Wavelet basis construction method based on separation blast vibration signal

Ling, Tonghua; Zhang, Sheng; Chen, Qianqian; Li, Jie

doi:10.1007/s11771-015-2812-3

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…The empirical results have demonstrated that the sym7, sym8 [24], db6 [25], and db8 [26] wavelet basic functions are particularly effective in handling blast vibration signals The 'ddencmp' function in MATLAB provides default values for denoising or compression for the critically sampled discrete WD or WPD. The default threshold estimation methods encompass 'rigrsure', 'heursure', 'sqtwolog', 'minimaxi'.…”

Section: Noise Reductionmentioning

confidence: 99%

Study on the Equivalence Transformation between Blasting Vibration Velocity and Acceleration

Yu,

Wu,

et al. 2024

Sensors

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The evaluation of blasting vibrations primarily hinges on two physical quantities: velocity and acceleration. A significant challenge arises when attempting to reference the two types of vibration data in relation to one another, such as different types of seismometers, noise, etc., necessitating a method for their equivalent transformation. To address this, a transformation method is discussed in detail with a case study, and equations for the ratio (Ra) of the particle peak velocity (PPV) to the particle peak acceleration (PPA) are proposed. The findings are twofold: (1) The conventional data conversion processes often suffer from low accuracy due to the presence of trend terms and noise in the signal. To mitigate this, the built-in MATLAB function is used for trend term elimination, complemented by a combined approach that integrates CEEMDAN with WD/WDP for noise reduction. These significantly enhance the accuracy of the transformation. (2) This analysis reveals a positive power function correlation between Ra and the propagation distance of the blast vibrations, contrasted by a negative correlation with the maximum charge per delay. Intriguingly, the Ra values observed in pre-splitting blasting operations are consistently lower than those in bench blasting. The established Ra equations offer a rapid, direct method for assessing the transformation between the PPV and PPA, providing valuable insights for the optimization of blasting design.

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Section: Noise Reductionmentioning

confidence: 99%

Study on the Equivalence Transformation between Blasting Vibration Velocity and Acceleration

Yu,

Wu,

et al. 2024

Sensors

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“…The ability of WT to decompose and reconstruct signals through Wavelet functions enables the extraction of signal features at different scales, making it particularly effective for the identification of transient impulses [16]. Researchers have highlighted the importance of using appropriate wavelet bases in wavelet analysis, as different bases can lead to varying results when analyzing the same signal [17]. Studies have shown that wavelet analysis is highly suitable for detecting and analyzing transient disturbances, outperforming various other detection algorithms [4].…”

Section: Introductionmentioning

confidence: 99%

New Method for Transient Signal Analysis using the Continuous Wavelet Transform

Gomez-Luna,

Cuadros,

Candelo-Becerra

2024

Preprint

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This paper presents a new method for transient signal analysis in the frequency domain based on the continuous wavelet transform (CWT). The study involved testing signals obtained from an electronic switch that considers opening and closing operations. The source is connected to inductive, resistive, and capacitive loads. The resonance behaviors were analyzed, obtaining the result that the signal processing performed by CWT is better for spectrum analysis than the Fourier Transform (FT). The results show that the CWT achieves greater reliability, repeatability, high noise attenuation, and softness of the analyzed spectrum. This study shows the potential of WT for analyzing transient signals, specifically in the connection and disconnection of loads.

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“…Many scholars have also proposed improved methods related to EMD and WT to adapt to the complexity of signal and the diversity of applications, such as ensemble empirical mode decomposition (EEMD) [21] and variational mode decomposition (VMD) [16,22]. There are many ways to improve wavelet denoising methods, such as improving the threshold function to solve the problem that reconstruction signal may oscillate or distort that caused by traditional threshold functions [23][24][25][26][27][28][29][30][31], setting adaptive threshold [32,33], finding the optimal wavelet basis [34][35][36], and setting self-adaptive wavelet decomposition level [15]; some scholars combine EMD and WT [37].…”

Section: Introductionmentioning

confidence: 99%

Combined EEMD with a Novel Flexible Wavelet Threshold Function for Weighing Signal Denoising Approach

et al. 2022

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This paper proposes a method of combining ensemble empirical mode decomposition (EEMD) with a novel flexible wavelet threshold function to reduce the random error in the weighing result that caused by the nonstationary and nonlinear noise in quality characteristic parameter measurement equipment. The original signal is first processed by EEMD, and then, all intrinsic mode functions (IMFs) are processed by a novel flexible threshold function proposed by this paper. Finally, the denoised signal is obtained by adding reconstructed IMFs and residual. Through theoretical analysis, the proposed threshold function can retain more useful information and have continuity at the segmentation point. Moreover, the addition of adjustable parameters makes it more adaptable. Its advantages are verified by comparing the denoised results with other threshold functions in the simulation model. In weighing experiment, the validity of the novel flexible threshold function in weighing signal denoising is verified, and the effectiveness of the proposed method is also quantitatively confirmed by comparing the random errors of the original signal and the denoised signal.

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Wavelet basis construction method based on separation blast vibration signal

Cited by 7 publications

References 26 publications

Study on the Equivalence Transformation between Blasting Vibration Velocity and Acceleration

Study on the Equivalence Transformation between Blasting Vibration Velocity and Acceleration

New Method for Transient Signal Analysis using the Continuous Wavelet Transform

Combined EEMD with a Novel Flexible Wavelet Threshold Function for Weighing Signal Denoising Approach

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