Empirical Mode Decomposition based Detrended Fluctuation Analysis of Uterine Electromyography Signals in Term Conditions

2020 ◽  
Vol 9 (2) ◽  
pp. 127-134
Author(s):  
P. Vardhini ◽  
N. Punitha ◽  
S. Ramakrishnan
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Mode Decomposition ◽  
Detrended Fluctuation ◽  
Uterine Electromyography ◽  
Electromyography Signals

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

scholarly journals Removal of Dust Microelectric Signal Based on Empirical Mode Decomposition and Multifractal Detrended Fluctuation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Jiming Li ◽  
Yongji Sun ◽  
Xuezhen Cheng
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Time Lapse ◽  
Noise Removal ◽  
Noise Signal ◽  
Fluctuation Analysis ◽  
Measured Signal ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Detrended Fluctuation

Microcharge induction has recently been applied as a dust detection method. However, in complex environments, the detection device can be seriously polluted by noise. To improve the quality of the measured signal, the characteristics of both the signal and the noise should be analyzed so as to determine an effective noise removal method. Traditional removal methods mostly deal with specific noise signals, and it is difficult to consider the correlation of measured signals between adjacent time periods. To overcome this shortcoming, we describe a method in which wavelet decomposition is applied to the measured signal to obtain sub-band components in different frequency ranges. A time-lapse Pearson method is then used to analyze the correlation of the sub-band components and the noise signal. This allows the sub-band component of the measurement signal that has the strongest correlation with the noise to be determined. Based on multifractal detrended fluctuation analysis combined with empirical mode decomposition, the similarity between the signal sub-band components and the noise sub-band components is analyzed and three indices are employed to determine the multifractal characteristics of the sub-band components. The consistency between noise components and signal components is obtained and the main signal components are verified. Finally, the sub-band components are used to reconstruct the signal, giving the noise-free measured (microcharge induction) signal. The filtered signal presents smoother, multifractal features.

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