scholarly journals Salinity Interdecadal Variability in the Western Equatorial Pacific and Its Effects During 1942-2018

2021 ◽  
Author(s):  
Rong-Hua Zhang ◽  
Guanghui Zhou ◽  
Hai Zhi ◽  
Chuan Gao ◽  
Hongna Wang ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Equatorial Pacific ◽  
Ocean Reanalysis ◽  
Surface Warming ◽  
Salinity Effects ◽  
Mode Decomposition ◽  
Salinity Variability ◽  
Western Equatorial Pacific ◽  
Subsurface Layers ◽  
Eemd Method

Abstract Ocean reanalysis products are used to examine salinity variability and its relationships with temperature in the western equatorial Pacific during 1942-2018. An ensemble empirical mode decomposition (EEMD) method is adopted to separate salinity and temperature signals at different time scales; a focus is placed on interdecadal component in this study. Pronounced interdecadal variations in salinity are seen in the western equatorial Pacific, which exhibits persistent and transitional phases in association with temperature. A surface freshening is accompanied by a surface warming during the 1980s-1990s, but saltening and cooling in the 2000s, with interdecadal shifts occurring around the late 1970s, late 1990s, and in 2016-2018, respectively. Determined by anomaly signs of temperature and salinity, their combined effects can be density-compensated or density-uncompensated, acting to produce density variability that is suppressed or enhanced, respectively. The effects are phase- and region dependent. In the subsurface layers at 200m, where salinity and temperature anomalies are nearly of the same sign during interdecadal evolution, their effects are mostly density-compensated. The situation is more complicated in the surface layer. Variations in SSS and SST during the persistent phases tend to be of opposite sign with their density-uncompensated effects, acting to enhance density anomalies; but they can be of the same sign during the transitional periods, with density-compensated salinity effects. Examples are given for relationships among these fields which exhibit phase differences in anomaly transitions in the late 1990s in the western equatorial Pacific; salinity anomalies are seen to cause a delay in phase transition of density anomalies. Furthermore, their relative contributions to interdecadal variabilities of density and stratification are quantified. The consequences for salinity effects are also discussed with their feedbacks on local SST.

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

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.

scholarly journals Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Xiong ◽  
Shulin Tian ◽  
Chenglin Yang
Keyword(s):  
Fault Diagnosis ◽  
Relative Entropy ◽  
Analog Circuits ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method ◽  
Learning Machine ◽  
Elm Classifier

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

Denoising of the Base Vibration Signal Based on EEMD

2011 ◽  
Vol 143-144 ◽  
pp. 689-693 ◽  
Author(s):  
X.J. Li ◽  
K. Wang ◽  
G.B. Wang ◽  
Q. Li
Keyword(s):  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Decomposition Level ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Common ◽  
Eemd Method ◽  
Selection Of ◽  
Better Than

Vibration signals of rotating machinery on the base are very weak and always buried in noisy noise; the common denoising methods have become powerless. It presents an ensemble empirical mode decomposition method (EEMD) that is used to denoise for the base vibration signal, which not only to overcome the problem of mode mixing, but also to avoid the selection of wavelet basis function and decomposition level of the problem. Experimental results of simulation and measured data show that EEMD method can effectively reduce the base vibration signal noise, which is better than the wavelet and EMD denoising method.

scholarly journals Upper-Ocean Salinity Variability in the Tropical Pacific: Case Study for Quasi-Decadal Shift during the 2000s Using TRITON Buoys and Argo Floats

2013 ◽  
Vol 26 (20) ◽  
pp. 8126-8138 ◽  
Author(s):  
Takuya Hasegawa ◽  
Kentaro Ando ◽  
Iwao Ueki ◽  
Keisuke Mizuno ◽  
Shigeki Hosoda
Keyword(s):  
Tropical Pacific ◽  
Equatorial Pacific ◽  
Sea Surface ◽  
Upper Ocean ◽  
Salinity Variability ◽  
Ocean Salinity ◽  
Central Equatorial Pacific ◽  
Western Equatorial Pacific ◽  
Sea Surface Temperature Anomalies ◽  
The Tropical Pacific

Abstract Upper-ocean salinity variation in the tropical Pacific is investigated during the 2000s, when Triangle Trans-Ocean Buoy Network (TRITON) buoys and Argo floats were deployed and more salinity data were observed than in previous periods. This study focuses on upper-ocean salinity variability during the warming period of El Niño–Southern Oscillation (ENSO)-like quasi-decadal (QD)-scale sea surface temperature anomalies over the central equatorial Pacific (January 2002–December 2005; hereafter “warm QD phase”). It is shown that strong negative salinity anomalies occur in the western tropical Pacific and the off-equatorial Pacific in the upper ocean at depths less than 80 m, showing a horseshoe-like pattern centered at the western tropical Pacific during the warm QD phase. TRITON mooring buoy data in the western equatorial Pacific show that low-salinity and high-temperature water could be transported eastward from the western equatorial Pacific to the central equatorial Pacific during the warm QD phase. Similar patterns, but with the opposite sign of salinity anomalies, appear in the cold QD phase during January 2007–December 2009 with negative sea surface temperature anomalies over the central equatorial Pacific. It is suggested that effects from zonal salinity advection and precipitation could contribute to the generation of the salinity variations in the western equatorial Pacific for QD phases during the 2000s. On the other hand, the contribution of meridional salinity advection is much less than that of zonal salinity advection. In addition, El Niño Modoki and La Niña events could affect salinity changes for warm and cold QD phases via interannual-scale zonal salinity advection variations in the western equatorial Pacific during the 2000s.

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 An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1519 ◽  
Author(s):  
Dengyun Wu ◽  
Jianwen Wang ◽  
Hong Wang ◽  
Hongxing Liu ◽  
Lin Lai ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Ratio ◽  
Characteristic Frequency ◽  
Automatic Monitoring ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method

Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.

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 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.

Study on climate change impacts of sea level rise on the Saemangeum Sea Dike using the EEMD method

2021 ◽  
Author(s):  
Baeg Lee ◽  
Hyun-Han Kwon
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Linear Regression Analysis ◽  
Climate Change Impacts ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Eemd Method ◽  
The Impact ◽  
Sea Dike

<p>The Saemangeum Sea Dike, completed in 2011 in South Korea, is the longest sea dike in the world. One of the most pressing issues of today, climate change leads to sea level rise that could increase the risk of sea dike overflow. However, what impact climate change will have on the Saemangeum Sea Dike has not been verified yet. Here, we estimate the impacts of sea level rise on the Saemangeum Sea Dike, using the Linear Regression Analysis method and the Ensemble Empirical Mode Decomposition (EEMD) method. The sea level data of the Saemangeum area of the last 30 years was analysed and the results showed that the maximum rate of sea level rise of the Saemangeum Sea Dike is at 2.51 mm/year when the EEMD method was applied. It means that the impact of sea level rise on the Saemangeum sea dike is reasonably low because the sea level rise will exceed the freeboard after 366 years, so there is currently no clear sign that it poses a problem.</p>

Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

2012 ◽  
Vol 518-523 ◽  
pp. 3887-3890 ◽  
Author(s):  
Wei Chen ◽  
Shang Xu Wang ◽  
Xiao Yu Chuai ◽  
Zhen Zhang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Random Noise ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

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