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Journal articles on the topic '2D variational mode decomposition'

Author: Grafiati

Published: 21 June 2025

Last updated: 2 August 2025

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1

Zhang, Xingli, Yan Chen, Ruisheng Jia, and Xinming Lu. "Two-dimensional variational mode decomposition for seismic record denoising." Journal of Geophysics and Engineering 19, no. 3 (2022): 433–44. http://dx.doi.org/10.1093/jge/gxac032.

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Abstract Seismic signal denoising is the main task of seismic data processing. This study proposes a novel method for the denoising seismic record on the basis of a two-dimensional variational mode decomposition (2D-VMD) algorithm and permutation entropy (PE). 2D-VMD is a recently introduced adaptive signal decomposition method in which $K$ and $\alpha $ are important decomposing parameters to determine the number of modes, and have a predictable effect on the nature of detected modes. We present a novel method to address the problems of selecting appropriate $K$ and $\alpha $ values and apply

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Li, Yue, Linlin Li, and Chao Zhang. "Desert seismic signal denoising by 2D compact variational mode decomposition." Journal of Geophysics and Engineering 16, no. 6 (2019): 1048–60. http://dx.doi.org/10.1093/jge/gxz065.

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Abstract Noise suppression and effective signal recovery are very important for seismic signal processing. The random noise in desert areas has complex characteristics due to the complex geographical environment; noise characteristics such as non-stationary, non-linear and low frequency. These make it difficult for conventional denoising methods to remove random noise in desert seismic records. To address the problem, this paper proposes a two-dimensional compact variational mode decomposition (2D-CVMD) algorithm for desert seismic noise attenuation. This model decomposes the complex desert se

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Liu, Chao, Ziang Wang, Yaping Huang, Aiping Zeng, and Hongming Fan. "Application of 2D Variational Mode Decomposition Method in Seismic Signal Denoising." Elektronika ir Elektrotechnika 30, no. 2 (2024): 46–53. http://dx.doi.org/10.5755/j02.eie.36100.

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Seismic data are typical nonlinear and nonstationary data. In the acquisition and processing of seismic data, many factors interfere with it. Seismic data contain both effective waves and random noises, seriously affecting the quality of seismic data and not conducive to the goal of fine interpretation of subsequent seismic data. Therefore, studying new seismic data denoising methods is beneficial for improving the quality of seismic data and plays a very important role in subsequent seismic data interpretation. In this paper, the principle of variational mode decomposition (VMD) and 2D-VMD is

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Kang, Shouqiang, Yaqi Liang, Yujing Wang, and Mikulovich V I. "Color image encryption method based on 2D-variational mode decomposition." Multimedia Tools and Applications 78, no. 13 (2019): 17719–38. http://dx.doi.org/10.1007/s11042-018-7129-4.

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Chen, Daxing. "Two-Dimensional Variational Mode Decomposition for Noise Suppression in Nanoscale Optoelectronic Signal Processing." Journal of Nanoelectronics and Optoelectronics 20, no. 4 (2025): 415–27. https://doi.org/10.1166/jno.2025.3749.

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This paper proposes a denoising method based on two-dimensional variational mode decomposition (2D-VMD) and a subtraction-average-based optimizer (SABO) for enhancing signal clarity in nanoscale optoelectronic systems. The SABO algorithm optimizes the decomposition parameters (mode number K and penalty factor α) using minimum image entropy, enabling efficient separation of noise from high-resolution signals in nanophotonic imaging or quantum dot-based sensors. The 2D-VMD decomposes multi-channel optoelectronic data into intrinsic mode functions (IMFs), which are analyzed via frequency-wavenumb

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Weng, Wenbo. "Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition." Scientific Programming 2023 (April 25, 2023): 1–12. http://dx.doi.org/10.1155/2023/8846287.

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A synthetic aperture radar (SAR) automatic target recognition (ATR) method is developed based on the two-dimensional variational mode decomposition (2D-VMD). 2D-VMD decomposes original SAR images into multiscale components, which depict the time-frequency properties of the targets. The original image and its 2D-VMD components are highly correlated, so the multitask sparse representation is chosen to jointly represent them. According to the resulted reconstruction errors of different classes, the target label of test sample can be classified. The moving and stationary target acquisition and rec

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Xiao, Qiyang, Jian Li, Sijin Wu, et al. "Adaptive DSPI phase denoising using mutual information and 2D variational mode decomposition." Measurement Science and Technology 29, no. 4 (2018): 045203. http://dx.doi.org/10.1088/1361-6501/aaa380.

