A New Denoising Method for UHF PD Signals Using Adaptive VMD and SSA-Based Shrinkage Method

Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies.

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Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding

Applied Sciences ◽

10.3390/app9010180 ◽

2019 ◽

Vol 9 (1) ◽

pp. 180 ◽

Cited By ~ 4

Author(s):

Weifang Zhang ◽

Meng Zhang ◽

Yan Zhao ◽

Bo Jin ◽

Wei Dai

Keyword(s):

Discrete Wavelet ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Mode Decomposition ◽

Noisy Signals ◽

Band Limited ◽

Adaptive Signal Decomposition ◽

Adaptive Signal ◽

The Impact

Damage detection using an FBG sensor is a critical process for an assessment of any inspection technology classified as structural health monitoring (SHM). FBG signals containing noise in experiments are developed to detect flaws. In this paper, we propose a novel signal denoising method that combines variational mode decomposition (VMD) and changed thresholding wavelets to denoise experimental and mixed signals. VMD is a recently introduced adaptive signal decomposition algorithm. Compared with traditional empirical mode decomposition (EMD), and it is well founded theoretically and more robust to noise samples. First, input signals were broken down into a given number of K band-limited intrinsic mode functions (BLIMFs) by VMD. For the purpose of avoiding the impact of overbinning or underbinning on VMD denoising, the mixed signals, which were obtained by adding different signal/noise ratio (SNR) noises to the experimental signals, were designed to select the best decomposition number K and data-fidelity constraint parameter α. After that, the realistic experimental signals were processed using four denoising algorithms to evaluate denoising performance. The results show that, upon adding additional noisy signals and realistic signals, the proposed algorithm delivers excellent performance over the EMD-based denoising method and discrete wavelet transform filtering.

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

Applied Sciences ◽

10.3390/app10113790 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3790 ◽

Cited By ~ 1

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.

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Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition

Entropy ◽

10.3390/e23121567 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1567

Author(s):

Ragavesh Dhandapani ◽

Imene Mitiche ◽

Scott McMeekin ◽

Venkateswara Sarma Mallela ◽

Gordon Morison

Keyword(s):

Performance Metrics ◽

Regularization Parameter ◽

Signal To Noise Ratio ◽

Partial Discharge ◽

Sensor Data ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Mode Decomposition ◽

Band Limited

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.

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A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1177/0954408915595763 ◽

2016 ◽

Vol 231 (2) ◽

pp. 317-328 ◽

Cited By ~ 3

Author(s):

Xueli An ◽

Junjie Yang

Keyword(s):

Empirical Mode Decomposition ◽

Real World ◽

Approximate Entropy ◽

Vibration Signal ◽

Signal Denoising ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Multivariate Empirical Mode Decomposition ◽

Mode Decomposition ◽

Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

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Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means

E3S Web of Conferences ◽

10.1051/e3sconf/202020603019 ◽

2020 ◽

Vol 206 ◽

pp. 03019

Author(s):

Kun Zhao ◽

Jisheng Ding ◽

YanFei Sun ◽

ZhiYuan Hu

Keyword(s):

Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Side Scan Sonar ◽

Sonar Image ◽

Local Means ◽

Residual Component ◽

Mode Decomposition ◽

Non Local ◽

Bidimensional Empirical Mode Decomposition

In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.

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Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.4302 ◽

2021 ◽

Vol 11 (5) ◽

pp. 7536-7541

Author(s):

W. Mohguen ◽

S. Bouguezel

Keyword(s):

Empirical Mode Decomposition ◽

Additive White Gaussian Noise ◽

Ensemble Empirical Mode Decomposition ◽

Ecg Signal ◽

Mean Square ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Mode Decomposition ◽

Mean Square Difference ◽

Thresholding Function

In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.

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Well-log decomposition using variational mode decomposition in assisting the sequence stratigraphy analysis of a conglomerate reservoir

Geophysics ◽

10.1190/geo2017-0817.1 ◽

2018 ◽

Vol 83 (4) ◽

pp. B221-B228 ◽

Cited By ~ 9

Author(s):

Zhaohui Xu ◽

Bo Zhang ◽

Fangyu Li ◽

Gang Cao ◽

Yuming Liu

Keyword(s):

Sequence Stratigraphy ◽

Oil Field ◽

Well Logs ◽

Bohai Bay ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Sequence Identification ◽

Band Limited ◽

System Tracts

Sequence stratigraphy analysis is one of the most important tasks in evaluating and characterizing the reservoir system within a basin. However, it is very hard to identify the system tracts and lithofacies using well logs for the conglomerate reservoirs because of the strong lithology heterogeneity. Based on the fact that the system tracts and lithofacies usually illustrate cycle features within the basin, we decompose the well logs into different intrinsic modes to characterize the sequence units and lithofacies at different scale. First, we analyze the log response to lithologies to determine the well logs used for sequence analysis. Then, we use variational mode decomposition to decompose the selected well logs into an ensemble of different band-limited intrinsic mode functions, each with its center wavenumber. Finally, we interpret the sequence stratigraphy and lithofacies using corresponding decomposed modes. We validate the effectiveness of our method in the lithofacies and sequence identification for a conglomerate reservoir in the Shengli oil field, Bohai Bay Basin, east China. The decomposed intrinsic modes with a larger center wavenumber perfectly characterize the sequence units at a larger scale, whereas the decomposed intrinsic modes with a smaller center wavenumber reveal the lithofacies changes at a smaller scale. The application illustrates that it is much more convenient and easier for sequence stratigraphy analysis to integrate the original and decomposed logs.

