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Analysis of critical states based on acoustic emission signals during progressive failure of wood.
Jia, Xiaoyan; Li, Junqiu; Zhang, Qinghui; Zhang, Meilin; Jin, Yiting; Ding, Yang.
Afiliação
  • Jia X; Department of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China.
  • Li J; Department of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China.
  • Zhang Q; Department of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China.
  • Zhang M; Department of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China.
  • Jin Y; Department of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China.
  • Ding Y; Department of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China.
PLoS One ; 19(5): e0302528, 2024.
Article em En | MEDLINE | ID: mdl-38753717
ABSTRACT
The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the "collapse" time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system "crash" time for beech and 4 s in advance of the system "crash" time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Madeira / Acústica Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Madeira / Acústica Idioma: En Ano de publicação: 2024 Tipo de documento: Article