Fault diagnosis of gearbox using empirical mode decomposition and multi-fractal detrended cross-correlation analysis

2016 ◽  
Vol 385 ◽  
pp. 350-371 ◽  
Author(s):  
Hongmei Liu ◽  
Jichang Zhang ◽  
Yujie Cheng ◽  
Chen Lu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Cross Correlation ◽  
Mode Decomposition ◽  
Cross Correlation Analysis

Multiscale Detrended Cross-Correlation Analysis of Traffic Time Series Based on Empirical Mode Decomposition

2015 ◽  
Vol 14 (03) ◽  
pp. 1550023 ◽  
Author(s):  
Yi Yin ◽  
Pengjian Shang
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Long Range ◽  
Cross Correlation ◽  
Traffic Signals ◽  
Correlation Structure ◽  
Noise Effects ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Cross Correlations

In this paper, we propose multiscale detrended cross-correlation analysis (MSDCCA) to detect the long-range power-law cross-correlation of considered signals in the presence of nonstationarity. For improving the performance and getting better robustness, we further introduce the empirical mode decomposition (EMD) to eliminate the noise effects and propose MSDCCA method combined with EMD, which is called MS-EDXA method, then systematically investigate the multiscale cross-correlation structure of the real traffic signals. We apply the MSDCCA and MS-EDXA methods to study the cross-correlations in three situations: velocity and volume on one lane, velocities on the present and the next moment and velocities on the adjacent lanes, and further compare their spectrums respectively. When the difference between the spectrums of MSDCCA and MS-EDXA becomes unobvious, there is a crossover which denotes the turning point of difference. The crossover results from the competition between the noise effects in the original signals and the intrinsic fluctuation of traffic signals and divides the plot of spectrums into two regions. In all the three case, MS-EDXA method makes the average of local scaling exponents increased and the standard deviation decreased and provides a relative stable persistent scaling cross-correlated behavior which gets the analysis more precise and more robust and improves the performance after noises being removed. Applying MS-EDXA method avoids the inaccurate characteristics of multiscale cross-correlation structure at the short scale including the spectrum minimum, the range for the spectrum fluctuation and general trend, which are caused by the noise in the original signals. We get the conclusions that the traffic velocity and volume are long-range cross-correlated, which is accordant to their actual evolution, while velocities on the present and the next moment and velocities on adjacent lanes reflect the strong cross-correlations both in temporal and spatial dimensions. We also reveal the similarity and uniqueness in the cross-correlation situations between velocities. Besides, signals on one lane show stronger long-range cross-correlation than that on adjacent lanes. Thus, the multiscale cross-correlation structure acquired by MS-EDXA is more close to the intrinsic mechanism of traffic system and reflects more accurate and more abundant traffic information.

MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS OF HIGH-FREQUENCY STOCK SERIES BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Fractals ◽  
2020 ◽  
Vol 28 (02) ◽  
pp. 2050035 ◽  
Author(s):  
DANLEI GU ◽  
JINGJING HUANG
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Cross Correlation ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Series ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Eemd Method

We used the multifractal detrended cross-correlation analysis (MFDCCA) method based on ensemble empirical mode decomposition (EEMD) to study the 5-min high-frequency data of two Chinese stocks and two US stocks. Using EEMD method to decompose the original high-frequency stock data can effectively reduce the interference of noise on the series, which helps to reveal the internal characteristics of the stock system and extract more accurate and rich information. We first conducted a cross-correlation test and cross-correlation coefficient analysis on the reconstructed stock data of two groups, and found that there is a cross-correlations between them. Then we used the EEMD-based MFDCCA method to analyze the cross-correlation between the data and found that there are significant cross-correlations between DJI and NASDAQ and between SSEC and SZSE. The cross-correlation of the two Chinese stocks is stronger than that of the two US stocks. The MFDCCA results of the comparison of the original series with the reconstructed series after decomposition by the EEMD method show that the reconstructed series can display more internal details of the multifractal cross-correlation metrics compared with the original series.

scholarly journals EMD Method for Minimizing the Effect of Seasonal Trends in Detrended Cross-Correlation Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
You Gao ◽  
Nianpeng Wang
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Intrinsic Mode Functions ◽  
Nonstationary Signals ◽  
Seasonal Trends ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Correlated Signals ◽  
Emd Method

Detrended cross-correlation analysis (DCCA) is a scaling method commonly used to estimate long-range power-law cross-correlation in nonstationary signals. Recent studies have reported signals superimposed with trends, which often lead to the complexity of the signals and the susceptibility of DCCA. This paper artificially generates long-range cross-correlated signals and systematically investigates the effect of seasonal trends. Specifically, for the crossovers raised by trends, we propose a smoothing algorithm based on empirical mode decomposition (EMD) method which decomposes underlying signals into several intrinsic mode functions (IMFs) and a residual trend. After the removal of slowly oscillating components and residual term, seasonal trends are eliminated.

Application of Cross-correlation Analysis Method for Measurement of the Fluid Flow Rate Based on X-ray Radiation

2019 ◽  
Vol 11 (1) ◽  
pp. 01025-1-01025-5 ◽  
Author(s):  
N. A. Borodulya ◽  
◽  
R. O. Rezaev ◽  
S. G. Chistyakov ◽  
E. I. Smirnova ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Correlation Analysis ◽  
Flow Rate ◽  
Cross Correlation ◽  
Fluid Flow Rate ◽  
Analysis Method ◽  
X Ray ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method
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