scholarly journals Extracting Modulated Annual Cycle in Climate and Ocean Time Series Using an Enhanced Harmonic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yibo Zhang ◽  
Haidong Pan ◽  
Shuang Li ◽  
Xianqing Lv
Keyword(s):  
Harmonic Analysis ◽  
Annual Cycle ◽  
Numerical Experiments ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Annual Cycles ◽  
Mode Decomposition ◽  
Observation Series ◽  
Climate Series ◽  
Modulated Annual Cycle

Accurate extraction of the modulated annual cycle (MAC) is important for climatic and oceanic research. A variety of methods are available to extract the annual cycle with inconsistent results. Since actual annual cycles are unknown in the observation series, the reliability and applicability of the results extracted by these methods are difficult to estimate. In this study, three widely used decomposition methods, ensemble empirical mode decomposition (EEMD), nonlinear mode decomposition (NMD), and enhanced harmonic analysis (EHA), are evaluated by idealized numerical experiments for extracting modulated annual cycles from climate series. Idealized numerical experiments are carried out and show that the recently proposed EHA had the most accuracy in extracting the MAC from the constructed data. The optimal independent point (IP) number, which makes the most accurate result for EHA, can be found in each ideal experiment. In the actual experiment, two IP selection criteria are proposed for EHA to extract MAC from observations.

Changes in the Amplitude of the Temperature Annual Cycle in China and Their Implication for Climate Change Research

2011 ◽  
Vol 24 (20) ◽  
pp. 5292-5302 ◽  
Author(s):  
Cheng Qian ◽  
Congbin Fu ◽  
Zhaohua Wu
Keyword(s):  
Climate Change ◽  
Annual Cycle ◽  
Linear Trend ◽  
Surface Air Temperature ◽  
Mean Amplitude ◽  
Warming Trend ◽  
Ensemble Empirical Mode Decomposition ◽  
Surface Solar Radiation ◽  
Climate Change Research ◽  
Annual Cycles

Abstract Climate change is not only reflected in the changes in annual means of climate variables but also in the changes in their annual cycles (seasonality), especially in the regions outside the tropics. In this study, the ensemble empirical mode decomposition (EEMD) method is applied to investigate the nonlinear trend in the amplitude of the annual cycle (which contributes 96% of the total variance) of China’s daily mean surface air temperature for the period 1961–2007. The results show that the variation and change in the amplitude are significant, with a peak-to-peak annual amplitude variation of 13% (1.8°C) of its mean amplitude and a significant linear decrease in amplitude by 4.6% (0.63°C) for this period. Also identified is a multidecadal change in amplitude from significant decreasing (−1.7% decade−1 or −0.23°C decade−1) to significant increasing (2.2% decade−1 or 0.29°C decade−1) occurring around 1993 that overlaps the systematic linear trend. This multidecadal change can be mainly attributed to the change in surface solar radiation, from dimming to brightening, rather than to a warming trend or an enhanced greenhouse effect. The study further proposes that the combined effect of the global dimming–brightening transition and a gradual increase in greenhouse warming has led to a perceived warming trend that is much larger in winter than in summer and to a perceived accelerated warming in the annual mean since the early 1990s in China. It also notes that the deseasonalization method (considering either the conventional repetitive climatological annual cycle or the time-varying annual cycle) can also affect trend estimation.

scholarly journals Mapping Relation between Contour Error Components of Crankshaft Pin Journal and Axis Position Control Error of Oscillating Grinding Machine

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6497
Author(s):  
Xiaoyan Fang ◽  
Xiaowei Sheng ◽  
Yize Sun ◽  
Yang Xu
Keyword(s):  
Position Control ◽  
High Reliability ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Contour Error ◽  
Control Error ◽  
Error Components ◽  
Mode Decomposition ◽  
Axis Position ◽  
Grinding Machine

Automatic crankshaft production lines require high reliability and accuracy stability for the oscillating grinding machine. Crankshaft contour error represent the most intuitive data in production field selective inspection. If the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine can be found, it would be great significance for the reliability maintenance of the oscillating grinding machine. Firstly, a contour error decomposition method based on ensemble empirical mode decomposition (EEMD) is proposed. Secondly, according to the contour generating principle of the pin journal by oscillating grinding, a calculation method to obtain the effect of the axis position control error of the oscillating grinder on the contour error of the pin journal is proposed. Finally, through the grinding experiments, the error data are acquired and measured to calculate and decompose the contour error by using the proposed methods for obtaining the mapping relation between the crankshaft pin journal contour error and the axis position control error. The conclusions show that the proposed calculation and decomposition methods can obtain the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine, which can be used to predict the key functional component performance of the machine tool from the oscillating grinding workpiece contour error.

The Application of Improved HHT to Harmonic Analysis and Fault Detection of Power System

2011 ◽  
Vol 354-355 ◽  
pp. 1406-1411
Author(s):  
Wen Hua Han ◽  
Hai Xia Ren ◽  
Xu Chen ◽  
Xiao Juan Tao
Keyword(s):  
Fault Detection ◽  
Power System ◽  
Harmonic Analysis ◽  
Harmonic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Domain Analysis ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

Hilbert-Huang transform (HHT) is a new time-frequency-domain analysis method, which is suitable for non-stationary and nonlinear signals. In this paper, endpoint continuation and ensemble empirical mode decomposition (EEMD) decomposition method are introduced to improve the HHT, which solve the endpoint winger and modal aliasing problem. The improved HHT (IHHT) is used for analyzing the harmonic signal and detecting the fault signal of power system. Simulation results show that IHHT is feasible and effective for harmonic analysis and fault detection.

FUNDAMENTAL MODES OF ATMOSPHERIC CFC-11 FROM EMPIRICAL MODE DECOMPOSITION

2012 ◽  
Vol 04 (04) ◽  
pp. 1250024 ◽  
Author(s):  
KOSEKI J. KOBAYASHI-KIRSCHVINK ◽  
KING-FAI LI ◽  
RUN-LIE SHIA ◽  
YUK L. YUNG
Keyword(s):  
Empirical Mode Decomposition ◽  
Annual Cycle ◽  
Fourier Transforms ◽  
Ensemble Empirical Mode Decomposition ◽  
Polar Regions ◽  
Initial Growth ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Legislative Control ◽  
Eemd Method

Following an initial growth, the concentrations of chlorofluorocarbon-11 (CFC-11) in the atmosphere started to decline in the 1990's due to world-wide legislative control on emissions. The amplitude of the annual cycle of CFC-11 was much larger in the earlier period compared with that in the later period. We apply here the Ensemble Empirical Mode Decomposition (EEMD) analysis to the CFC-11 data obtained by the U.S. National Oceanic and Atmospheric Administration. The sum of the second and third intrinsic mode functions (IMFs) represents the annual cycle, which shows that the annual cycle of CFC-11 has varied by a factor of 2–3 from the mid-1970's to the present over polar regions. The results provide an illustration of the power of the EEMD method in extracting a variable annual cycle from data dominated by increasing and decreasing trends. Finally, we compare the annual cycle obtained by the EEMD analysis to that obtained using conventional methods such as Fourier transforms and running averages.

scholarly journals Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 783 ◽  
Author(s):  
Martin Valtierra-Rodriguez ◽  
Juan Amezquita-Sanchez ◽  
Arturo Garcia-Perez ◽  
David Camarena-Martinez
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Induction Motors ◽  
A Priori ◽  
Digital Signal ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Mode Decomposition

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.

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.

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