不同岩石脆性破坏声发射时频特性及信号识别  

Time-frequency characteristic and signal recognition of acoustic emission generated from different rock brittle failure

作　　者：刘建伟[1] 吴贤振[1] 刘祥鑫[2,3] 喻圆圆[1] 胡维[1] 尹丽冰 

机构地区：[1]江西理工大学资源与环境工程学院,江西赣州341000 [2]河北联合大学矿业工程学院,河北唐山063009 [3]河北联合大学河北省矿业开发与安全技术重点实验室,河北唐山063009

出　　处：《有色金属科学与工程》2013年第6期73-77,共5页Nonferrous Metals Science and Engineering

基　　金：国家自然科学基金资助项目(51174071);江西省教育厅科技计划项目(GJJ12336);江西省教育厅青年基金项目(GJJ12364);河北联合大学青年基金资助项目(z201203);江西理工大学研究生创新专项资金项目(YC2012-X05)

摘　　要：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 END and Welch spectrum; a high recognition rate can be reached by neural network with various characteristics of different rock acoustic emission.

关 键 词：岩石力学 声发射 Welch谱 EMD能量熵 BP神经网络 信号识别 

分 类 号：TD326[矿业工程—矿井建设]