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Showing 1-25 of 822 resultsfor "Index Terms":variational mode decomposition (VMD)
"Index Terms":variational mode decomposition (VMD)
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Improved VMD method and its application in power quality disturbance detection
2023 8th Asia Conference on Power and Electrical Engineering (ACPEE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Aiming at the weak anti-interference and low accuracy problems of power quality disturbance detection in noisy environment, a variational mode decomposition method based on Pearson correlation coefficient method and arithmetic optimization algorithm is proposed. Pearson correlation coefficient is used to determine the optimal number of signal decomposition, and after that, the optimal penalty fact...Show More
Application of VMD Algorithm in UGW-based Rail Breakage Detection System
2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
As a solid medium of sound propagation, rails provide perfect acoustic features. In this way, rail breakage detection systems based on ultrasonic guided waves (UGW) have been recently developed. In outdoor applications of these systems, different types of interference are usually added to the received signal, which makes UGW signals difficult to distinguish and analyze, even leading to false alarm...Show More
Comparative study between VMD and EMD in bearing fault diagnosis
2014 9th International Conference on Industrial and Information Systems (ICIIS)
Year: 2014 | Conference Paper |
Cited by: Papers (54)
This paper proposes a novel Variational mode decomposition (VMD) algorithm for bearing fault diagnosis. The Fast Fourier Transform fails to analyse the transient and non-stationary signals. Discrete Fourier transform and Empirical mode decomposition do not have the ability to attain the accurate Intrinsic mode functions under dynamic system fault conditions because the characteristic of expo...Show More
Adaptive Signal Decomposition Methods
2024 Systems of Signals Generating and Processing in the Field of on Board Communications
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Digital signal processing methods are crucial in analyzing radio signals, especially in complex radio electronic environments (REE). With advancements in radio communication, navigation, and control systems, REE often hosts multiple sources operating on the same or adjacent spectrum segments, representing various radio systems. Consequently, signal mixing from different sources with additive noise...Show More
Frequency Shifting-based Variational Mode Decomposition Method for Speech Signal Decomposition
2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)
Year: 2022 | Conference Paper |
Cited by: Papers (6)
In order to solve the problem of mode mixing and mode aliasing arising from speech decomposition, this paper proposes a speech signal decomposition method based on Variational Mode Decomposition (VMD): Variational Mode Decomposition-Frequency Shifting, VMD-FS). The method takes advantage of the VMD's good extraction of the fundamental frequency of the speech signal, sets specific...Show More
As a key equipment of the high-speed Electric multiple unit (EMU), the Balise transmission module (BTM) is tend to be affected by electromagnetic disturbances. However, the existing filter can not well eliminate electromagnetic disturbances. In order to improve the Signal to noise ratio (SNR) of BTM uplink signal, Variational mode decomposition (VMD) method is introduced into de-noising the ...Show More
A Parameter Optimized Variational Mode Decomposition Method for Harmonic and Inter-harmonic Detection
2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
An variational mode decomposition (VMD) has been applied in the field of harmonic detection, but the error will be large if the decomposition parameters are set artificially. To improve the accuracy of VMD in inter-harmonics detection, we need to determine the number of wolves, maximum number of iterations, convergence factor and other parameters, and then select component sample entro...Show More
Research on fault diagnosis of rolling bearing based on Variational Mode Decomposition and Hilbert envelope spectrum analysis
2024 36th Chinese Control and Decision Conference (CCDC)
Year: 2024 | Conference Paper |
Rolling bearing is an important part of rotating machinery equipment, so fault diagnosis is of great significance. A fault diagnosis method combining VMD (Variational Mode Decomposition) and Hilbert envelope spectrum analysis is proposed. First, the VMD method which optimized by Antlion optimization algorithm is used to decompose the original fault signal to obtain the IMF(Intrinsic Mo...Show More
Comparative Study Between EEMD-MED and VMD-MED Techniques Dedicated to Bearing Fault Detection in Induction Motors
Yasser Damine;Salim Sbaa;Noureddine Bessous;Ahmed Chaouki Megherbi
2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
