Reconstruction and information entropy analysis of frictional vibration signals in running-in progress

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Haijie Yu ◽  
Haijun Wei ◽  
Daping Zhou ◽  
Jingming Li ◽  
Hong Liu
Keyword(s):  
Information Entropy ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Reconstruction Method ◽  
Content Type ◽  
Cross Correlation Analysis ◽  
Eemd Method ◽  
Concept Of Information ◽  
Frictional Vibration

Purpose This study aims to reconstruct the frictional vibration signal from noise and characterize the running-in process by frictional vibration. Design/methodology/approach There is a strong correlation between tangential frictional vibration and normal frictional vibration. On this basis, a new frictional vibration reconstruction method combining cross-correlation analysis with ensemble empirical mode decomposition (EEMD) was proposed. Moreover, the concept of information entropy of friction vibration is introduced to characterize the running-in process. Findings Compared with the wavelet packet method, the tangential friction vibration and the normal friction vibration reconstructed by the method presented in this paper have a stronger correlation. More importantly, during the running-in process, the information entropy of friction vibration gradually decreases until the equilibrium point is reached, which is the same as the changing trend of friction coefficient, indicating that the information entropy of friction vibration can be used to characterize the running-in process. Practical implications The study reveals that the application EEMD method is an appropriate approach to reconstruct frictional vibration and the information entropy of friction vibration represents the running-in process. Based on these results, a condition monitoring system can be established to automatically evaluate the running-in state of mechanical parts. Originality/value The EEMD method was applied to reconstruct the frictional vibration. Furthermore, the information entropy of friction vibration was used to analysis the running-in process.

scholarly journals Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3125
Author(s):  
Zou ◽  
Chen ◽  
Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Field Experiments ◽  
Low Frequency ◽  
Vibration Signal ◽  
Micro Electro Mechanical System ◽  
Ensemble Empirical Mode Decomposition ◽  
Reconstruction Method ◽  
Monitoring Methods ◽  
Dynamic Displacement ◽  
Mode Decomposition

Considering the lack of precision in transforming measured micro-electro-mechanical system (MEMS) accelerometer output signals into elevation signals, this paper proposes a bridge dynamic displacement reconstruction method based on the combination of ensemble empirical mode decomposition (EEMD) and time domain integration, according to the vibration signal traits of a bridge. Through simulating bridge analog signals and verifying a vibration test bench, four bridge dynamic displacement monitoring methods were analyzed and compared. The proposed method can effectively eliminate the influence of low-frequency integral drift and high-frequency ambient noise on the integration process. Furthermore, this algorithm has better adaptability and robustness. The effectiveness of the method was verified by field experiments on highway elevated bridges.

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.

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.

Application of MSE-EEMD Method in Gear Fault Diagnosis

2014 ◽  
Vol 530-531 ◽  
pp. 261-265
Author(s):  
Min Qiang Xu ◽  
Yong Bo Li ◽  
Hai Yang Zhao ◽  
Si Yang Zhang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Engineering Application ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Multiscale Entropy ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Eemd Method

Focus on the nonlinear and non-stationary characteristics of gear box vibration signal, the method of gear fault diagnosis based on Ensemble Empirical Mode Decomposition (EEMD) and multiscale entropy (MSE) was proposed . The complicated signal can be decomposed into several stationary IMF components with reality meanings by EEMD which has the advantages of eliminating aliasing state of vibration signal, and the MSE can extract the fault feature from the signals effectively. The concepts of EEMD and MSE are introduced firstly, and then they are applied to measure the complexity of gearbox signals. Through the engineering application of the diagnosis on gear typical fault of different wearing degree demonstrated that the proposed method can extracting the fault feature of gear fault effectively and realize the gear fault diagnosis.

An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Shi-Chang Du ◽  
Tao Liu ◽  
De-Lin Huang ◽  
Gui-Long Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Decomposition ◽  
Critical Parameters ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Two Parameters ◽  
Mixing Problem ◽  
Mode Mixing

The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical parameters (i.e., the amplitude of added white noise and the number of ensemble trials) are not selected appropriately. A novel EEMD method with optimized two parameters is proposed to solve the mode mixing problem in vibration signal decomposition in this paper. In the proposed optimal EEMD, the initial values of the two critical parameters are selected based on an adaptive algorithm. Then, a multimode search algorithm is explored to optimize the critical two parameters by its good performance in global and local search. The performances of the proposed method are demonstrated by means of a simulated signal, two bearing vibration signals, and a vibration signal in a milling process. The results show that compared with the traditional EEMD method and other improved EEMD method, the proposed optimal EEMD method automatically obtains the appropriate parameters of EEMD and achieves higher decomposition accuracy and faster computational efficiency.

The Research to the De-Noising Method for the Vibration Signals Based on EEMD

2014 ◽  
Vol 687-691 ◽  
pp. 3806-3808
Author(s):  
Wei Chang Xu ◽  
Tao Tang ◽  
Ji Fang Liu ◽  
Wei Huang
Keyword(s):  
Random Noise ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Practical Engineering ◽  
The Mean ◽  
Eemd Method ◽  
Harmonic Components ◽  
White Noises

Dynamical properties of mechanical systems can be obtained with the vibration signals from the systems. However, for the influence of noises, it is difficult to accurately acquire the features. Therefore, de-noising operation is significant for vibration signal in the practical engineering. In order to resolve this problem, the Ensemble Empirical Mode Decomposition (EEMD) method is introduced to try to eliminate noises from the analyzed signal. At first, the theory of the method is illustrated, which included adding white noises, EMD for the signal and calculating the mean of the intrinsic mode function. On this base, the signal which contains several harmonic components with white noise is processed by EEMD. As the result shown, the random noise can be effectively removed; moreover, the harmonic components can be accurately separated. And these improve that the EEMD is an effective method for the de-noising.

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 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 Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

Fuzzy Matter-Element Evaluation Method for Analysis of Debris Flow Risk Assessment

2012 ◽  
Vol 446-449 ◽  
pp. 3058-3061 ◽  
Author(s):  
Chun Tan ◽  
Jian Ping Chen ◽  
Yu Zhen Pan ◽  
Cen Cen Niu ◽  
Li Ming Xu
Keyword(s):  
Risk Assessment ◽  
Debris Flow ◽  
Information Entropy ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Practical Application ◽  
Element Analysis ◽  
Matter Element Analysis ◽  
Concept Of Information

Based on the principle of fuzzy matter-element analysis, the concept of information entropy is introduced to establish a fuzzy matter-element evaluation method. This method is utilized to comprehensively evaluate the degree of debris flow. The classifications of debris flow are regarded as the objects of matter-element and their indexes for evaluation as well as the corresponding fuzzy values are used to construct the composite fuzzy matter-elements. By calculating the relevancy the comprehensive evaluation of debris flow can be carried out. This model is applied to analyze the degree of debris flow in the practical application. The application shows that the model is effective and practical.

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