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Zhang, Xuebing, Yuxiang Qin, Yaoyao Li, et al. "A GPR 2D Teager-Kaiser energy operator based on the multivariate variational mode decomposition." Remote Sensing Letters 14, no. 1 (2022): 30–38. http://dx.doi.org/10.1080/2150704x.2022.2154178.

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Cai, Kewei, Taoping Hu, Wenping Cao, and Guofeng Li. "Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network." Applied Sciences 9, no. 18 (2019): 3681. http://dx.doi.org/10.3390/app9183681.

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This paper presents a hybrid approach combining phase space reconstruction (PSR) with a convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a PSR technique is developed to transform a 1D voltage disturbance signal into a 2D image file. Then, a CNN model is developed for the image classification. The feature maps are extracted automatically from the image file and different patterns are derived from variables in CNN. A set of synthetic signals, as well as operational measurements, are used to validate the proposed method. Moreover, the test results ar

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Jiang, Liubing, Xiaolong Zhou, Li Che, Shuwei Rong, and Hexin Wen. "Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition." Sensors 19, no. 9 (2019): 1962. http://dx.doi.org/10.3390/s19091962.

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As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dime

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Zhang, Yi, Fuzhou Liu, Jie Guan, and Yongli Zhu. "Time-frequency Fusion Method via Convolutional Neural Network for Partial Discharge Classification." Journal of Physics: Conference Series 2452, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2452/1/012014.

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Abstract To improve the accuracy of partial discharge (PD) pattern recognition by jointing time-domain (TD) and frequency-domain (FD) information, a time-frequency (TF) fusion method via convolution neural network (CNN) is proposed in this paper. Firstly, PD signals are represented by PD waveform images and transformed into the envelope of variational mode decomposition-based Hilbert marginal spectrum (VHMS). Secondly, a fusion network, FuNet involving a 2-dimensional CNN (2D-CNN), a 1D-CNN, and a multilayer perceptron (MLP), is established to join TF information. In FuNet, the 2D-CNN inputted

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Zhu, Yuying, Shuning Zhang, Huichang Zhao, and Si Chen. "Target Identification with Improved 2D-VMD for Carrier-Free UWB Radar." Sensors 21, no. 7 (2021): 2465. http://dx.doi.org/10.3390/s21072465.

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In recent years, the interest in radar automatic target recognition (RATR) based on the carrier-free ultra-wideband (UWB) radar has been increasing. Compared with narrow-band and other bandwidth radars, the echo signal of the carrier-free UWB radar includes more comprehensive and detailed information with respect to the targeted object. In this paper, we first utilized 3ds Max to acquire accurate geometric models and applied a time-domain integral equation (TDIE) for echo signal acquisition under the condition that the transmitted signals had an extremely short duration period. By comparing th

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Yan, Hongbo, Pengbo Zhao, Zhuang Du, Yang Xu, and Pei Liu. "Frequency division denoising algorithm based on VIF adaptive 2D-VMD ultrasound image." PLOS ONE 16, no. 3 (2021): e0248146. http://dx.doi.org/10.1371/journal.pone.0248146.

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Ultrasound imaging has developed into an indispensable imaging technology in medical diagnosis and treatment applications due to its unique advantages, such as safety, affordability, and convenience. With the development of data information acquisition technology, ultrasound imaging is increasingly susceptible to speckle noise, which leads to defects, such as low resolution, poor contrast, spots, and shadows, which affect the accuracy of physician analysis and diagnosis. To solve this problem, we proposed a frequency division denoising algorithm combining transform domain and spatial domain. F

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Hou, Sizu, and Wenyao Wang. "Fault-Line Selection Method in Active Distribution Networks Based on Improved Multivariate Variational Mode Decomposition and Lightweight YOLOv10 Network." Energies 17, no. 19 (2024): 4958. http://dx.doi.org/10.3390/en17194958.