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Denoising of ECG Signal Based on Empirical Mode Decomposition and Adaptive Noise Cancellation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.40-41.140 ◽

2010 ◽

Vol 40-41 ◽

pp. 140-145

Author(s):

Ren Di Yang ◽

Yan Li Zhang

Keyword(s):

Empirical Mode Decomposition ◽

Noise Cancellation ◽

High Signal ◽

Ecg Signal ◽

Adaptive Noise Cancellation ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Mode Decomposition ◽

Reconstructed Signal ◽

Adaptive Noise

To remove the noises in ECG and to overcome the disadvantage of the denoising method only based on empirical mode decomposition (EMD), a combination of EMD and adaptive noise cancellation is introduced in this paper. The noisy ECG signals are firstly decomposed into intrinsic mode functions (IMFs) by EMD. Then the IMFs corresponding to noises are used to reconstruct signal. The reconstructed signal as the reference input of adaptive noise cancellation and the noisy ECG as the basic input, the de-noised ECG signal is obtained after adaptive filtering. The de-noised ECG has high signal-to-noise ratio, preferable correlation coefficient and lower mean square error. Through analyzing these performance parameters and testing the denoising method using MIT-BIH Database, the conclusion can be drawn that the combination of EMD and adaptive noise cancellation has considered the frequency distribution of ECG and noises, eliminate the noises effectively and need not to select a proper threshold.

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A Novel Empirical Mode Decomposition Denoising Scheme

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.143-144.527 ◽

2010 ◽

Vol 143-144 ◽

pp. 527-532

Author(s):

Wei Du ◽

Quan Liu

Keyword(s):

Empirical Mode Decomposition ◽

Basic Principle ◽

Decomposition Process ◽

Signal Denoising ◽

Intrinsic Mode Functions ◽

Noise Levels ◽

Denoising Method ◽

Mode Decomposition ◽

Adaptive Decomposition

This paper presents a novel and fast scheme for signal denoising by using Empirical mode decomposition (EMD). The EMD involves the adaptive decomposition of signal into a series of oscillating components, Intrinsic mode functions(IMFs), by means of a decomposition process called sifting algorithm. The basic principle of the method is to reconstruct the signal with IMFs previously selected and thresholded. The denoising method is applied to four simulated signals with different noise levels and the results compared to Wavelets, EMD-Hard and EMD-Soft methods.

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MEMS Gyroscope Bias Drift Self-Calibration Based on Noise-Suppressed Mode Reversal

Micromachines ◽

10.3390/mi10120823 ◽

2019 ◽

Vol 10 (12) ◽

pp. 823 ◽

Cited By ~ 1

Author(s):

Haoyu Gu ◽

Baolin Zhao ◽

Hao Zhou ◽

Xianxue Liu ◽

Wei Su

Keyword(s):

Empirical Mode Decomposition ◽

Calibration Method ◽

Ensemble Empirical Mode Decomposition ◽

Bias Error ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Bias Drift ◽

Self Calibration ◽

Mode Decomposition ◽

Mems Gyroscopes

This paper presents a bias drift self-calibration method for micro-electromechanical systems (MEMS) gyroscopes based on noise-suppressed mode reversal without the modeling of bias drift signal. At first, the bias drift cancellation is accomplished by periodic switching between operation mode of two collinear gyroscopes and subtracting the bias error which is estimated by the rate outputs from a consecutive period interval; then a novel filtering algorithm based on improved complete ensemble empirical mode decomposition (improved complete ensemble empirical mode decomposition with adaptive noise—CEEMDAN) is applied to eliminate the noise in the calibrated signal. A set of intrinsic mode functions (IMFs) is obtained by the decomposition of the calibrated signal using improved CEEMDAN method, and the threshold denoising method is utilized; finally, the de-noised IMFs are reconstructed into the desired signal. To verify the proposed method, the hardware circuit with an embedded field-programmable gate array (FPGA) was implemented and applied in bias drift calibration for the two MEMS gyroscopes manufactured in our laboratory. The experimental results indicate that the proposed method is feasible, and it achieved a better performance than the typical mode reversal. The bias instability of the two gyroscopes decreased from 0.0066 ° / s and 0.0055 ° / s to 0.0011 ° / s ; and, benefiting from the threshold denoising based on improved CEEMDAN, the angle random walks decreased from 1.18 × 10 − 4 ° / s 1 / 2 and 2.04 × 10 − 4 ° / s 1 / 2 to 2.19 × 10 − 5 ° / s 1 / 2 , respectively.

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