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Study on Spectrum Characteristics and Clustering of Acoustic Emission Signals from Rock Fracture

  • Yanbo Zhang
  • Wenrui WuEmail author
  • Xulong Yao
  • Peng Liang
  • Lin Sun
  • Xiangxin Liu
Article
  • 16 Downloads

Abstract

Acoustic emission signals are relevant to the process of rock failure. In this paper, acoustic emission waveform signals during rock loading are acquired through uniaxial compression test of granite in laboratory. Short-time Fourier transform is used to analyze the acoustic emission signals during rock fracture to obtain the peak frequency. Based on the peak frequency of acoustic emission, four types of acoustic emission signals are classified by using fuzzy C-means method. The parameters of different types of acoustic emission signals are analyzed, which include ring count, duration, amplitude and energy. Meanwhile, the progressive propagation of surface cracks in rock specimens is quantitatively analyzed by digital image correlation (DIC) technology. The result shows that different types of acoustic emission signals correspond to different strength of rock fracture. Before rock fracture, high-count, long-duration and high-energy precursory characteristic signals appear intensively. The event density \( {\text{Den}}_{t} = 1 \) is taken as the early warning threshold of rock fracture through the quantitative analysis of acoustic emission signal. The present results of the research show the usefulness of the DIC and acoustic emission techniques in experiment of that type.

Keywords

Acoustic emission (AE) Fuzzy C-means (FCM) Time–frequency analysis Rock fracture 

Notes

Acknowledgements

The support of Chinese Natural Science Foundation (Nos. 51574102, 51774138, 51804122) and Hebei Natural Science Foundation Project (No. E2017209241) is gratefully acknowledged.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yanbo Zhang
    • 1
    • 2
  • Wenrui Wu
    • 1
    • 2
    Email author
  • Xulong Yao
    • 1
    • 2
  • Peng Liang
    • 1
    • 2
  • Lin Sun
    • 1
    • 2
  • Xiangxin Liu
    • 1
    • 2
  1. 1.School of Mining EngineeringNorth China University of Science and TechnologyTangshanChina
  2. 2.Key Laboratory of Mining and Safety Technology of Hebei ProvinceTangshanChina

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