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In active distribution networks (ADNs), the extensive deployment of distributed generations (DGs) heightens system nonlinearity and non-stationarity, which can weaken fault characteristics and reduce fault detection accuracy. To improve fault detection accuracy in distribution networks, a method combining improved multivariate variational mode decomposition (IMVMD) and YOLOv10 network for active distribution network fault detection is proposed. Firstly, an MVMD method optimized by the northern goshawk optimization (NGO) algorithm named IMVMD is introduced to adaptively decompose zero-sequence

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Qian, Shouguo, Xianqing Lv, Yanhua Cao, and Fenjing Shao. "Parameter Estimation for a 2D Tidal Model with POD 4D VAR Data Assimilation." Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/6751537.

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Combining the proper orthogonal decomposition (POD) reduced order method and 4D VAR (four-dimensional Variational) data assimilation method with a two-dimensional (2D) tidal model, a model is constructed to simulate theM2tide in the Bohai, Yellow, and East China Seas (BYECS). This model consists of two submodels: the POD reduced order forward model is used to simulate the tides, while its adjoint model is used to optimize the control variables. Numerical experiment is carried out to assimilate the harmonic constants, which are derived from TOPEX/Poseidon (T/P) altimeter data, into the 2D tidal

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Wu, Zhe, Qiang Zhang, Lifeng Cheng, and Shengyue Tan. "A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion." Applied Sciences 9, no. 24 (2019): 5443. http://dx.doi.org/10.3390/app9245443.

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Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbi

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Wang, Lei, Yigang He, and Lie Li. "A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network." Electronics 10, no. 3 (2021): 255. http://dx.doi.org/10.3390/electronics10030255.

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High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including

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18

Johnson, Christine, Brian J. Hoskins, Nancy K. Nichols, and Susan P. Ballard. "A Singular Vector Perspective of 4DVAR: The Spatial Structure and Evolution of Baroclinic Weather Systems." Monthly Weather Review 134, no. 11 (2006): 3436–55. http://dx.doi.org/10.1175/mwr3243.1.

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Abstract The extent to which the four-dimensional variational data assimilation (4DVAR) is able to use information about the time evolution of the atmosphere to infer the vertical spatial structure of baroclinic weather systems is investigated. The singular value decomposition (SVD) of the 4DVAR observability matrix is introduced as a novel technique to examine the spatial structure of analysis increments. Specific results are illustrated using 4DVAR analyses and SVD within an idealized 2D Eady model setting. Three different aspects are investigated. The first aspect considers correcting error

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19

Shen, Yamin, Yuxuan Ma, Simin Deng, Chiou-Jye Huang, and Ping-Huan Kuo. "An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting." Sustainability 13, no. 4 (2021): 1694. http://dx.doi.org/10.3390/su13041694.

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Electricity load forecasting is one of the hot concerns of the current electricity market, and many forecasting models are proposed to satisfy the market participants’ needs. Most of the models have the shortcomings of large computation or low precision. To address this problem, a novel deep learning and data processing ensemble model called SELNet is proposed. We performed an experiment with this model; the experiment consisted of two parts: data processing and load forecasting. In the data processing part, the autocorrelation function (ACF) was used to analyze the raw data on the electricity

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20

Protas, Bartosz, Bernd R. Noack, and Jan Östh. "Optimal nonlinear eddy viscosity in Galerkin models of turbulent flows." Journal of Fluid Mechanics 766 (February 4, 2015): 337–67. http://dx.doi.org/10.1017/jfm.2015.14.

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AbstractWe propose a variational approach to the identification of an optimal nonlinear eddy viscosity as a subscale turbulence representation for proper orthogonal decomposition (POD) models. The ansatz for the eddy viscosity is given in terms of an arbitrary function of the resolved fluctuation energy. This function is found as a minimizer of a cost functional measuring the difference between the target data coming from a resolved direct or large-eddy simulation of the flow and its reconstruction based on the POD model. The optimization is performed with a data-assimilation approach generali

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21

Li, Wu, Lanre Akinyemi, Dianchen Lu, and Mostafa M. A. Khater. "Abundant Traveling Wave and Numerical Solutions of Weakly Dispersive Long Waves Model." Symmetry 13, no. 6 (2021): 1085. http://dx.doi.org/10.3390/sym13061085.

