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Research on Modulation Signal Denoising Method Based on Improved variational mode decomposition

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Journal of Electronic Research and Application 2024年第1期8卷 7-15页

作者： Canyu Mo Qianqiang Lin Yuanduo Niu Haoran Du Xi’an Electronic Engineering Research Institute Xi’an 710075China College of Electronic Science National University of Defense TechnologyChangsha 410199China

In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance.

关键词： Micro-motion modulation signal variational mode decomposition Genetic algorithm Adaptive optimization

Underwater acoustic signal denoising model based on secondary variational mode decomposition

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Defence Technology（防务技术） 2023年第10期28卷 87-110页

作者： Hong Yang Wen-shuai Shi Guo-hui Li School of Electronic Engineering Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China

Due to the complexity of marine environment,underwater acoustic signal will be affected by complex background noise during transmission.Underwater acoustic signal denoising is always a difficult problem in underwater acoustic signal processing.To obtain a better denoising effect,a new denoising method of underwater acoustic signal based on optimized variational mode decomposition by black widow optimization algorithm(BVMD),fluctuation-based dispersion entropy threshold improved by Otsu method(OFDE),cosine similarity stationary threshold(CSST),BVMD,fluctuation-based dispersion entropy(FDE),named BVMD-OFDE-CSST-BVMD-FDE,is proposed.In the first place,decompose the original signal into a series of intrinsic mode functions(IMFs)by BVMD.Afterwards,distinguish pure IMFs,mixed IMFs and noise IMFs by OFDE and CSST,and reconstruct pure IMFs and mixed IMFs to obtain primary denoised signal.In the end,decompose primary denoising signal into IMFs by BVMD again,use the FDE value to distinguish noise IMFs and pure IMFs,and reconstruct pure IMFs to obtain the final denoised signal.The proposed mothod has three advantages:(i)BVMD can adaptively select the decomposition layer and penalty factor of VMD.(ii)FDE and CS are used as double criteria to distinguish noise IMFs from useful IMFs,and Otsu algorithm and CSST algorithm can effectively avoid the error caused by manually selecting thresholds.(iii)Secondary decomposition can make up for the deficiency of primary decomposition and further remove a small amount of noise.The chaotic signal and real ship signal are denoised.The experiment result shows that the proposed method can effectively denoise.It improves the denoising effect after primary decomposition,and has good practical value.

关键词： Underwater acoustic signal Denoising variational mode decomposition Secondary decomposition Fluctuation-based dispersion entropy Cosine similarity

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Spatio-Temporal Wind Speed Prediction Based on variational mode decomposition

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Computer Systems Science & Engineering 2022年第11期43卷 719-735页

作者： Yingnan Zhao Guanlan Ji Fei Chen Peiyuan Ji Yi Cao School of Computer and Science Nanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.

关键词： Short-term wind speed prediction variational mode decomposition attention mechanism SENet BiLSTM

Removal of Ocular Artifacts from Electroencephalo-Graph by Improving variational mode decomposition

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China Communications 2022年第2期19卷 47-61页

作者： Miao Shi Chao Wang Wei Zhao Xinshi Zhang Ye Ye Nenggang Xie Department of Mechanical Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China College of Civil Engineering and Architecture Anhui Polytechnic UniversityAnhuiWuhu 241000PR China Department of Computer Science and Technology Anhui University of TechnologyAnhuiMa’anshan 243002PR China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterAnhuiHefei230000PR China Department of Management science and Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.variational mode decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.

关键词： ocular artifact variational mode decomposition squirrel search algorithm global guidance ability opposition-based learning

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A Multi-Granularity Density Peak Clustering Algorithm Based on variational mode decomposition

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Chinese Journal of Electronics 2021年第4期30卷 658-668页

作者： GU Ziwen LI Peng LANG Xun YU Yixuan SHEN Xin CAO Min School of Information Science and Engineering Yunnan University Electric Power Research Institute Yunnan Power Grid Co. Ltd.

Density peak clustering(DPC) can identify cluster centers quickly, without any prior knowledge. It is supposed that the cluster centers have a high density and large distance. However, some real datasets have a hierarchical structure, which will result in local cluster centers having a high density but a smaller distance. DPC is a flat clustering algorithm that searches for cluster centers globally, without considering local differences. To address this issue, a Multi-granularity DPC(MG-DPC) algorithm based on variational mode decomposition(VMD) is proposed. MG-DPC can find global cluster centers in the coarse-grained space, as well as local cluster centers in the fine-grained space.In addition, the density is difficult to calculate when the dataset has a high dimension.Neighborhood preserving embedding(NPE) algorithm can maintain the neighborhood relationship between samples while reducing the dimensionality. Moreover, DPC requires human experience in selecting cluster centers. This paper proposes a method for automatically selecting cluster centers based on Chebyshev’s inequality. MG-DPC is implemented on the dataset of load-data to realize load classification. The clustering performance is evaluated using five validity indices compared with four typical clustering methods. The experimental results demonstrate that MG-DPC outperforms other comparison methods.

