An Adaptive Noise-Free Method of Seismic Resolution Enhancement Based on Extrapolated Multiresolution Singular Value Decomposition

Hu, Ying; Chen, Hui; Lu, Lingqi; Zhang, Huaibang; Marfurt, Kurt J.

doi:10.1109/tgrs.2020.2970732

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…( 9) Iterative search for the optimal solution using the Alternating Direction Multiplier algorithm. The iterative solution process updates the variables ûn+1 k and ω n+1 k through equations ( 13) and (14).…”

Section: Vmd-psementioning

confidence: 99%

“…This is achieved by decomposing the signal into approximate and detailed components with varying resolutions. The general and detailed features of the signal are then displayed at different levels in the form of multiresolution [14]. In the context of reducing noise in the vibration signal of a CB, the original signal can be decomposed into noise signals and gradually removed to achieve noise reduction.…”

Section: Mrsvd Algorithmmentioning

confidence: 99%

“…MRSVD is a signal processing method that effectively removes noise and retains useful information. However, the choice of parameters can affect its noise reduction effectiveness [14]. Therefore, in order to make up for the shortcomings of the above reference, this article proposes a parameter adaptive noise reduction method based on ICEEMDAN-MRSVD.…”

Section: Introductionmentioning

confidence: 99%

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Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

Yan,

Zhuang,

2024

Meas. Sci. Technol.

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The vibration signal associated with the operating process of circuit breakers(CBs) includes a detailed operating status in the formation of the operating mechanism. To effectively extract the characteristic information of vibration effectively for diagnosis and analysis, a new feature extraction method for the CBs operating mechanism is proposed. First, a new denoising method, the improved complete ensemble empirical mode decomposition with adaptive noise-multi-resolution singular value decomposition (ICEEMDAN-MRSVD), is introduced, which can effectively remove the influence of noise on faults. Then, a quantitative method is proposed to extract the characteristic information of the CB, i.e. the variational mode decomposition (VMD)-power spectrum entropy (PSE) is proposed. By using this method, the difference of CB vibration signals in different fault states can be quantified. Through comparative analysis of different recognition models, experiments show that the support vector machine model based on ICEEMDAN-MRSVD noise reduction and VMD-PSE features has a high recognition accuracy of 98.61%, which has high application value.

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Section: Vmd-psementioning

confidence: 99%

Section: Mrsvd Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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