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

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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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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Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽

10.3390/s18103510 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3510 ◽

Cited By ~ 24

Author(s):

Zhijian Wang ◽

Junyuan Wang ◽

Wenhua Du

Keyword(s):

Fault Diagnosis ◽

Signal To Noise Ratio ◽

Optimal Number ◽

Original Signal ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Multiple Fault ◽

Mode Decomposition ◽

Fault Features ◽

Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

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Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

Applied Sciences ◽

10.3390/app112210943 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10943

Author(s):

Zhili Chen ◽

Peng Wang ◽

Zhixian Gui ◽

Qinghui Mao

Keyword(s):

Signal To Noise Ratio ◽

Solution Process ◽

Vertical Component ◽

Microseismic Monitoring ◽

Center Frequency ◽

Variational Mode Decomposition ◽

Denoising Method ◽

Microseismic Data ◽

Mode Decomposition ◽

Processing Step

Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.

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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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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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Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211063688 ◽

2021 ◽

pp. 014233122110636

Author(s):

Dongmei Wang ◽

Lijuan Zhu ◽

Jikang Yue ◽

Jingyi Lu ◽

Gongfa Li

Keyword(s):

Search Algorithm ◽

Signal To Noise Ratio ◽

Decomposition Algorithm ◽

Signal Denoising ◽

Two Dimensional ◽

Variational Mode Decomposition ◽

Denoising Method ◽

Mode Decomposition ◽

Penalty Factor ◽

Whale Optimization

To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

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Seismic Random Noise Attenuation Method Based on Variational Mode Decomposition and Correlation Coefficients

Electronics ◽

10.3390/electronics7110280 ◽

2018 ◽

Vol 7 (11) ◽

pp. 280 ◽

Cited By ~ 3

Author(s):

Yaping Huang ◽

Hanyong Bao ◽

Xuemei Qi

Keyword(s):

Seismic Data ◽

Signal To Noise Ratio ◽

Random Noise ◽

Correlation Coefficients ◽

Ensemble Empirical Mode Decomposition ◽

Noise Attenuation ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Random Noise Attenuation ◽

Mode Decomposition

Seismic data is easily affected by random noise during field data acquisition. Therefore, random noise attenuation plays an important role in seismic data processing and interpretation. According to decomposition characteristics of seismic signals by using variational mode decomposition (VMD) and the constraint conditions of correlation coefficients, this paper puts forward a method for random noise attenuation in seismic data, which is called variational mode decomposition correlation coefficients VMDC. Firstly, the original signals were decomposed into intrinsic mode functions (IMFs) with different characteristics by VMD. Then, the correlation coefficients between each IMF and the original signal were calculated. Next, based on the differences among correlation coefficients of effective signals and random noise as well as the original signals, the corresponding treatment was carried out, and the effective signals were reconstructed. Finally, the random noise attenuation was realized. After adding random noise to simple sine signals and the synthetic seismic record, the improved complementary ensemble empirical mode decomposition (ICEEMD) and VMDC were used for testing. The testing results indicate that the proposed VMDC has better random noise attenuation effects. It was also used in real-world seismic data noise attenuation. The results also show that it could effectively improve the signal-to-noise ratio (SNR) of seismic data and could provide high-quality basic data for further interpretation of seismic data.

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

Sensors ◽

10.3390/s19071594 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1594 ◽

Cited By ~ 9

Author(s):

Jun Zhang ◽

Junjia He ◽

Jiachuan Long ◽

Min Yao ◽

Wei Zhou

Keyword(s):

Noise Suppression ◽

Singular Spectrum Analysis ◽

Electrical Equipment ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Feature Preservation ◽

Mode Decomposition ◽

Insulation Defect ◽

Shrinkage Method ◽

Band Limited

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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Analysis of hydropower unit vibration signals based on variational mode decomposition

Journal of Vibration and Control ◽

10.1177/1077546315605240 ◽

2015 ◽

Vol 23 (12) ◽

pp. 1938-1953 ◽

Cited By ~ 12

Author(s):

Xueli An ◽

Luoping Pan ◽

Fei Zhang

Keyword(s):

Hydraulic Turbine ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Accurate Analysis ◽

Vibration Signals ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method ◽

The Face ◽

Band Limited ◽

Nonlinear Signal

The vibration signals of hydropower units are nonstationary when serious vortex occurs in the draft tube of the hydraulic turbine. The traditional signal analysis method based on Fourier transform is not suitable for the nonstationary signals. In the face of the nonstationarity of such signals and the limitation of the empirical mode decomposition method, a new nonstationary and nonlinear signal analyzing method based on variational mode decomposition (VMD) is introduced into hydropower unit vibration signals analysis. Firstly, VMD is used to decompose the signal into an ensemble of band-limited intrinsic mode functions components. Then, frequency spectrum analysis of these components is conducted to obtain the characteristic frequencies of the signal caused by the serious vortex of hydraulic turbine. Analysis of real test data shows that this proposed method can effectively suppress mode mixing. It can realize accurate analysis of nonstationary vibration signals. This provides a new way for analyzing hydropower unit vibration signals.

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The use of variational mode decomposition in assisting sedimentary cyclicity analysis: A case study from an Albian carbonate reservoir, Campos Basin, southeast Brazil

Geophysics ◽

10.1190/geo2019-0447.1 ◽

2020 ◽

Vol 85 (3) ◽

pp. B77-B86

Author(s):

Leandro Hartleben Melani ◽

Bruno César Zanardo Honório ◽

Ulisses Miguel da Costa Correia ◽

Alexandre Campane Vidal

Keyword(s):

Gamma Ray ◽

Carbonate Reservoir ◽

Well Log ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Short Term ◽

Time Frequency ◽

Mode Decomposition ◽

Campos Basin ◽

Band Limited

The sedimentary cyclicity analysis investigates the cyclic patterns and the different hierarchical orders of cyclicity in the stratigraphic record. The detection of cyclic depositional patterns is a key element of quantitative stratigraphy. It is often based on well-log data, which can be challenging due to the presence of superimposed cycles and nongeologic artifacts. We have developed an approach to assist the detection of sedimentary cyclicity in well-log signals based on a multiscale spectral analysis method. First, we apply variational mode decomposition to decompose the gamma-ray logs into band-limited subsignals, the intrinsic mode functions (IMFs), to investigate different orders of smoothness, signal-to-noise ratio, and the cyclicity embedded in the geologic record. Conventional time-domain analysis is carried out to understand the general trends in the IMFs, which enables us to better identify long-term cycles associated with transgressive-regressive (T-R) sequences. Then, by appropriately selecting a given IMF and extracting the instantaneous frequency (IF) and its mirrored version, we build a cyclicity log that can map expressive behavior change in the time-frequency domain. Because the IF is more sensitive to the signal variations, we could highlight the short-term cycles throughout the formation in detail. The detected short-term cycles are in agreement with the T-R sequence. We apply our method to the Albian carbonate succession of Macaé Group, Campos Basin, Brazil. We understand that our method can be a valuable tool for semiautomated detection of sedimentary cycles, assisting in the characterization of different hierarchical orders of cyclicity.

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