To improve defect detection in rotating electrical machines (REMs), many techniques are used in this field. Bearing defects can be damaged the REMs. This paper compares Ensemble Empirical Mode Decomposition combined with Minimum Entropy Deconvolution (EEMD-MED) to variational mode decomposition with MED (VMD-MED) to detect bearing defects. First, VMD divides the signal into IMFs. In ad...Show More
Extraction of Ballistocardiogram Signal and Respiratory Signal based on WOA-VMD Algorithm
2023 4th International Conference on Computer Engineering and Application (ICCEA)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The acquisition of ballistocardiogram (BCG) signals during sleeping always presents all kinds of high-frequency noise, which causes the problem to extract effective signals. This article proposes a new method to extract heartbeat and respiration signals by combining the whale optimization algorithm (WOA) and variational mode decomposition (VMD). Firstly, the sample entropy and energy differe...Show More
Research on fault feature extraction of rotating machinery based on adaptive VMD and optimized CYCBD
2022 34th Chinese Control and Decision Conference (CCDC)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
To solve the problem that early fault features of rotating machinery are difficult to extract, an adaptive k-value hierarchical variational mode decomposition (H-VMD) combined with optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) fault feature extraction method is proposed in this paper. Mode decomposition of the vibration signal is performed with H-VMD. and...Show More
EMG Signal Decomposition Methods for Physical Actions: A Comparative Analysis
2023 1st International Conference on Circuits, Power and Intelligent Systems (CCPIS)
Year: 2023 | Conference Paper |
Advanced approaches are needed for the detection, decomposition, and classification of EMG data obtained from muscles. This study evaluates three popular decomposition techniques, Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD), and Variational Mode Decomposition (VMD), in order to determine which one offers superior accuracy. This paper uses the dataset of physical acti...Show More
Prior Frequency Information Assisted VMD Method for SBP Sonar Data Noise Removal
Sub-bottom profiler (SBP) sonar data are always polluted by noise that leads to the wrong interpretation. To deal with this situation, a method combining the prior information of SBP sonar data and the variational mode decomposition (VMD) framework is proposed attempting to recover clean data from its corrupted version. First, to deal with the unknown mode number of VMD method, a mode ...Show More
Short-Term EV Charging Load Predicting Based on Adaptive VMD and LSTM Methods
IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society
Year: 2023 | Conference Paper |
Cited by: Papers (5)
The uncoordinated charging of large-scale electric vehicles (EVs) generally deteriorates the peak-valley difference of daily electric demands. To facilitate the operation of charging stations and electric power distributers, this work proposes a charging load prediction algorithm by combining the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) methods. The VMD::...Show More
A Denoising Method of Partial Discharge Signal Based on Improved SVD-VMD
A denoising method combined with singular value decomposition (SVD) and variational mode decomposition (VMD) is proposed to eliminate noise in on-site partial discharge (PD) signals from high-voltage electrical equipment. In the Fourier transform power spectrum, periodic narrowband interference was eliminated after SVD was offered to determine the number of singular values of periodic narrow...Show More
Power load Forecasting based on VMD Multifrequency Combinations
Pingli Li;Hongchi Liu;Meng Li;Qiang He;Hailiang Chao;Gang Zhang
2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Power system load forecasting plays an important role in formulating the power system development planning, fuel planning and power generation planning. The traditional single model cannot fully characterize the fluctuating characteristics of load, scholars have attempted to build other prediction models based on empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) to...Show More
Heart Rate Extraction with VMD Algorithm in Non-Stationary Clutter Environment Based on FMCW Radar Systems
2021 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT)
Year: 2021 | Conference Paper |
Cited by: Papers (8)
This paper presents a variational mode decomposition (VMD)-based heart rate extraction algorithm to extract heart rate of the target under intermittent interference of non-stationary clutters caused by another person. When nonstationary clutters exist around the interested target, the heart rate detection becomes difficult for FMCW radars. In regard to the situation, VMD is utilized to...Show More
