Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM
Acoustic emission (AE) signals produced by different types of rocks have different characteristics of information. Determining the brittle mineral content of rock according to the acoustic emission characteristics of rock is helpful to understand the mechanical behavior of rock in field monitoring....
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/3717867 |
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| author | Weihua Wang |
| author_facet | Weihua Wang |
| author_sort | Weihua Wang |
| collection | DOAJ |
| description | Acoustic emission (AE) signals produced by different types of rocks have different characteristics of information. Determining the brittle mineral content of rock according to the acoustic emission characteristics of rock is helpful to understand the mechanical behavior of rock in field monitoring. This article constructs a deep learning algorithm model to identify acoustic emission signals released from rock fractures with different brittle mineral contents. In response to the interference characteristics of acoustic emission signal data, a multiscale one-dimensional convolutional neural network embedded with efficient channel attention (ECA) module was incorporated into the model, and multiscale convolutional kernels were used to extract features of different levels of precision. In the latter half of the model, the BLSTM network was incorporated to extract time series-related features, local spatial uncorrelated features, and weak periodic pattern features from the acoustic emission signal data. To solve the problem that the recognition accuracy of minority samples decreases, this study replaces ReLU activation function with SELU. The results show that the multiscale 1DCNN-BLSTM model embedded in ECA module has a good antinoise performance, and the recognition accuracy can reach over 90%. The discovery of this work provides a new idea for exploring the mechanism of rock mass instability. |
| format | Article |
| id | doaj-art-d70ab0d4387b444f92d9ce09697f60f3 |
| institution | DOAJ |
| issn | 2090-0155 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-d70ab0d4387b444f92d9ce09697f60f32025-08-20T03:19:47ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/3717867Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTMWeihua Wang0School of Intelligent Equipment EngineeringAcoustic emission (AE) signals produced by different types of rocks have different characteristics of information. Determining the brittle mineral content of rock according to the acoustic emission characteristics of rock is helpful to understand the mechanical behavior of rock in field monitoring. This article constructs a deep learning algorithm model to identify acoustic emission signals released from rock fractures with different brittle mineral contents. In response to the interference characteristics of acoustic emission signal data, a multiscale one-dimensional convolutional neural network embedded with efficient channel attention (ECA) module was incorporated into the model, and multiscale convolutional kernels were used to extract features of different levels of precision. In the latter half of the model, the BLSTM network was incorporated to extract time series-related features, local spatial uncorrelated features, and weak periodic pattern features from the acoustic emission signal data. To solve the problem that the recognition accuracy of minority samples decreases, this study replaces ReLU activation function with SELU. The results show that the multiscale 1DCNN-BLSTM model embedded in ECA module has a good antinoise performance, and the recognition accuracy can reach over 90%. The discovery of this work provides a new idea for exploring the mechanism of rock mass instability.http://dx.doi.org/10.1155/2024/3717867 |
| spellingShingle | Weihua Wang Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM Journal of Electrical and Computer Engineering |
| title | Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM |
| title_full | Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM |
| title_fullStr | Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM |
| title_full_unstemmed | Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM |
| title_short | Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM |
| title_sort | recognition algorithm of ae signal of rock fracture based on multiscale 1dcnn blstm |
| url | http://dx.doi.org/10.1155/2024/3717867 |
| work_keys_str_mv | AT weihuawang recognitionalgorithmofaesignalofrockfracturebasedonmultiscale1dcnnblstm |