Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network
Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network
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DOI：
10.1155/2015/846308
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发表时间：
2015-05
影响因子：
1.6
通讯作者：
Xiangxin Liu;Zhengzhao Liang;Yanbo Zhang;Xianzhen Wu;Zhiyi Liao
中科院分区：
文献类型：
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作者：
Xiangxin Liu;Zhengzhao Liang;Yanbo Zhang;Xianzhen Wu;Zhiyi Liao
不同类型的岩石产生不同频率和振幅的声发射信号。如何在现场监测中根据岩石的声发射特征来确定岩石类型，也有助于了解岩石的力学行为。不同类型的岩石标本（麻粒岩，花岗岩，石灰岩，粉砂岩）进行单轴压缩，直到失败，并记录其AE信号在其破裂过程中。采用小波变换对声发射信号进行分解，建立人工神经网络对岩石类型和噪声（人工敲击噪声和电噪声）进行识别。结果表明，不同岩石具有不同的破裂特征和声发射特征。小波变换为获取岩石声发射信号和环境噪声的能量谱、峰值频率等基本特征提供了强有力的手段，而人工神经网络是识别不同类型岩石声发射信号和环境噪声的有效方法。
Different types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limestone, and siltstone) were subjected to uniaxial compression until failure, and their AE signals were recorded during their fracturing process. The wavelet transform was used to decompose the AE signals, and the artificial neural network (ANN) was established to recognize the rock types and noise (artificial knock noise and electrical noise). The results show that different rocks had different rupture features and AE characteristics. The wavelet transform provided a powerful method to acquire the basic characteristics of the rock AE and the environmental noises, such as the energy spectrum and the peak frequency, and the ANN was proved to be a good method to recognize AE signals from different types of rocks and the environmental noises.