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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