关键词： Density peaks variational mode decomposition Neighborhood preserving embedding Granular computing Shape clustering

Adaptive variational mode decomposition for Bearing Fault Detection

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Journal of Signal and Information Processing 2023年第2期14卷 9-24页

作者： Xing Xing Ming Zhang Wilson Wang Electrical and Computer Engineering Lakehead University Thunder Bay Canada Automotive Engineering Weifang College of Engineering Qingzhou China Mechanical Engineering Lakehead University Thunder Bay Canada

Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of rotating machinery defects are related to bearing faults. However, reliable bearing fault detection still remains a challenging task, especially in industrial applications. The objective of this work is to propose an adaptive variational mode decomposition (AVMD) technique for non-stationary signal analysis and bearing fault detection. The AVMD includes several steps in processing: 1) Signal characteristics are analyzed to determine the signal center frequency and the related parameters. 2) The ensemble-kurtosis index is suggested to decompose the target signal and select the most representative intrinsic mode functions (IMFs). 3) The envelope spectrum analysis is performed using the selected IMFs to identify the characteristic features for bearing fault detection. The effectiveness of the proposed AVMD technique is examined by experimental tests under different bearing conditions, with the comparison of other related bearing fault techniques.

关键词： Bearing Fault Detection Vibration Signal Analysis Intrinsic mode Functions variational mode decomposition

Prediction for permeability index of blast furnace based on VMD-PSO-BP model

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Journal of Iron and Steel Research(International) 2024年第3期31卷 573-583页

作者： Xiao-jie Liu Yu-jie Zhang Xin Li Zhi-feng Zhang Hong-yang Li Ran Liu Shu-jun Chen School of Metallurgy and Energy North China University of Science and TechnologyTangshan 063210HebeiChina Chengde Branch HBIS Group Co.Ltd.Chengde 067000HebeiChina

The permeability index is one of the important production indicators to monitor the operation of blast furnace.It is crucial to grasp the trends of changes in the new permeability index in time.For the complex vibration spectrum of the permeability index,a prediction model of the permeability index based on the VMD-PSO-BP(variational mode decomposition-particle swarm optimization-back propagation)method was proposed.Firstly,the key factors that affect the permeability index of blast furnace were studied from multiple perspectives.Then,the permeability index was divided into multiple sub-modes based on the difference of frequency bands by the VMD algorithm,and a PSO-BP prediction model was established for each sub-mode.Finally,the prediction results of each sub-mode were summed to obtain the final one.The results show that the composite prediction accuracy by using the VMD algorithm is 3%higher than that of the traditional prediction method,which has better applicability.

关键词： Big data-Blast furnace Air permeability variational mode decomposition Particle swarm optimization Back propagation model prediction

Quality assessment of friction-stir-welded aluminum alloy welds via three-dimensional force signals

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Advances in Manufacturing 2024年第1期12卷 61-75页

作者： Ji-Hong Dong Yi-Ming Huang Jia-Lei Zhu Wei Guan Xu-Kai Ren Huan-Wei Yu Lei Cui Beijing Institute of Petrochemical Technology Beijing102617People’s Republic of China Beijing Academy of Safety Engineering and Technology Beijing102617People’s Republic of China AVIC Manufacturing Technology Institute Beijing100024People’s Republic of China Tianjin Key Laboratory of Advanced Joining Technology School of Materials Science and EngineeringTianjin UniversityTianjin300350People’s Republic of China Shaoxing Special Equipment Testing Institute Shaoxing Key Laboratory of Special Equipment Intelligent Testing and EvaluationShaoxing312071ZhejiangPeople’s Republic of China

An online detection technology must be developed for realizing the real-time control of friction stir welding.In this study,the three-dimensional force exerted on a material during friction stir welding was collected synchronously and the relationship between the forces and welding quality was investigated.The results indicated that the fluctuation period of the traverse force was equal to that of the lateral force during the stable welding stage.The phase difference between two horizontal forces wasπ/2.The values of the horizontal forces increased with welding speed,whereas their amplitudes remained the same.The proposed force model showed that the traverse and lateral forces conformed to an elliptical curve,and this result was consistent with the behavior of the measured data.The variational mode decomposition was used to process the plunge force.The intrinsic mode function that represented the real fluctuation in the plunge force varied at the same frequency as the spindle rotational speed.When tunnel defects occurred,the fluctuation period features were consistent with those obtained during normal welding,whereas the ratio parameter defined in this study increased significantly.

关键词： Friction stir welding(FSW) Three-dimensional force model variational mode decomposition Tunnel defect

Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation modes Using PMU Data

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Journal of modern Power Systems and Clean Energy 2024年第1期12卷 34-40页

作者： Joice G.Philip Jaesung Jung Ahmet Onen the Department of Energy Systems Research Ajou UniversitySuwonSouth Korea the Department of Electrical and Electronic Engineering Abdullah Gul UniversityKayseri38080Turkey the Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscatOman

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscillations.If the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm.The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

关键词： Empirical wavelet transform(EWT) sub-synchronous oscillation Prony-based method Yoshida algorithm variational mode decomposition phasor measurement unit(PMU)

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