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In this article, plenty of wave solutions of the (2 + 1)-dimensional Kadomtsev–Petviashvili–Benjamin–Bona–Mahony ((2 + 1)-D KP-BBM) model are constructed by employing two recent analytical schemes (a modified direct algebraic (MDA) method and modified Kudryashov (MK) method). From the point of view of group theory, the proposed analytical methods in our article are based on symmetry, and effectively solve those problems which actually possess explicit or implicit symmetry. This model is a vital model in shallow water phenomena where it demonstrates the wave surface propagating in both directio

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Dragomiretskiy, Konstantin, and Dominique Zosso. "Variational Mode Decomposition." IEEE Transactions on Signal Processing 62, no. 3 (2014): 531–44. http://dx.doi.org/10.1109/tsp.2013.2288675.

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23

Nazari, Mojtaba, and Sayed Mahmoud Sakhaei. "Successive variational mode decomposition." Signal Processing 174 (September 2020): 107610. http://dx.doi.org/10.1016/j.sigpro.2020.107610.

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Rehman, Naveed ur, and Hania Aftab. "Multivariate Variational Mode Decomposition." IEEE Transactions on Signal Processing 67, no. 23 (2019): 6039–52. http://dx.doi.org/10.1109/tsp.2019.2951223.

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Wang, Yuanxin. "An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis." Shock and Vibration 2021 (November 11, 2021): 1–5. http://dx.doi.org/10.1155/2021/2030128.

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Variational mode decomposition is an adaptive nonrecursive signal decomposition and time-frequency distribution estimation method. The improper selection of the decomposition number will cause under decomposition or over decomposition, and the improper selection of the penalty factor will affect the bandwidth of modal components, so it is very necessary to look for the optimal parameter combination of the decomposition number and the penalty factor of variational mode decomposition. Hence, differential evolution algorithm is used to look for the optimization combination of the decomposition nu

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Wu, Guoning, Guochang Liu, Junxian Wang, and Pingping Fan. "Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition." Computational Intelligence and Neuroscience 2022 (February 26, 2022): 1–14. http://dx.doi.org/10.1155/2022/2132732.

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Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode decomposition were proposed for lateral continuity consideration. Due to large input data, mini-batch multivariate variational mode decomposition is proposed in this paper. The proposed method takes advantages both of variational mode decomposition and mu

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Zhang, Jianwei, Ge Hou, Han Wang, Yu Zhao, and Jinlin Huang. "Operation feature extraction of flood discharge structure based on improved variational mode decomposition and variance dedication rate." Journal of Vibration and Control 26, no. 3-4 (2019): 229–40. http://dx.doi.org/10.1177/1077546319878542.

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Operation feature extraction of flood discharge structures under ambient excitation has attracted increasing attention in recent years. However, the vibration signal of flood discharge structures is a nonstationary random signal with low signal-to-noise ratio, which is mixed with lots of low-frequency water flow noise and high-frequency white noise. It is difficult to excavate the hidden vibration characteristic information accurately. To solve the problem, we propose a novel denoising method called improved variational mode decomposition. As an improved method of variational mode decompositio

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Lin, Peng, Jingtao Zhao, Suping Peng, and Xiaoqin Cui. "Diffraction separation by variational mode decomposition." Geophysical Prospecting 69, no. 5 (2021): 1070–85. http://dx.doi.org/10.1111/1365-2478.13093.

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Choi, Guebin, Hee-Seok Oh, Youngjo Lee, Donghoh Kim, and Kyungsang Yu. "Variational Mode Decomposition with Missing Data." Korean Journal of Applied Statistics 28, no. 2 (2015): 159–74. http://dx.doi.org/10.5351/kjas.2015.28.2.159.

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Baldini, Gianmarco, Gary Steri, Raimondo Giuliani, and Franc Dimc. "Radiometric identification using variational mode decomposition." Computers & Electrical Engineering 76 (June 2019): 364–78. http://dx.doi.org/10.1016/j.compeleceng.2019.04.014.

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Zosso, Dominique, Konstantin Dragomiretskiy, Andrea L. Bertozzi, and Paul S. Weiss. "Two-Dimensional Compact Variational Mode Decomposition." Journal of Mathematical Imaging and Vision 58, no. 2 (2017): 294–320. http://dx.doi.org/10.1007/s10851-017-0710-z.