Adaptive Harmonic Detection of Active Power Filter based on Improved VMD
Wenjie Huang;Rongkun Wang;Yujia Zhuang;Zhixin Wang;Quankai Du
2022 IEEE 5th International Electrical and Energy Conference (CIEEC)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Harmonic detection accuracy is the key factor affecting the performance of active power filter (APF). As an effective harmonic detection method, any complex signal can be adaptively decomposed into several intrinsic mode functions (IMFs) with a certain bandwidth by Variational Mode Decomposition (VMD). Aiming at the problems that VMD will be affected under the background of strong nois...Show More
Variational Mode Decomposition-based Notch Filter for Bearing Fault Detection
Yassine Amirat;Elhoussin Elbouchikhi;Zhibin Zhou;Mohamed Benbouzid;Gilles Feld
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Year: 2019 | Conference Paper |
Cited by: Papers (6)
This work presents a variational mode decomposition (VMD) based detector for bearing fault in electrical machines. Its performance is compared to that of the ensemble empirical mode decomposition (EEMD) based. A notch filter based Pearson correlation was developed and used to extract the dominant mode. Experimental results showed that the VMD outperformed in terms of statistical featur...Show More
Spectral Decomposition for Hydrocarbon Detection Based on VMD and Teager–Kaiser Energy
Wei Liu;Siyuan Cao;Zhiming Wang;Xiangzhan Kong;Yangkang Chen
Hydrocarbons can cause anomalies in the energy density of seismic signals when seismic waves pass through them. Teager-Kaiser energy (TKE) is an important attribute that can be utilized to depict the energy density of a seismic signal and the energy distribution of a seismic wavefield. In this letter, a novel spectral decomposition-based approach for hydrocarbon detection is proposed that applies ...Show More
MOMEDA Model Based Variational Mode Decomposition for Pelton Wheel Fault Detection
2021 International Conference on Simulation, Automation & Smart Manufacturing (SASM)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
The identification of fault frequency is a vital step in fault diagnosis. For this purpose, variational mode decomposition (VMD) is being used widely as it has ability to represent the signal in Time-frequency domain. The VMD, on the other hand, often fails to analyse non-stationary signals with low-frequency noises. In this paper, a multipoint optimal minimum entropy deconvolution adj...Show More
An efficient method for extracting respiratory activity from single-lead-ECG based on variational mode decomposition
2015 22nd Iranian Conference on Biomedical Engineering (ICBME)
Year: 2015 | Conference Paper |
Cited by: Papers (4)
Recording and monitoring of respiratory signal has a great importance in medical fields. Old methods for recording this signal are mostly expensive, affected from the environmental conditions and troublesome for the patient. Consequently, using indirect methods like ECG-derived respiratory signal (EDR) is an appropriate solution for reducing above problems. In this regard, multi resolution decompo...Show More
A New Feature Extraction for Rolling Bearing Using Sparse Representation Based on Improved K-singular Value Decomposition and VMD
2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
Bearings are an essential part of the rotating machinery, which are easy to fail prematurely because of the complex working environment. When rolling bearings are damaged, the multi-component coupling is constantly vibrated, which makes fault signals appear nonlinear and nonstationary. The periodic shock components associated with the fault information are mixed with a great many noise. Unluckily,...Show More
ECG Denoising Method Based on an Improved VMD Algorithm
Chengjun Li;Yacen Wu;Haijun Lin;Jianmin Li;Fu Zhang;Yuxiang Yang
Electrocardiogram (ECG) acquisition is easily contaminated by interferences, and denoising is the most important task in ECG detection. The variational mode decomposition (VMD) algorithm is widely used in ECG denoising, which can overcome mode aliasing between intrinsic mode function (IMF) components that existed in the traditional empirical mode decomposition (EMD) algorithm, but the mode d...Show More
Short-Term PV Power Interval Prediction Based on VMD and Deep Learning Model
2023 China Automation Congress (CAC)
Year: 2023 | Conference Paper |
At present, the short-term prediction of photovoltaic power generation is very important for the safe and stable operation of photovoltaic power generation system. Aiming at the problem that point prediction is difficult to describe the fluctuation of photovoltaic power generation, a method of PV power generation interval prediction based on variational mode decomposition and deep learning model i...Show More
Short-Term PV Power Interval Prediction Based on VMD and Deep Learning Model
Year: 2023 | Conference Paper |
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