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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 *** acoustic signal denoising is always a difficult problem in underwater acoustic signal *** 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 *** the first place,decompose the original signal into a series of intrinsic mode functions(IMFs)by ***,distinguish pure IMFs,mixed IMFs and noise IMFs by OFDE and CSST,and reconstruct pure IMFs and mixed IMFs to obtain primary denoised *** 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 *** 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 *** chaotic signal and real ship signal are *** experiment result shows that the proposed method can effectively *** 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

A debris-flow forecasting method with infrasound-based variational mode decomposition and ARIMA

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Journal of Mountain Science 2024年第12期21卷 4019-4032页

作者： DONG Hanchuan LIU Shuang PANG Lili LIU Dunlong DENG Longsheng FANG Lide ZHANG Zhonghua College of Quality and Technical Supervision Hebei UniversityBaoding 071002China Technology Innovation Center for Geological Environment Monitoring Ministry of Natural ResourcesTianjin 300304China Center for Hydrogeology and Environmental Geology Survey China Geological SurveyTianjin 300304China Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu 610299China College of Software Engineering Chengdu University of Information and TechnologyChengdu 610225China School of Geological Engineering and Geomatics Chang’an UniversityXi’an 710054China

Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris ***,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)***-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and *** mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search *** Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the *** on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound *** reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence *** Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.

关键词： Debris flow infrasound variational mode decomposition Sparrow search algorithm ARIMA model Hilbert transform

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 *** 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 ***,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 ***,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal ***,VASTN accumulates the predictions of each IMF to obtain the predicted wind *** 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 *** 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 *** mode decomposition(VMD)is an adaptive and completely nonrecursive signal processing *** are two parameters in VMD that have a great influence on the result of signal ***,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 *** original seasonal monitoring condition in SSA is *** feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring ***-based learning is introduced to reposition the position of the population in this *** is applied to optimize the important parameters of ***-VMD model is established to remove ocular artifacts from EEG *** have verified the effectiveness of our proposal in a public dataset compared with other *** 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

variational mode decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis

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Journal of Applied Mathematics and Physics 2024年第6期12卷 2320-2332页

作者： Zhenyu Xu Zhangwei Chen State Key Lab of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China

Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While variational mode decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.

关键词： Electric Vibrator Noise Analysis Signal Decomposing variational mode decomposition Empirical Wavelet Transform

Microseismic signal denoising by combining variational mode decomposition with permutation entropy

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Applied Geophysics 2022年第1期19卷 65-80,144,145页

作者： Zhang Xing-Li Cao Lian-Yue Chen Yan Jia Rui-Sheng Lu Xin-Ming College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and TechnologyQingdao 266590China

Remarkable progress has been achieved on microseismic signal denoising in recent years,which is the basic component for rock-burst ***,its denoising effectiveness remains *** extract the effective microseismic signal from polluted noisy signals,a novel microseismic signal denoising method that combines the variational mode decomposition(VMD)and permutation entropy(PE),which we denote as VMD–PE,is proposed in this *** is a recently introduced technique for adaptive signal decomposition,where K is an important decomposing parameter that determines the number of *** provides a predictable eff ect on the nature of detected *** this work,we present a method that addresses the problem of selecting an appropriate K value by constructing a simulation signal whose spectrum is similar to that of a mine microseismic signal and apply this value to the VMD–PE *** addition,PE is developed to identify the relevant effective microseismic signal modes,which are reconstructed to realize signal *** experimental results show that the VMD–PE method remarkably outperforms the empirical mode decomposition(EMD)–VMD filtering and detrended fl uctuation analysis(DFA)–VMD denoising methods of the simulated and real microseismic *** expect that this novel method can inspire and help evaluate new ideas in this field.

关键词： Denoising Microseismic signal Permutation entropy variational mode decomposition

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 *** improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the *** algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of *** approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation *** optimal mode number and quadratic penalty factor for VMD are *** 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)*** effective modes are then reconstructed with the denoised modes,achieving signal *** experimental data verification,the proposed algorithm demonstrates effective denoising of modulation *** 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

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

Adaptive sensitive frequency band selection for VMD to identify defective components of an axial piston pump

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Chinese Journal of Aeronautics 2022年第1期35卷 250-265页

作者： Anil KUMAR C.P.GANDHI Hesheng TANG Govind VASHISHTHA Rajesh KUMAR Yuqing ZHOU Jiawei XIANG College of Mechanical and Eilectrical Engineering Wenzhou UniversityWenzhou 325035China Amity University Uttar Pradesh Noida 201313India Ray at Bahru University Mohali 140104India Sant Longowal Institute of Engineering and Technology Longowal 148106India

The underlying study investigates single valued neutrosophic entropy based adaptive sensitive frequency band selection for variational mode decomposition(VMD)for the purpose of identifying defective components in an axial *** proposed methodology is applied in the following ***,VMD is applied for decomposing vibration signals into various frequency bands,called as *** computing energy of each VMD,the lower(minimum)and upper(maximum)bounds from these energy readings are extracted for defect conditions,such as outer race,inner race,worn piston,faulty cylinder and valve plate,and blocked hole of the ***,energy interval ranges are obtained and further converted into the form of single valued neutrosophic sets(SVNSs).Then,the proposed neutrosophic entropy measure is deployed for quantifying the non-linear connection between each bearing defect conditions and various frequency *** mode having maximum neutrosophic entropy value is designated to the“most sensitive”frequency ***,envelope demodulation is applied to the most sensitive selected frequency band for finding defective *** proposed neutrosophic entropy and VMD based methodology is effective in providing a better insight for selecting suitable frequency band for carrying out envelope demodulation in comparison to existing methods.

关键词： Axial piston pump Frequency band selection Reciprocating machinery Rotating machinery Trigonometric neutrosophic entropy variational mode decomposition Vibration