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Esquivel-Cruz, Eduardo, Francisco Beltran-Carbajal, Ivan Rivas-Cambero, José Humberto Arroyo-Núñez, Ruben Tapia-Olvera, and Daniel Guillen. "Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals." Algorithms 18, no. 1 (2025): 25. https://doi.org/10.3390/a18010025.

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Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of very similar frequencies and mode mixing. In this context, a hybrid strategy to estimate harmonic vibration modes in weakly damped, multi-degree-of-freedom vibrating mechanical systems by combining Empirical Mode Decomposition and Variational Mode Decomposition is described.

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Li, Haodong, Ying Xu, Dong An, Lixiu Zhang, Songhua Li, and Huaitao Shi. "Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 2 (2019): 335–51. http://dx.doi.org/10.1177/1461348419846730.

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Fault diagnosis of rolling bearings can effectively prevent sudden accidents and is an important factor for the safe operation of mechanical systems. However, traditional time–frequency analysis techniques cannot effectively obtain the fault feature information. In this paper, a flat variational modal decomposition denoising method based on wavelet transform and variational modal decomposition is proposed to solve susceptibility of vibration signal to noise interference and easily obtain fault features. In this method, first, a series of mother wavelets with different periods are designed base

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An, Xueli, and Fei Zhang. "Pedestal looseness fault diagnosis in a rotating machine based on variational mode decomposition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 13 (2016): 2493–502. http://dx.doi.org/10.1177/0954406216637378.

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According to the non-stationary characteristic of rotating machinery vibration signals of a rotor system with a loose pedestal fault, variational mode decomposition was applied in the pedestal looseness fault diagnosis for such a rotor system. Variational mode decomposition is used to decompose the rotor vibration signal into several stable components. This can achieve the separation of the pedestal looseness fault signal from the background signals, and extract the fault characteristic of a vibration signal from a rotor system with pedestal looseness. Experimental data from a rotor system wit

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An, Xueli, Hongtao Zeng, and Chaoshun Li. "Envelope demodulation based on variational mode decomposition for gear fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 4 (2016): 864–70. http://dx.doi.org/10.1177/0954408916644271.

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A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. Accor

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Miao, Qiuyan, Qingxin Shu, Bin Wu, Xinglin Sun, and Kaichen Song. "A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend." Sensors 22, no. 5 (2022): 1801. http://dx.doi.org/10.3390/s22051801.

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Complex variational mode decomposition (CVMD) has been proposed to extend the original variational mode decomposition (VMD) algorithm to analyze complex-valued data. Conventionally, CVMD divides complex-valued data into positive and negative frequency components using bandpass filters, which leads to difficulties in decomposing signals with the low-frequency trend. Moreover, both decomposition number parameters of positive and negative frequency components are required as prior knowledge in CVMD, which is difficult to satisfy in practice. This paper proposes a modified complex variational mode

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Thomas, Marc, Djamal Zarour, Salim Meziani, and Mourad Kedadouche. "Faulty bearing features by variational mode decomposition." Vibroengineering PROCEDIA 16 (December 17, 2017): 29–34. http://dx.doi.org/10.21595/vp.2017.19336.

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Baldini, Gianmarco, and Fausto Bonavitacola. "Channel identification with Improved Variational Mode Decomposition." Physical Communication 55 (December 2022): 101871. http://dx.doi.org/10.1016/j.phycom.2022.101871.

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Chen, Shiqian, Xingjian Dong, Zhike Peng, Wenming Zhang, and Guang Meng. "Nonlinear Chirp Mode Decomposition: A Variational Method." IEEE Transactions on Signal Processing 65, no. 22 (2017): 6024–37. http://dx.doi.org/10.1109/tsp.2017.2731300.

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Bian, Xihui, Zitong Shi, Yingjie Shao, Yuanyuan Chu, and Xiaoyao Tan. "Variational Mode Decomposition for Raman Spectral Denoising." Molecules 28, no. 17 (2023): 6406. http://dx.doi.org/10.3390/molecules28176406.

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As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two art

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Gao, Wenjia, Dan Liu, Qisong Wang, Yongping Zhao, and Jinwei Sun. "Narrowband constraint-enhanced successive variational mode decomposition." Biomedical Signal Processing and Control 104 (June 2025): 107396. https://doi.org/10.1016/j.bspc.2024.107396.

