scholarly journals Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 176504-176513 ◽  
Author(s):  
Ling-Qun Zuo ◽  
Hong-Mei Sun ◽  
Qi-Chao Mao ◽  
Xiao-Ying Liu ◽  
Rui-Sheng Jia
Keyword(s):  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Wavelet Packet ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Suppression Method ◽  
Microseismic Signal

Research on Fault Diagnosis for Gear Based on Ensemble Empirical Mode Decomposition and Slice Bi-Spectrum

2013 ◽  
Vol 718-720 ◽  
pp. 934-939
Author(s):  
Gui Ji Tang ◽  
Xiao Long Wang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Ensemble Empirical Mode Decomposition ◽  
Quadratic Phase ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Signal ◽  
Harmonic Components ◽  
Simulation Signal

A new method on fault diagnosis for gear based on ensemble empirical mode decomposition and slice bi-spectrum is proposed. Firstly, fault signal was decomposed into a series of intrinsic mode function components of different frequency bands by EEMD, and then calculated the envelope signal of IMF component by Hilbert demodulation method. Finally, analyzed the envelope signal by slice bi-spectrum and extracted the fault characteristic frequency. The anti-alias decomposition capacity of EEMD and capabilities of noise suppression and non-quadratic phase coupling harmonic components elimination of slice bi-spectrum were verified by analyzing the simulation signal. The analysis results of gear pitting failure signal and gear wear fault signal showed that this method could judge gear fault type accurately and has a certainly degree reliability.

scholarly journals Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion

2020 ◽  
Vol 10 (11) ◽  
pp. 3790 ◽  
Author(s):  
Jinyong Zhang ◽  
Linlu Dong ◽  
Nuwen Xu
Keyword(s):  
Empirical Mode Decomposition ◽  
Akaike Information Criterion ◽  
Noise Suppression ◽  
Information Criterion ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition

Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (SNRs) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the SNRs of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics.

scholarly journals The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Liu ◽  
Peng Zheng ◽  
Qilin Dai ◽  
Zhongli Zhou
Keyword(s):  
Spectral Analysis ◽  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Mode Splitting ◽  
Mode Decomposition ◽  
Microseismic Signal ◽  
Mode Mixing

The problems of mode mixing, mode splitting, and pseudocomponents caused by intermittence or white noise signals during empirical mode decomposition (EMD) are difficult to resolve. The partly ensemble EMD (PEEMD) method is introduced first. The PEEMD method can eliminate mode mixing via the permutation entropy (PE) of the intrinsic mode functions (IMFs). Then, bilateral permutation entropy (BPE) of the IMFs is proposed as a means to detect and eliminate mode splitting by means of the reconstructed signals in the PEEMD. Moreover, known ingredient component signals are comparatively designed to verify that the PEEMD method can effectively detect and progressively address the problem of mode splitting to some degree and generate IMFs with better performance. The microseismic signal is applied to prove, by means of spectral analysis, that this method is effective.

scholarly journals A Nonparametric Method for Automatic Denoising of Microseismic Data

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Pingan Peng ◽  
Liguan Wang
Keyword(s):  
Power Spectrum ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
P Wave ◽  
Noise Power ◽  
Microseismic Data ◽  
Mode Decomposition ◽  
Synthetic Datasets ◽  
Microseismic Signal

Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data. The method consists of three major steps: (1) applying a two-step AIC algorithm to pick P-wave arrival; (2) subtracting the noise power spectrum from the signal power spectrum; (3) recovering the microseismic signal by inverse Fourier transform. The proposed method is tested on synthetic datasets with different signal types and SNRs, as well as field datasets. The results of the proposed method are compared against ensemble empirical mode decomposition (EEMD) and wavelet denoising methods, which shows the effectiveness of the method for denoising and improving the SNR of microseismic data.

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