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

Abstract

Aiming at the situation that complementary ensemble empirical mode decomposition (CEEMD) noise suppression method may produce redundant noise and wavelet transform easily loses high-frequency detail information, considering wavelet packet transform can be used to perform better time-frequency localization analysis on signals containing a large amount of medium and high frequency information, according to the noise and useful signal components of both the characteristic of self-correlation function is different, the CEEMD and wavelet packet threshold jointed method is proposed. The method uses the energy concentration ratio to find noise and useful signal component demarcation point to denoise the microseismic signals. Firstly, we utilize adaptively decompose the signal from high frequency to low frequency by the CEEMD; Secondly, using the self-correlation method to select the intrinsic mode function (IMF) that needs noise suppression, the wavelet suppression method is used to suppress the noise of several high-frequency components whose self-correlation coefficient is below the critical value K; Finally, the IMF component after the wavelet packet threshold noise suppression is reconstructed with the noisefree IMF component. In order to verify the effectiveness of the proposed method on the noise suppression of microseismic signal, we added a Gaussian white noise to the Ricker wavelet signal similar to the microseismic signal. The experimental results show that the signal-to-noise ratio (SNR) of the signal is raised more than 10dB. The energy percentage is higher than 92%. In practical engineering, our proposal achieves an effective noise suppression effect on the microseismic signal.INDEX TERMS Complementary ensemble empirical mode decomposition, wavelet packet threshold, selfcorrelation, noise suppression.

Multistage Centrifugal Pump Fault Diagnosis by Selecting Fault Characteristic Modes of Vibration and Using Pearson Linear Discriminant Analysis

“…Eq. 15shows the out-of-plane vibration of mechanical seal, where h and t are the cross-sectional height and thickness of the ring, d is the diameter, c is the torsion constant, v is Poisson's ratio, and n represents the mode of vibration in (15) and (16). It is worth noting that the fundamental and in-plane vibrations occur at high frequencies.…”

“…However, these kinds of techniques need a baseline or reference signals to ensure the appropriate noise reduction in the unimpaired signals. When applied to vibration signals, empirical mode decomposition has a persuasive noise reduction capability; however, its limited mathematical background, the presence of mode mixing, and extreme interpolation make this technique less attractive for solving the problems described above [16], [17]. Specifically, for CPs that consist of different mechanical components, it is hard to focus on one optimal narrow band for the FCF.…”

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

“…x t x t (11) where x(t) is a pure signal, x(t)new is a denoised signal, and N is the number of sampling points. If the signal has a high SNR after denoising, the denoising effect is better.…”

“…At present, there are many methods for denoising MS signals widely used. The characteristics and application scope of the widely used MS signals can be roughly divided into three categories: estimation filtering method [5,6], wavelet threshold filtering method [7,8], and adaptive time-frequency analysis method [9][10][11]. These methods have certain limitations in practical engineering applications.…”

Pressure Vessel Diagnosis by Eliminating Undesired Signal Sources and Incorporating GA-Based Fault Feature Evaluation

“…Empirical mode decomposition (EMD), a cutting-edge method with compelling noise-reducing capabilities, is another potential option, especially for vibration signal noise. Yet, some problems such as mode mixing and deficient enveloping still puzzle noise-filter designers [14][15][16]. In other words, prevailing de-noising algorithms are ineffective when attempting to eliminate interfering sources from nonstationary signals.…”