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Gui, Huazhan, Yifei Zhao, Ying Zhang, et al. "Fault diagnosis of flight control system based on BWO-VMD-DNCNN." Journal of Physics: Conference Series 2820, no. 1 (2024): 012072. http://dx.doi.org/10.1088/1742-6596/2820/1/012072.

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Abstract The flight control system of commercial aircraft is the core system of the aircraft and is directly related to whether the aircraft can fly safely. There are difficulties in fault diagnosis of flight control systems, such as noise and complex signals in the collected signals. This paper proposes a fault diagnosis method to solve this problem, using the Beluga Whale Optimization (BWO) and the denoise convolutional neural network (DNCNN) improved variational mode decomposition combined with the support vector machine optimized convolutional neural network. Firstly, the signal undergoes

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S, Rahul, and Sunitha R. "Dominant Electromechanical Oscillation Mode Identification using Modified Variational Mode Decomposition." Arabian Journal for Science and Engineering 46, no. 10 (2021): 10007–21. http://dx.doi.org/10.1007/s13369-021-05818-x.

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Isham, M. Firdaus, M. Salman Leong, M. Hee Lim, and Z. Asrar Ahmad. "Variational mode decomposition: mode determination method for rotating machinery diagnosis." Journal of Vibroengineering 20, no. 7 (2018): 2604–21. http://dx.doi.org/10.21595/jve.2018.19479.

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An, Xueli, and Luoping Pan. "Bearing fault diagnosis of a wind turbine based on variational mode decomposition and permutation entropy." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 2 (2017): 200–206. http://dx.doi.org/10.1177/1748006x17693492.

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Variational mode decomposition is a new signal decomposition method, which can process non-linear and non-stationary signals. It can overcome the problems of mode mixing and compensate for the shortcomings in empirical mode decomposition. Permutation entropy is a method which can detect the randomness and kinetic mutation behavior of a time series. It can be considered for use in fault diagnosis. The complexity of wind power generation systems means that the randomness and kinetic mutation behavior of their vibration signals are displayed at different scales. Multi-scale permutation entropy an

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Lu, Jing-Yi, Dong Ye, and Wen-Ping Ma. "Time delay estimation based on variational mode decomposition." Advances in Mechanical Engineering 9, no. 1 (2017): 168781401668858. http://dx.doi.org/10.1177/1687814016688587.

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In order to improve the time delay estimation of colored noise signals, this article proposes generalized cross-correlation time delay estimation based on variational mode decomposition. First of all, we put forward the signal energy detection criterion to extract the effective signal from the signal, which can reduce the amount of calculation and improve the real-time performance. Second, the effective signal is decomposed into a number of intrinsic mode functions using variational mode decomposition. The correlation coefficients of each intrinsic mode function and the original signal are cal

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Anisimov, Pavel S., Viacheslav V. Zemlyakov, and Jiexing Gao. "2D least-squares mode decomposition for mode division multiplexing." Optics Express 30, no. 6 (2022): 8804. http://dx.doi.org/10.1364/oe.449393.

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Hadiyoso, Sugondo, Inung Wijayanto, Achmad Rizal, and Suci Aulia. "Biometric systems based on ECG using ensemble empirical mode decomposition and Variational Mode decomposition." Journal of Applied Engineering Science 18, no. 2 (2020): 181–91. http://dx.doi.org/10.5937/jaes18-26041.

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Xia, Saiqiang, Jun Yang, Wanyong Cai, Chaowei Zhang, Liangfa Hua, and Zibo Zhou. "Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications." Sensors 21, no. 5 (2021): 1637. http://dx.doi.org/10.3390/s21051637.

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In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper. The CVMD is extended from variational mode decomposition (VMD), which is a recently introduced technique for adaptive signal decomposition, limited to only dealing with the real signal. Thus, the VMD is extended from the real domain to the complex domain firstly. Then, the optimal effective order of singular value is obtained by singular value decomposition (SVD) to solve the probl

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Ye, Maoyou, Xiaoan Yan, and Minping Jia. "Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM." Entropy 23, no. 6 (2021): 762. http://dx.doi.org/10.3390/e23060762.

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The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculat

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