scholarly journals A Novel Denoising Algorithm of Electromagnetic Ultrasonic Detection Signal Based on Improved EEMD Method

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Wenkang Gong ◽  
Qi Liu ◽  
Wenhao Du ◽  
Weichen Xu ◽  
Gang Wang
Keyword(s):  
White Noise ◽  
Permutation Entropy ◽  
Denoising Method ◽  
Noise Component ◽  
Ultrasonic Signals ◽  
Crack Location ◽  
Residual Difference ◽  
Eemd Method ◽  
Wavelet Transform Modulus Maximum ◽  
Improved Eemd

In this paper, we propose a new denoising algorithm for electromagnetic ultrasonic signals based on the improved EEMD method, which can adaptively adjust for added noise and average times in different noisy environments, so that the effect of the residual difference of white noise on the results can be eliminated as far as possible. First, the way to add white noise in the EEMD method is processed, and then the permutation entropy algorithm is used to identify the nature of the components obtained during the decomposition. Then the wavelet transform modulus maximum denoising method is used to deal with the IMF components of the high-frequency part obtained before. Finally, the processed IMF results and residual difference are summed up. The results show that after processing, the noise component in the signal is less and the original information is more reserved, which prevents the signal distortion to a great extent and provides more effective data for subsequent processing. In the experiment, the crack defect data collected by the electromagnetic ultrasonic experiment system were processed by the improved EEMD method. Compared with the traditional EEMD method, it can retain the information of crack location more accurately, which proves the effectiveness of the proposed method.

Improved EEMD Applied to Rotating Machinery Fault Diagnosis

2011 ◽  
Vol 128-129 ◽  
pp. 154-159 ◽  
Author(s):  
Lue Chen ◽  
Ge Shi Tang ◽  
Yan Yang Zi ◽  
Fei Fan
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Precise Diagnosis ◽  
Empirical Mode Decomposition Method ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

Ensemble Empirical Mode Decomposition (EEMD) is a new noise-assisted data analysis (NADA) method. The effect of EEMD depends on two key parameters which are the amplitude of white noise and the ensemble times. However, the shortcoming of EEMD is that it lacks adaptability and reliability because these two key important parameters are obtained by experience and human intervention. An Improved Ensemble Empirical Mode Decomposition method is proposed in this paper, by adding white noise and ascertaining ensemble number adaptively. The criterion of adding white noise in Improved EEMD is established, by which a composite simulation signal could be adaptively and accurately decomposed into IMFs without mode mixing. The proposed method is applied to a gear fault detection of hot strip finishing mills. The result shows that Improved EEMD method successfully extracts the gear fault feature with high precise diagnosis results.

ROTATING MACHINERY FAULT DETECTION BASED ON IMPROVED ENSEMBLE EMPIRICAL MODE DECOMPOSITION

2014 ◽  
Vol 06 (02n03) ◽  
pp. 1450006 ◽  
Author(s):  
LUE CHEN ◽  
YAN-YANG ZI ◽  
ZHENG-JIA HE ◽  
YA-GUO LEI ◽  
GE-SHI TANG
Keyword(s):  
Fault Detection ◽  
White Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Mode Decomposition ◽  
Hot Strip ◽  
Gear Fault ◽  
Precise Diagnosis ◽  
Eemd Method ◽  
Improved Eemd

An improved EEMD approach is introduced in this paper based on automatically obtaining the adding white noise amplitude and the ensemble number according to different analyzing signal characteristics. The adding white noise affects decomposition effect is researched in detail, a criterion of adding white noise in EEMD is established, and the improved EEMD algorithm is described. Simulated signals demonstrate the effectiveness of the improved EEMD in diagnosing the faults of rotating machinery. The improved EEMD is successfully applied to an early rub-impact fault detection of machine unit for catalytic cracking of heavy oil, with the fault reason being analyzed in detail. A gear fault detection of hot strip finishing mills is also analyzed utilizing the improved EEMD method. The results show that the improved EEMD can obtain more precise diagnosis results than the original EMD and FFT spectrum.

scholarly journals Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Zhijian Wang ◽  
Junyuan Wang ◽  
Wenan Cai ◽  
Jie Zhou ◽  
Wenhua Du ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
White Noise ◽  
Decomposition Method ◽  
Low Noise ◽  
Permutation Entropy ◽  
Noise Component ◽  
Local Mean Decomposition ◽  
Fault Features ◽  
Local Mean ◽  
Mode Mixing

