Improving empirical mode decomposition for vibration signal analysis

2016 ◽  
Vol 231 (12) ◽  
pp. 2223-2234 ◽  
Author(s):  
Mousa Rezaee ◽  
Amin Taraghi Osguei
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Local Mean ◽  
The Common ◽  
Vibration Signal Analysis ◽  
Overshoot And Undershoot ◽  
Better Than

In this paper, the empirical mode decomposition as a signal processing method has been studied to overcome one of its shortcomings. In the previous studies, some improvements have been made on the empirical mode decomposition and it has been applied for condition monitoring of mechanical systems. These improvements include elimination of mode mixing and restraining of end effect in empirical mode decomposition method. In this research, to increase the accuracy of empirical mode decomposition, a new local mean has been proposed in the sifting process. Through the proposed local mean, the overshoot and undershoot problems in defining the local mean of common algorithm are alleviated. Meanwhile, it is capable to separate the components with close frequencies. Through the analysis of simulated signals via the new algorithm, it is shown that the accuracy is improved. Finally, empirical mode decomposition-based fault diagnosis approach has been applied to a vibration signal obtained from a faulty gearbox. The results show that the proposed method can resolve the effects of damage in vibration signals better than the common empirical mode decomposition method and helps for the isolation and localization of the fault.

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 An Improved Empirical Mode Decomposition Method for Vibration Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaohan Liu ◽  
Guangfeng Shi ◽  
Weina Liu
Keyword(s):  
Signal Processing ◽  
Power Spectrum ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Blasting Vibration ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

Feature Extraction of EEG Signals Based on Local Mean Decomposition and Fuzzy Entropy

2020 ◽  
Vol 34 (12) ◽  
pp. 2058017
Author(s):  
Yanping Li ◽  
Qi Wang ◽  
Tao Wang ◽  
Jian Pei ◽  
Shuo Zhang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Extraction Method ◽  
Decomposition Method ◽  
Fuzzy Entropy ◽  
Eeg Signals ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Local Mean

An improved feature extraction method is proposed aiming at the recognition of motor imagined electroencephalogram (EEG) signals. Using local mean decomposition, the algorithm decomposes the original signal into a series of product function (PF) components, and meaningless PF components are removed from EEG signals in the range of mu rhythm and beta rhythm. According to the principle of feature time selection, 4[Formula: see text]s to 6[Formula: see text]s motor imagery EEG signals are selected as classification data, and the sum of fuzzy entropies of second-and third-order PF components of [Formula: see text], [Formula: see text] lead signals is calculated, respectively. Mean value of fuzzy entropy [Formula: see text] is used as input element to construct EEG feature vector, and support vector machine (SVM) is used to classify and predict EEG signals for recognition. The test results show that this feature extraction method has higher classification accuracy than the empirical mode decomposition method and the total empirical mode decomposition method.

Fault Diagnosis of Transmission Based on EMD and Fractal Technology

2014 ◽  
Vol 926-930 ◽  
pp. 1712-1715
Author(s):  
Zhen Shu Ma ◽  
Chao Liu ◽  
Hua Gang Sun ◽  
Zhi Chuan Liu
Keyword(s):  
Fractal Dimension ◽  
Fault Diagnosis ◽  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Great Influence ◽  
Vibration Signal ◽  
Fault Type ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

As a result of the presence of noise in the measured vibration signal has a great influence on the results of calculation of fractal dimension, Therefore the empirical mode decomposition method for noise reduction of gear vibration signal is used, calculation fractal dimension, extraction fault feature of Gear in different conditions. The measured results show that: Different fault states have different fractal dimension, we can judge the fault type of gear effectively by the fractal dimension.

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