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Shock and Vibration
Volume 2015, Article ID 846308, 14 pages
Research Article

Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network

1College of Mining Engineering, Hebei United University, Tangshan, Hebei 063009, China
2School of Civil Engineering, Dalian University of Technology, Dalian 116024, China
3School of Resources and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China

Received 7 August 2014; Revised 26 October 2014; Accepted 28 October 2014

Academic Editor: Ting-Hua Yi

Copyright © 2015 Xiangxin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.