Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure
Source abstract
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.
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Research context
OpenAlex topics: Rock Mechanics and Modeling · Earthquake Detection and Analysis · Geoscience and Mining Technology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology (Beijing), Beijing 100083, China
School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083, China
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Turin, Italy
Funding
National Natural Science Foundation of China
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