Monitoring Rock Failure Processes Using the Hilbert–Huang Transform of Acoustic Emission Signals

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• 作者：Ji Zhang ; Weihong Peng ; Fengyu Liu ; Haixiang Zhang…
• 关键词：Rock fracturing ; Failure process ; Acoustic emission ; Hilbert–Huang transform ; Time–frequency analysis
• 刊名：Rock Mechanics and Rock Engineering
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：49
• 期：2
• 页码：427-442
• 全文大小：3,365 KB
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• 作者单位：Ji Zhang (1)
  Weihong Peng (1)
  Fengyu Liu (1)
  Haixiang Zhang (1)
  Zhijian Li (1)
  
  1. School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, 221116, China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Geophysics and Geodesy
  Civil Engineering
  
• 出版者：Springer Wien
• ISSN：1434-453X

文摘

Rock fracturing generates acoustic emission (AE) signals that have statistical parameters referred to as AE signal parameters (AESP). Identification of rock fracturing or the failure process stage using such data raises several challenges. This study proposes a Hilbert–Huang transform-based AE processing approach to capture the time–frequency characteristics of both AE signals and AESP during rock failure processes. The damage occurring in tested rock specimens can be illustrated through analysis using this method. In this study, the specimens were 25 × 60 × 150 mm3 in size and were compressed at a displacement rate of 0.05 mm/min until failure. The recorded data included force and displacement, AE signals, and AESP. The AESP in the last third of the strain range period and 14 typical moments of strong AE signals were selected for further investigation. These results show that AE signals and AESP can be jointly used for identification of deformation stages. The transition between linear and nonlinear deformation stages was found to last for a short period in this process. The instantaneous frequency of the AE effective energy rate increased linearly from 0.5 to 1.5 Hz. Attenuation of elastic waves spreading in rock samples developed with deformation, as illustrated in the Hilbert spectra of AE signals. This attenuation is frequency dependent. Furthermore, AE signals in the softening process showed a complex frequency distribution attributed to the mechanical properties of the tested specimen. The results indicate that rock failure is predictable. The novel technology applied in this study is feasible for analysis of the entire deformation process, including softening and failure processes. Keywords Rock fracturing Failure process Acoustic emission Hilbert–Huang transform Time–frequency analysis