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

Zhang, Weifang; Zhang, Meng; Zhao, Yan; Jin, Bo; Dai, Wei

doi:10.3390/app9010180

Cited by 15 publications

(8 citation statements)

References 26 publications

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“…Then, the first six IMF components are respectively subjected to sparse transformation to obtain coefficients to be reconstructed θ = {θ1, θ2, ..., θ6}. The threshold T is calculated according to the algorithm of Section 2.3, and the calculation result is shown in Figure 7, and soft-thresholding filtering is performed on the reconstruction To further verify the effectiveness and feasibility of the proposed method, this paper compares with traditional discrete wavelet transform (DWT) denoising, EEMD denoising, and VMD_DWT [37] denoising methods. The simulation experiments of these denoising methods are carried out on the MATLAB platform.…”

Section: Emd_cs_st Algorithm Applicationmentioning

confidence: 99%

“…These results are shown in Table 1. To further verify the effectiveness and feasibility of the proposed method, this paper compares with traditional discrete wavelet transform (DWT) denoising, EEMD denoising, and VMD_DWT [37] denoising methods. The simulation experiments of these denoising methods are carried out on the MATLAB platform.…”

Section: Emd_cs_st Algorithm Applicationmentioning

confidence: 99%

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Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

Dong

et al. 2020

Applied Sciences

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Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST) combined EMD_CS_ST denoising method is proposed. First, through EMD decomposition of the noise signal, a series of Intrinsic Mode Functions (IMF) from high frequency to low frequency are obtained. By calculating the correlation coefficient between each IMF and the original signal, the boundary component between the signal and the noise was identified, and the boundary component and its previous components were sparsely processed in the discrete wavelet transform domain to obtain the original sparse coefficient θ. Second, θ is filtered by ST to get the reconstruction coefficient θnew after denoising. Then, CS was used to recover and reconstruct θnew to get the denoised IMFnew component and then recombined with the remaining IMF components to get the signal after denoising. In the simulation experiment, the denoising process of EMD_CS_ST method is introduced in detail, and the denoising ability of EMD_CS_ST, DWT, EEMD, and VMD_DWT under 10 different noise levels is discussed. The signal-to-noise ratio, signal standard deviation, correlation coefficient, waveform diagram, and spectrogram before and after denoising are compared and analyzed. Moreover, the signals obtained from the underground cavern of the Shuangjiangkou hydropower station were denoised by the EMD_CS_ST method, and the signals before and after denoising were analyzed by time-frequency spectrum. These results show that the proposed method has better denoising ability.

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Section: Emd_cs_st Algorithm Applicationmentioning

confidence: 99%

Section: Emd_cs_st Algorithm Applicationmentioning

confidence: 99%

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

Dong

et al. 2020

Applied Sciences

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“…Also, the wavelet transform has the advantages of low entropy, multi-resolution, wavelet base selection diversity, and decorrelation. In general, a one-dimensional noisy signal can be expressed as follows (Xu et al., 2016; Zhang et al., 2019) where gi is the noisy original signal, fi is the real signal, ei is the noise signal, and λ is the noise level. The process of wavelet denoising is as follows: The original signal is resolved into a high-frequency part and a low-frequency part by wavelet transform.…”

Section: Preprocessing Of Experimental Datamentioning

confidence: 99%

Section: Preprocessing Of Experimental Datamentioning

confidence: 99%

Life prediction of lithium-ion battery based on a hybrid model

Chen

Wun

Wang

et al. 2020

Energy Exploration & Exploitation

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Lithium battery is a new energy equipment. Because of its long service life and high energy density, it is widely used in various industries. However, as the number of uses increases, the life of the energy battery gradually decreases. Aging of battery will bring security risks to energy storage system. Through the life prediction of energy lithium battery, the health status of energy battery is assessed, so as to improve the safety of energy storage system. Therefore, a hybrid model is proposed to predict the life of the energy lithium battery. The lithium-ion battery capacity data are always divided into two scales, which are predicted by extreme learning machine and support vector machine model. The energy lithium-ion battery capacity attenuation data were obtained through experiments. The original signal is decomposed into five layers by using the wavelet basis function to denoise the signal. Finally, the denoised signal is synthesized. The noise reduction effect of each wavelet was analyzed. The analysis results show that the mean square error value of the Haar wavelet is 5.31e-28, which indicates that the Haar wavelet has the best noise reduction effect. Finally, the combined model was tested by using two sets of experiments. The prediction results of the combined model are compared with those of the single model. The test results show that the prediction results of the combined model are better than the single model for either experiment 1 or experiment 2. Experiment 1 indicated the root mean square error values are 29.58 and 79.68% smaller than the root mean square error values of extreme learning machine and support vector machine. The model proposed in this study has positive significance for the safety improvement of energy storage system and can promote the development and utilization of energy resources.

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“…Based on the previous research [ 37 ], the process of the VMD-DWT signal de-noising algorithm is described as follows:…”

Section: Central Wavelength Detection Algorithmmentioning

confidence: 99%

The Analysis of FBG Central Wavelength Variation with Crack Propagation Based on a Self-Adaptive Multi-Peak Detection Algorithm

Zhang

Wang

et al. 2019

Sensors

Self Cite

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We propose a peak seeking algorithm to extract the damage characteristic-variation of central wavelength to monitor the crack damage status in aluminum alloy plates using surface bonded fiber Bragg grating (FBG) sensors. The FBG sensors are sensitive to the uniform and non-uniform strain distribution along their longitudinal direction, and the effect appears in the power spectrum of the reflected light from the gauge section. In this paper, we propose a fast-self-adaptive multi-peak seeking algorithm to detect the central wavelength shifting of the FBG reflection spectrum with the crack propagation. The proposed peak searching algorithm results point to a significant improvement compared to other conventional methods. Then the central wavelength shifting is applied to explain the crack propagation behavior of the aluminum plates under quasi-static tensile test conditions. The different damages feature changing intervals which are associated with the crack position and the FBGs location, demonstrating that central wavelength shifting performs as an indicator to detect structural crack damage.

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

Cited by 15 publications

References 26 publications

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

Life prediction of lithium-ion battery based on a hybrid model

The Analysis of FBG Central Wavelength Variation with Crack Propagation Based on a Self-Adaptive Multi-Peak Detection Algorithm

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