Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network
Different types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limest...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/846308 |
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| _version_ | 1849400897425113088 |
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| author | Xiangxin Liu Zhengzhao Liang Yanbo Zhang Xianzhen Wu Zhiyi Liao |
| author_facet | Xiangxin Liu Zhengzhao Liang Yanbo Zhang Xianzhen Wu Zhiyi Liao |
| author_sort | Xiangxin Liu |
| collection | DOAJ |
| description | Different types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limestone, and siltstone) were subjected to uniaxial compression until failure, and their AE signals were recorded during their fracturing process. The wavelet transform was used to decompose the AE signals, and the artificial neural network (ANN) was established to recognize the rock types and noise (artificial knock noise and electrical noise). The results show that different rocks had different rupture features and AE characteristics. The wavelet transform provided a powerful method to acquire the basic characteristics of the rock AE and the environmental noises, such as the energy spectrum and the peak frequency, and the ANN was proved to be a good method to recognize AE signals from different types of rocks and the environmental noises. |
| format | Article |
| id | doaj-art-867646fa50b645b9a4e340f3129133ad |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-867646fa50b645b9a4e340f3129133ad2025-08-20T03:37:53ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/846308846308Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural NetworkXiangxin Liu0Zhengzhao Liang1Yanbo Zhang2Xianzhen Wu3Zhiyi Liao4College of Mining Engineering, Hebei United University, Tangshan, Hebei 063009, ChinaSchool of Civil Engineering, Dalian University of Technology, Dalian 116024, ChinaCollege of Mining Engineering, Hebei United University, Tangshan, Hebei 063009, ChinaSchool of Resources and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Civil Engineering, Dalian University of Technology, Dalian 116024, ChinaDifferent types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limestone, and siltstone) were subjected to uniaxial compression until failure, and their AE signals were recorded during their fracturing process. The wavelet transform was used to decompose the AE signals, and the artificial neural network (ANN) was established to recognize the rock types and noise (artificial knock noise and electrical noise). The results show that different rocks had different rupture features and AE characteristics. The wavelet transform provided a powerful method to acquire the basic characteristics of the rock AE and the environmental noises, such as the energy spectrum and the peak frequency, and the ANN was proved to be a good method to recognize AE signals from different types of rocks and the environmental noises.http://dx.doi.org/10.1155/2015/846308 |
| spellingShingle | Xiangxin Liu Zhengzhao Liang Yanbo Zhang Xianzhen Wu Zhiyi Liao Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network Shock and Vibration |
| title | Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network |
| title_full | Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network |
| title_fullStr | Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network |
| title_full_unstemmed | Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network |
| title_short | Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network |
| title_sort | acoustic emission signal recognition of different rocks using wavelet transform and artificial neural network |
| url | http://dx.doi.org/10.1155/2015/846308 |
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