scholarly journals Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223482-223494
Author(s):  
Zhenxing Liu ◽  
Jianhua Chang ◽  
Hongxu Li ◽  
Luyao Zhang ◽  
Sicheng Chen
Keyword(s):  
Machine Learning ◽  
Online Optimization ◽  
Signal Denoising ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mode Decomposition

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

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.

An adaptive seismic signal denoising method based on variational mode decomposition

Measurement ◽  
2021 ◽  
pp. 109277
Author(s):  
Xinyi Yao ◽  
Qiuzhan Zhou ◽  
Cong Wang ◽  
Jikang Hu ◽  
Pingping Liu
Keyword(s):  
Seismic Signal ◽  
Signal Denoising ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mode Decomposition

scholarly journals Research on Novel Denoising Method of Variational Mode Decomposition in MEMS Gyroscope

2021 ◽  
Vol 21 (1) ◽  
pp. 19-24
Author(s):  
Xiaolei Wang ◽  
Huiliang Cao ◽  
Yuzhao Jiao ◽  
Taishan Lou ◽  
Guoqiang Ding ◽  
...  
Keyword(s):  
Resonant Frequency ◽  
Reduction Method ◽  
Noise Signal ◽  
Sampling Frequency ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mems Gyroscope ◽  
Frequency Level ◽  
Mode Decomposition ◽  
Four Levels

Abstract The noise signal in the gyroscope is divided into four levels: sampling frequency level, device bandwidth frequency level, resonant frequency level, and carrier frequency level. In this paper, the signal in the dual-mass MEMS gyroscope is analyzed. Based on the variational mode decomposition (VMD) algorithm, a novel dual-mass MEMS gyroscope noise reduction method is proposed. The VMD method with different four-level center frequencies is used to process the original output signal of the MEMS gyroscope, and the results are analyzed by the Allan analysis of variance, which shows that the ARW of the gyroscope is increased from 1.998*10−1°/√h to 1.552*10−4°/√h, BS increased from 2.5261°/h to 0.0093°/h.

scholarly journals A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 597 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Technology ◽  
Signal Denoising ◽  
Frequency Noise ◽  
Denoising Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Adaptive Noise

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen’s chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.

Operation feature extraction of flood discharge structure based on improved variational mode decomposition and variance dedication rate

2019 ◽  
Vol 26 (3-4) ◽  
pp. 229-240
Author(s):  
Jianwei Zhang ◽  
Ge Hou ◽  
Han Wang ◽  
Yu Zhao ◽  
Jinlin Huang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Discharge Structure ◽  
Operation Characteristic ◽  
Rate Method

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 decomposition, improved variational mode decomposition can effectively determine the decomposition mode number of variational mode decomposition by using the mutual information method. Furthermore, improved variational mode decomposition is combined with a variance dedication rate to extract the overall operation characteristic information of the structure. In order to evaluate the applicability and effectiveness of the proposed improved variational mode decomposition–variance dedication rate method, we compare the denoising results of simulation signals produced by an improved variational mode decomposition–variance dedication rate with those produced by digital filter, wavelet threshold, empirical mode decomposition, empirical wavelet transform, complete ensemble empirical mode decomposition with adaptive noise, and improved variational mode decomposition methods and find a better performance of the improved variational mode decomposition–variance dedication rate method. In addition, the proposed method is applied to the Three Gorges Dam, and the results show that the improved variational mode decomposition–variance dedication rate method can effectively remove heavy background noises and extract the operation characteristic information of the flood discharge structure, which contributes to health monitoring and damage identification of the flood discharge structure.

scholarly journals Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding

2019 ◽  
Vol 9 (1) ◽  
pp. 180 ◽  
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.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

Sign in / Sign up

Export Citation Format

Share Document