Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform Analysis
Identifying the different damage modes of wood is of great significance for monitoring the occurrence, development, and evolution of wood material damage. This paper presents the research results of the application of acoustic emission (AE) technology to analyze and evaluate the mapping relationship...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/4/631 |
| _version_ | 1797504784792551424 |
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| author | Juncheng Tu Lichuan Yu Jian Zhao Jianzhong Zhang Dong Zhao |
| author_facet | Juncheng Tu Lichuan Yu Jian Zhao Jianzhong Zhang Dong Zhao |
| author_sort | Juncheng Tu |
| collection | DOAJ |
| description | Identifying the different damage modes of wood is of great significance for monitoring the occurrence, development, and evolution of wood material damage. This paper presents the research results of the application of acoustic emission (AE) technology to analyze and evaluate the mapping relationship between the damage pattern of wood in the fracture process and the AE signal. For the three-point bending specimen with pre-crack, the double cantilever beam specimen, the single fiber tensile specimen, and the uniaxial compression specimen, the bending tensile compression test and the AE monitoring were performed, respectively. After the post-processing and analysis of the recorded AE signal, the results show that the peak frequency of AE is an effective parameter for identifying different damage modes of wood. In this study, the empirical mode decomposition (EMD) of the AE signal can separate and extract a variety of damage modes contained in the AE signal. The Hilbert–Huang transform (HHT) of the AE signal can clearly describe the frequency distribution of intrinsic mode function (IMF) components in different damage stages on the time scale, and can calculate instantaneous energy accurately, which provides a basis for damage mode recognition and lays a foundation for further accurate evaluation of the wood damage process. |
| first_indexed | 2024-03-10T04:09:17Z |
| format | Article |
| id | doaj.art-7ea352928fcc4720a62fb1310414de4e |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T04:09:17Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-7ea352928fcc4720a62fb1310414de4e2023-11-23T08:15:09ZengMDPI AGForests1999-49072022-04-0113463110.3390/f13040631Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform AnalysisJuncheng Tu0Lichuan Yu1Jian Zhao2Jianzhong Zhang3Dong Zhao4School of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaIdentifying the different damage modes of wood is of great significance for monitoring the occurrence, development, and evolution of wood material damage. This paper presents the research results of the application of acoustic emission (AE) technology to analyze and evaluate the mapping relationship between the damage pattern of wood in the fracture process and the AE signal. For the three-point bending specimen with pre-crack, the double cantilever beam specimen, the single fiber tensile specimen, and the uniaxial compression specimen, the bending tensile compression test and the AE monitoring were performed, respectively. After the post-processing and analysis of the recorded AE signal, the results show that the peak frequency of AE is an effective parameter for identifying different damage modes of wood. In this study, the empirical mode decomposition (EMD) of the AE signal can separate and extract a variety of damage modes contained in the AE signal. The Hilbert–Huang transform (HHT) of the AE signal can clearly describe the frequency distribution of intrinsic mode function (IMF) components in different damage stages on the time scale, and can calculate instantaneous energy accurately, which provides a basis for damage mode recognition and lays a foundation for further accurate evaluation of the wood damage process.https://www.mdpi.com/1999-4907/13/4/631non-destructiveAE technique (AET)EMDHHTwood materialdamage pattern recognition |
| spellingShingle | Juncheng Tu Lichuan Yu Jian Zhao Jianzhong Zhang Dong Zhao Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform Analysis Forests non-destructive AE technique (AET) EMD HHT wood material damage pattern recognition |
| title | Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform Analysis |
| title_full | Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform Analysis |
| title_fullStr | Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform Analysis |
| title_full_unstemmed | Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform Analysis |
| title_short | Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform Analysis |
| title_sort | damage modes recognition of wood based on acoustic emission technique and hilbert huang transform analysis |
| topic | non-destructive AE technique (AET) EMD HHT wood material damage pattern recognition |
| url | https://www.mdpi.com/1999-4907/13/4/631 |
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