In industrial production, it is highly essential to extract faults in gearbox accurately. Specifically, in a strong noise environment, it is difficult to extract the fault features accurately. LMD (local mean decomposition) is widely used as an adaptive decomposition method in fault diagnosis. In order to improve the mode mixing of LMD, ELMD (ensemble Local Mean Decomposition) is proposed as local mode mixing exists in noisy environment, but white noise added in ELMD cannot be completely neutralized leading to the influence of increased white noise on PF (product function) component. This further leads to the increase in reconstruction errors. Therefore, this paper proposes a composite fault diagnosis method for gearboxes based on an improved ensemble local mean decomposition. The idea is to add white noise in pairs to optimize ELMD, defined as CELMD (Complementary Ensemble Local Mean Decomposition) then remove the decomposed high noise component by PE (Permutation Entropy) while applying the SG (Savitzky-Golay) filter to smooth out the low noise in PFs. The method is applied to both simulated signal and experimental signal, which overcomes mode mixing phenomenon and reduces reconstruction error. At the same time, this method avoids the occurrence of pseudocomponents and reduces the amount of calculation. Compared with LMD, ELMD, CELMD, and CELMDAN, it shows that improved ensemble local mean decomposition method is an effective method for extracting composite fault features.

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.

Threshold Noise Reduction Research of Improved EEMD Method

2012 ◽  
Vol 226-228 ◽  
pp. 237-240 ◽  
Author(s):  
Mei Jun Zhang ◽  
Hao Chen ◽  
Chuang Wang ◽  
Qing Cao
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Time Frequency ◽  
Noise Ratio ◽  
Reduction Effect ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

Improved EEMD Method Research

2012 ◽  
Vol 479-481 ◽  
pp. 1180-1185 ◽  
Author(s):  
Mei Jun Zhang ◽  
Si Chen Han ◽  
Chuang Wang ◽  
Shu Guang Li
Keyword(s):  
Spline Interpolation ◽  
Noise Pollution ◽  
Signal Decomposition ◽  
Measured Signal ◽  
Pulse Interference ◽  
Improvement Measures ◽  
Eemd Method ◽  
Simulation Signal ◽  
Overshoot And Undershoot ◽  
Improved Eemd

In order to correct the endpoint effect and modal aliasing phenomenon in EMD and EEMD, improved EEMD is put forward in this paper. on the basis of the cause of the endpoint effect,the improvement measures are proposed. The pulse interference and noise pollution is suppressed by threshold noise reduction.The overshoot and undershoot phenomenon is controlled by improving 3-spline interpolation to fit envelope. The endpoint effects is lessened by signal SVM prolongation.Compared with EMD and EEMD, not only the IMF false component is reduced, the modal aliasing avoided, and effectively the endpoint effect restrained, the distortion problem in the signal decomposition produces corrected in the results of simulation signal and the measured signal by the improved EEMD in this paper.

scholarly journals A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 765
Author(s):  
Pengfei Wang ◽  
Yanbin Gao ◽  
Menghao Wu ◽  
Fan Zhang ◽  
Guangchun Li ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Fiber Optic ◽  
Search Algorithm ◽  
Permutation Entropy ◽  
Navigation Systems ◽  
Variational Mode Decomposition ◽  
Fiber Optic Gyroscope ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Penalty Factor

Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number K and quadratic penalty factor α , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.

Noise reduction of shot-noise-dominated hyperspectral imagery by combining PCA with existing denoising methods

2021 ◽  
pp. 2150028
Author(s):  
Guang Yi Chen ◽  
Adam Krzyzak
Keyword(s):  
White Noise ◽  
Shot Noise ◽  
Signal To Noise Ratio ◽  
Hyperspectral Imagery ◽  
Gaussian White Noise ◽  
Principal Component ◽  
Data Cube ◽  
Wavelet Shrinkage ◽  
Denoising Method ◽  
Imagery Data

In this paper, we revisit the effects of principal component analysis (PCA) on hyperspectral imagery denoising. Our previous work combined PCA with wavelet shrinkage and particularly good denoising results has been achieved. We debate that any denoising methods can be used to replace wavelet shrinkage in our PCA+wavelet shrinkage algorithm. The major difference between this work and our previous PCA-based denoising method is that we consider a mixture of Gaussian and shot noise in this work whereas our previous methods studied Gaussian white noise alone. In addition, we retain [Formula: see text] [Formula: see text] PCA output components in our forward PCA transform in this paper whereas we keep all PCA output components [Formula: see text] in our previous works. The [Formula: see text] above is the number of spectral bands in the original hyperspectral imagery data cube. In addition, PCA is much better than nonlinear PCA for hyperspectral imagery denoising when Gaussian white noise and shot noise are introduced as demonstrated in this paper. Extensive experiments demonstrate that the method proposed in this paper outperforms the existing methods significantly in terms of signal-to-noise ratio for two testing hyperspectral imagery data cubes.

Sign in / Sign up

Export Citation Format

Share Document