Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure

Jing, Gang; Zhao, Yixin; Gao, Yirui; Marin Montanari, Pedro; Lacidogna, Giuseppe

doi:10.3390/app13169140

Open AccessArticle

Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure

by

Gang Jing

^1,2

,

Yixin Zhao

^1,2,3,*,

Yirui Gao

^1,2,

Pedro Marin Montanari

⁴

and

Giuseppe Lacidogna

^4,*

¹

Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology (Beijing), Beijing 100083, China

²

School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

³

State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083, China

⁴

Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Turin, Italy

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2023, 13(16), 9140; https://doi.org/10.3390/app13169140

Submission received: 11 July 2023 / Revised: 7 August 2023 / Accepted: 8 August 2023 / Published: 10 August 2023

(This article belongs to the Special Issue Acoustics and Vibrations Analyses of Materials at Different Scales: Experimental and Numerical Approaches: Volume II)

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This study proposes an enhanced variational mode decomposition method using the beetle antennae search optimizer algorithm. Simulation signal analysis demonstrates the effectiveness and superiority of the proposed method. Additionally, the study highlights that the multifractal parameters of the acoustic emission frequency-domain spectrum can serve as a reference for the early warning of coal damage and instability.

Abstract

Acoustic emission (AE) signal processing and interpretation are essential in mining engineering to acquire source information about AE events. However, AE signals obtained from coal mine monitoring systems often contain nonlinear noise, limiting the effectiveness of conventional analysis methods. To address this issue, a novel denoising approach using enhanced variational mode decomposition (VMD) and fuzzy entropy is proposed in this study. The denoised AE signal’s spectral multifractal features are analyzed. The optimization algorithm based on VMD with a weighted frequency index is introduced to avoid mode mixing and outperform other decomposition methods. The characteristic parameter Δα of the AE spectral multifractal parameter serves as an early warning indicator of coal instability. These findings contribute to the accurate extraction of time–frequency features and provide insights for on-site AE signal processing.

Keywords: acoustic emission; signal denoising; variational mode decomposition; multifractal

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MDPI and ACS Style

Jing, G.; Zhao, Y.; Gao, Y.; Marin Montanari, P.; Lacidogna, G. Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure. Appl. Sci. 2023, 13, 9140. https://doi.org/10.3390/app13169140

AMA Style

Jing G, Zhao Y, Gao Y, Marin Montanari P, Lacidogna G. Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure. Applied Sciences. 2023; 13(16):9140. https://doi.org/10.3390/app13169140

Chicago/Turabian Style

Jing, Gang, Yixin Zhao, Yirui Gao, Pedro Marin Montanari, and Giuseppe Lacidogna. 2023. "Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure" Applied Sciences 13, no. 16: 9140. https://doi.org/10.3390/app13169140

APA Style

Jing, G., Zhao, Y., Gao, Y., Marin Montanari, P., & Lacidogna, G. (2023). Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure. Applied Sciences, 13(16), 9140. https://doi.org/10.3390/app13169140

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure

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