Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

Hu, Hongping; Ao, Yan; Yan, Huichao; Bai, Yanping; Shi, Na

doi:10.1155/2021/5599096

Cited by 24 publications

(10 citation statements)

References 36 publications

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“…Yu et al 18 determined the number of decomposition layers of VMD by using permutation entropy and verified the superiority of this method based on the simulated and actual vibration signals. Hu et al 19 constructed an improved optimization algorithm, and computed the root mean square error as the fitness function to obtain the number of decomposition layers and the optimal penalty factor.…”

Section: Introductionmentioning

confidence: 99%

Denoising method of machine tool vibration signal based on variational mode decomposition and Whale-Tabu optimization algorithm

Fang

Chen

Deng

et al. 2023

Sci Rep

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The noise from other sources is inevitably mixed in the vibration information of CNC machine tools obtained using the sensors. In this work, a de-noising method based on joint analysis is proposed. The variational mode decomposition (VMD), correlation analysis (CA), and wavelet threshold (WT) denoising are used to denoise the original signal. First, VMD decomposes noisy signals into multiple intrinsic mode functions (IMFs). The penalty factor and decomposition level of VMD parameters are selected by the optimization algorithm by combining the whale optimization algorithm (WOA) and tabu search (TS). The minimum permutation entropy of IMF is used as the fitness function of the proposed fusion algorithm. Then, the IMF is divided into three categories by using the cross-correlation number. They include the pure components, signals containing noise, and complete noise components. Then, the WT method is used to further denoise the signals, and signal reconstruction is performed with the pure component to obtain the denoised signal. This joint analysis denoising method is named TS-WOA-VMD-CA-WT. The simulation results show that the fusion optimization algorithm proposed in this work has better performance as compared to the single optimization algorithm. It performs effectively when applied to the actual machine tool vibration signal denoising. Therefore, the proposed TS-WOA-VMD-CA-WT method is superior to other existing denoising techniques and has good generality, which is expected to be popularized and applied more widely.

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Section: Introductionmentioning

confidence: 99%

Denoising method of machine tool vibration signal based on variational mode decomposition and Whale-Tabu optimization algorithm

Fang

Chen

Deng

et al. 2023

Sci Rep

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show abstract

“…In order to establish a SVM model with more accurate prediction accuracy and suppress the high-frequency signal containing noise, that is, suppress the value of noise signal r ( a ) and recover the real signal e ( a ) in y ( a ) , then the WD algorithm is introduced. In recent years, the most common denoising method is to directly denoise the noisy signal through a single filter, but this method has some limitations for the processing of non-stationary signals (Hu et al, 2021a). To solve this problem, wavelet threshold is introduced for denoising, which can effectively remove the noise signal in the wind power data.…”

Section: Wd Processing Of Wind Powermentioning

confidence: 99%

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

Lian

2021

Wind Engineering

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The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

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“…With the deepening of denoising research, for the purpose of improving the performance of the single denoising algorithm, some scholars try to combine a variety of algorithms and have achieved specific research results [11,12]. Hu et al pointed out that the combination of WT and the optimized VMD is able to perform the signal denoising and overcome the disadvantages of VMD and WT, whose performance is superior to the two individual methods [13]. In addition, Hua et al proposed a denoising method to deal with the laser radar echo signal denoising problem based on the concept of joint denoising [14], in which they optimized the VMD parameters, including the number of decomposition modes and the quadratic penalty.…”

Section: Introductionmentioning

confidence: 99%

New Denoising Method for Lidar Signal by the WT-VMD Joint Algorithm

Wang

Ding

Wang

et al. 2022

Sensors

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Light detection and ranging (LIDAR) is an active remote sensing system. Lidar echo signal is non-linear and non-stationary, which is often accompanied by various noises. In order to filter out the noise and extract valid signal information, a suitable method should be chosen for noise reduction. Some denoising methods are commonly used, such as the wavelet transform (WT), the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and their improved algorithms. In this paper, a new denoising method named the WT-VMD joint algorithm based on the sparrow search algorithm (SSA), for lidar signal is selected by comparative experiment analysis. It is shown that this method is the most suitable one with the maximum signal-to-noise ratio (SNR), the minimum root-mean-square error (RMSE), and a relatively small indicator of smoothness when it is used in three kinds (50, 100, and 1000 pulses) of simulate lidar signals. The SNR is increased by 138.5%, 77.8% and 42.8% and the RMSE is decreased by 81.8%, 72.0% and 68.8% when being used to the three kinds of cumulative signal without pollution. Then, the SNR is increased by 83.3%, 60.4% and 24.0% and the RMSE is decreased by 70.8%, 66.0% and 50.5% when being used to the three kinds of cumulative signal with aerosol and clouds. The WT-VMD joint algorithm based on SSA is used in the denoising process for the actual lidar signal, showing extraordinary denoising effect and will improve the inversion accuracy of the lidar signal.

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Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

Cited by 24 publications

References 36 publications

Denoising method of machine tool vibration signal based on variational mode decomposition and Whale-Tabu optimization algorithm

Denoising method of machine tool vibration signal based on variational mode decomposition and Whale-Tabu optimization algorithm

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

New Denoising Method for Lidar Signal by the WT-VMD Joint Algorithm

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