation Types ◽  
Nonlinear Characteristics ◽  
Mode Decomposition ◽  
Eemd Method

Monitoring vegetation net primary productivity (NPP) is very important for evaluating ecosystem health. However, the nonlinear characteristics of the vegetation NPP remain unclear in the six provinces along the Maritime Silk Road in China. In this study, using NDVI and meteorological data from 1982 to 2015, NPP was estimated with the Carnegie-Ames-Stanford Approach (CASA) model based on vegetation type dynamics, and its nonlinear characteristics were explored through the ensemble empirical mode decomposition (EEMD) method. The results showed that: (1) The total NPP in the changed vegetation types caused by ecological engineering and urbanization increased but decreased in those caused by agricultural reclamation and vegetation destruction, (2) the vegetation NPP was dominated by interannual variations, mainly in the middle of the study area, while by long-term trends, mainly in the southwest and northeast, (3) for most of the vegetation types, NPP was dominated by the monotonically increasing trend. Although vegetation NPP in the urban land mainly showed a decreasing trend (monotonic decrease and decrease from increase), there were large areas in which NPP increased from decreasing. Although vegetation NPP in the farmland mainly showed increasing trends, there were large areas that faced the risk of NPP decreasing; (4) dynamical changes of vegetation type by agricultural reclamation and vegetation destruction made the NPP trend monotonically decrease in large areas, leading to ecosystem degradation, while those caused by urbanization and ecological engineering mainly made the NPP increase from decreasing, leading to later recovery from early degradation. Our results highlighted the importance of vegetation type dynamics for accurately estimating vegetation NPP, as well as for assessing their impacts, and the importance of nonlinear analysis for deepening our understanding of vegetation NPP changes.

scholarly journals Analysis of Vehicle Platform Vibration Based on Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chengwu Shen ◽  
Zhiqian Wang ◽  
Chang Liu ◽  
Qinwen Li ◽  
Jianrong Li ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Measurement Accuracy ◽  
Vibration Isolation ◽  
Principal Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Data ◽  
Mode Decomposition ◽  
Operation Process ◽  
Vehicle Platform ◽  
Mode Mixing

Vehicle platform vibration (VPV) directly affects the measurement accuracy of precise measuring instrument (PMI) fixed on it. In order to reduce the influences of VPV on measurement accuracy, it is necessary to perform vibration isolation between vehicle platform and PMI. Analysis of vibration characteristics is a prerequisite for vibration isolation. However, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) reveal that there is obvious mode mixing phenomenon in the collected VPV signals. In this paper, a noise stretch ensemble empirical mode decomposition (NSEEMD) method is proposed to suppress mode mixing, and the specific operation process of NSEEMD is expounded. By NSEEMD, mode mixing of the collected platform vibration data is well suppressed, and the principal component of platform vibration can be obtained.

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