Vibration feature extraction based on the improved variational mode decomposition and singular spectrum analysis combination algorithm

2018 ◽  
Vol 22 (7) ◽  
pp. 1519-1530 ◽  
Author(s):  
Hui Li ◽  
Tengfei Bao ◽  
Chongshi Gu ◽  
Bo Chen
Keyword(s):  
Spectrum Analysis ◽  
Damage Identification ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Low Frequency Noise ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Discharge Structure

Extraction of the vibration characteristics of a flood discharge structure under the influence of intensive background noise is one of the main challenges in vibration-based damage identification. A novel algorithm called normalized central frequency difference spectrum is proposed to improve the variational mode decomposition algorithm for high-frequency noise filtering. To eliminate the errors caused by end effect, the waveform matching extension algorithm is used to further improve the variational mode decomposition. However, the vibration signal is still coupled in low-frequency noise. Thereupon, the singular spectrum analysis algorithm is applied to filter the low-frequency noise. In this article, a simulated signal and the measured signals from a dam model are analyzed by the proposed algorithm. The results indicate that the proposed algorithm is robust to noise and has high denoising precision. In addition, this algorithm can offer clues for damage identification and localization of a flood discharge structure.

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.

The low-frequency noise spectrum analysis of the reliability of the InGaN LED

2013 ◽  
Author(s):  
Tzung-Te Chen ◽  
Chun-Fan Dai ◽  
Chun-Wen Chu ◽  
Han-Kuei Fu ◽  
Chien-Ping Wang ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Low Frequency ◽  
Noise Spectrum ◽  
Frequency Noise ◽  
Low Frequency Noise

Improved variational mode decomposition method for vibration signal processing of flood discharge structure

2021 ◽  
pp. 107754632110161
Author(s):  
Huokun Li ◽  
Gang Wang ◽  
Bowen Wei ◽  
Hanyue Liu ◽  
Wei Huang
Keyword(s):  
High Frequency ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Structural Vibration ◽  
Variational Mode Decomposition ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Discharge Structure ◽  
Frequency Components ◽  
Structure Vibration

It is crucial for flood discharge structure vibration safety evaluations to filter low-frequency noise, separate dense-frequency components and obtain high-frequency component accurately from vibration signals. Variational mode decomposition, a novel signal adaptive decomposition method, effectively processes flood discharge structures. However, the mode number and quadratic penalty item uncertainty in variational mode decomposition directly affects the vibration signal decomposition. Therefore, an improved variational mode decomposition method for vibration signal processing is proposed in this study. The proposed method adaptively determines the mode number based on singular entropy and frequency stability to completely separate the structural vibration components (including dense-frequency components and high-frequency components) and noise components from the vibration signal. Next, an objective quadratic penalty item function based on sample entropy and mutual information is proposed to quantify the mode mixing between the structural vibration components. Finally, a particle swarm optimisation algorithm based on beetle antenna search is proposed to optimise the quadratic penalty item, which overcomes the shortcomings of traditional algorithms and suppresses the mode mixing between the structural vibration components. The validity and feasibility of the proposed method was verified by the simulation signal and was applied to a sluice prototype project. The results showed that the method effectively filtered noise, greatly improved the vibration response signal-to-noise ratio and obtained the structural vibration component time history signal, which provides a foundation for flood discharge structure vibration safety evaluation and health monitoring.

scholarly journals Desert seismic signal denoising by 2D compact variational mode decomposition

2019 ◽  
Vol 16 (6) ◽  
pp. 1048-1060 ◽  
Author(s):  
Yue Li ◽  
Linlin Li ◽  
Chao Zhang
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Low Frequency ◽  
Seismic Signal ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Support Functions ◽  
Noise Characteristics ◽  
Mode Decomposition ◽  
Effective Signal

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 seismic data into an finite number of intrinsic mode functions with specific directions and vibration characteristics. The algorithm introduces binary support functions, which can detect the edge region of the signal in each mode by penalizing the support function through the L1 and total variation (TV) norm. Finally, the signal can be reconstructed by the support functions and the decomposed modes. We apply the 2D-CVMD algorithm to synthetic and real seismic data. The results show that the 2D-CVMD algorithm can not only suppress desert low-frequency noise, but also recover the weak effective signal.

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