针对不同岩石脆性破裂声发射信号的非稳定性等特点，提出了声发射参数、Welch谱、EMD和BP 神经网络相结合的声发射信号特征提取及识别方法。通过对3类脆性岩石进行单轴压缩声发射试验，获取了岩石破裂全过程的力学、声发射参数及波形；对各类岩石的声发射信号的时频特征进行了对比分析；综合声发射参数、峰值频率及EMD能量熵等特征向量，运用BP神经网络对岩石声发射及干扰源信号进行模式识别。结果表明，不同岩石在单轴加载下声发射参数随应力或时间的演化特征存在异同；EMD与Welch谱可很好体现出不同岩石声发射信号频谱与能量分布的特征差异；不同岩石声发射多种特征的神经网络具有良好的识别效果。%Considering the instability of acoustic emission (AE) signals of different rock fracture, the method for feature extraction and comprehensive recognition of AE is put forward combining with AE parameters, Welch spectrum, EMD and BP neural network. Through the acoustic emission experiment of three different brittle rocks under uniaxial compression, stress-strain curve and AE data are obtained. Comparative analysis is carried out towards the time-frequency characteristics of AE signal of rock samples. Feature vectors, such as AE parameters, Welch spectrum, and EMD energy entropy, are integrated with BP neural network to recognize different AE signal patterns. The results show that there are similarities and differences in characteristic evolving with stress or time of AE parameters of different rocks under uniaxial compression; characteristic differences of AE spectrum and energy distribution of different rocks can be well reflected from EMD and Welch spectrum; a high recognition rate can be reached by neural network with various characteristics of different rock acoustic emission.
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