基于频谱分析和卷积神经网络的岩石声发射信号定位研究

doi:10.13722/j.cnki.jrme.2021.0836

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Abstract As we all known，how to accurately locate the source position is of great significance in acoustic emission testing. The traditional AE localization method has some problems，such as wave velocity model and inaccurate on arrival-time picking，which brings a low localization accuracy. Therefore，a new localization method that combining spectrum analysis and convolutional neural network was developed，which don?t need the wave velocity model and the arrival-time picking for acoustic emission localization of rock. To be more specific，based on the acoustic emission full waveform signals collected by using active source acoustic emission test，we got spectral distribution of acoustic emission signals on each surface of cuboid granite sample using wavelet analysis method firstly. Then surface source positioning of acoustic emission signals was carried out，according to the spectral difference of acoustic emission signals，which cleverly transformed and the complex three-dimensional spatial positioning into two-dimensional plane positioning. At the same time，a convolutional neural network designed to transform the acoustic emission signals of the broken lead points on each surface using the short time Fourier transform. Finally，we taken the contains both time domain and frequency domain information of the signal spectrum characteristics as the convolution of the neural network input，and the two-dimensional space coordinates of the break point as the output features to train，test and verified the two-dimensional localization results of acoustic emission sources of lead points on each surface progressively. The results show that the average location errors of surface XOZ－(which attaches four transducers)，YOZ+，YOZ－，XOZ+ are 0.5 cm，0.7 cm，0.7 and 1.0 cm，respectively. To sum up，this study significantly improves the AE localization accuracy and avoids the shortcomings of traditional localization methods，which provides a new idea for rock AE localization.

Key words： rock mechanics acoustic emission source localization convolutional neural networks spectral analysis

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	CHEN Jie1
	2
	CHEN Ziyang1
	2
	PU Yuanyuan1
	2

Cite this article:

CHEN Jie1,2,CHEN Ziyang1, et al. Acoustic emission source localization in rocks based on spectral analysis and convolutional neural network[J]. , 2022, 41(S2): 3271-3281.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2021.0836 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/IS2/3271

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