单轴压缩下煤岩失稳声发射时间序列预测研究

Study on prediction of acoustic emission time series of coal rock fracture under uniaxial compressive

为提高煤岩失稳声发射信号的预测精度,有效预防瓦斯突出,将主成分分析(PCA)和回声状态网络(ESN)相结合,建立了煤岩失稳声发射时间序列预测模型。利用单轴压缩声发射试验采集煤岩失稳声发射时间序列数据;采用PCA对声发射数据特征参数进行降维,提取了表征煤岩失稳破裂程度的3个主成分综合指标;利用小世界网络(SW)优化回声状态网络储备池的拓扑结构,降低池内神经元的耦合程度。将PCA提取的3个综合指标作为回声状态网络的输入,建立PCA-SWESN声发射时间序列预测模型,并与PCA-ESN方法进行对比试验。研究结果表明:与PCA-ESN方法相比,PCA-SWESN预测模型经过小世界网络优化后,降低了ESN的病态解,提高了煤岩失稳声发射信号的预测精度,为防治瓦斯突出灾害提供了理论依据。

Improving the accuracy of coal-rock fracture acoustic emission signal is crucial to prevent gas outburst hazards. In this paper,a coal rock fracture acoustic emission time series prediction model was built on the basis of Principal Component Analysis( PCA) and Echo State Network( ESN),namely PCA-ESN. The acoustic emission time series data of coal-rock instability was obtained using uniaxial compression acoustic emission tests. PCA was applied to reduce the dimensionality of acoustic emission data. The three principal components were extracted as comprehensive indicators of coal rock destabilization rupture degrees. Small-World network( SW) was used to optimize the topology of the Echo State Network reserve pool and to reduce coupling of neurons in the pool. Another acoustic emission time series prediction model,PCA-SWESN was established by using the three comprehensive indexes extracted by PCA as the input of the echo state network.Compared to the PCA-ESN model,the PCA-SWESN model reduced the ill-conditioned solution of the ESN,improved the prediction accuracy of the acoustic emission signal of coal-rock instability. The PCS-SWESN technique provides a theoretical basis for preventing gas disasters.