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. 2022 Apr 2;22(7):2746.
doi: 10.3390/s22072746.

Study on Multiple Fractal Analysis and Response Characteristics of Acoustic Emission Signals from Goaf Rock Bodies

Xuebin Xie  1 , Shaoqian Li  1 , Jiang Guo  1
Affiliations

Study on Multiple Fractal Analysis and Response Characteristics of Acoustic Emission Signals from Goaf Rock Bodies

Xuebin Xie et al. Sensors (Basel). .

Abstract

Based on the actual monitoring data of the acoustic emission (AE) ground pressure monitoring and positioning system, this paper introduces fractal theory and the multifractal detrended fluctuation analysis (MF-DFA) method to estimate the waveform multifractal spectrum of goaf rock acoustic emission signals and investigate multifractal time-varying response characteristics. The research results show that the wavelet hard thresholding method has the best noise reduction effect on the original signal, and the box counting dimension has a strong waveform identification effect. Before deformation damage occurs, fractal spectral width Δα shows an increase and then decrease and the fluctuation scale factor Δf(α) decreases and then increases. When damage occurs, fractal spectral width Δα decreases and then stabilizes and concentrates. Simultaneously, the fluctuation scale factor Δf(α) keeps decreasing until the lowest point, and then shows an increasing trend until it reaches a stable state. This study is of great significance to the stability evaluation and disaster early warning of mine goaf.

Keywords: acoustic emission monitoring; fractal theory; mine goaf; multiple fractals; response characteristics; wavelet noise reduction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Framework diagram of mine acoustic emission remote monitoring system. (−120 m represents 120 m below the local datum level of the mine and −270 m represents 270 m below the local datum level of the mine.).
Figure 2
Figure 2
Line installation and layout. (a) Optical fiber transmission line erection; (b) installation of monitoring probe.
Figure 3
Figure 3
Underground instability and failure phenomena of the mine. (a) Underground surrounding rock caving and caving; (b)—70 m horizontal 306 stope point column falling across; (c)—120 middle section 410 and 412 stope column rock mass cracking.
Figure 4
Figure 4
Basic flow of wavelet denoising method.
Figure 5
Figure 5
MF-DFA calculation flow chart.
Figure 6
Figure 6
Acoustic emission original waveform of signal A and wavelet denoising.
Figure 7
Figure 7
Evaluating indicator performance comparisons with different noise reduction methods: (a) SNR, (b) RMSE.
Figure 8
Figure 8
Four typical acoustic emission signal waveforms. (a) Blasting operation waveform, (b) shovel operation waveform, (c) rock drilling operation waveform and (d) surrounding rock body waveform.
Figure 8
Figure 8
Four typical acoustic emission signal waveforms. (a) Blasting operation waveform, (b) shovel operation waveform, (c) rock drilling operation waveform and (d) surrounding rock body waveform.
Figure 9
Figure 9
The fractal dimension trend of four waveforms: (a) Hausdorff dimension, (b) box counting dimension.
Figure 10
Figure 10
Double logarithm fitting trend of q-order wave function Fq(s)-s.
Figure 11
Figure 11
Generalized Hurst exponent (a) and scaling function (b).
Figure 12
Figure 12
Multifractal spectrum of typical acoustic emission signals.
Figure 13
Figure 13
Time varying response law of multifractal characteristic quantity: (a) parameter Δα, (b) parameter Δf(α).
Figure 14
Figure 14
Multifractal development and variation law of rock mass in goaf.
See this image and copyright information in PMC

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