Xie Xuebin, Li Shaoqian, Guo Jiang
School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
Sensors (Basel). 2022 Apr 2;22(7):2746. doi: 10.3390/s22072746.
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 Δ shows an increase and then decrease and the fluctuation scale factor Δ() decreases and then increases. When damage occurs, fractal spectral width Δ decreases and then stabilizes and concentrates. Simultaneously, the fluctuation scale factor Δ() 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.
基于声发射(AE)地压监测与定位系统的实际监测数据,本文引入分形理论和多重分形去趋势波动分析(MF-DFA)方法,以估计采空区岩石声发射信号的波形多重分形谱,并研究多重分形时变响应特征。研究结果表明,小波硬阈值法对原始信号的降噪效果最佳,盒维数具有较强的波形识别效果。在变形破坏发生前,分形谱宽度Δ呈先增大后减小的趋势,波动尺度因子Δ()呈先减小后增大的趋势。当破坏发生时,分形谱宽度Δ先减小后稳定并集中。同时,波动尺度因子Δ()持续减小至最低点,然后呈上升趋势直至达到稳定状态。本研究对矿山采空区的稳定性评价和灾害预警具有重要意义。