Investigating a three-dimensional convolution recognition model for acoustic emission signal analysis during uniaxial compression failure of coal
Acoustic emission predicts coal sample failure, vital for early warnings and monitoring. The study emphasizes using acoustic emission to predict coal sample failure patterns and establish a discriminative model for monitoring. It employs a lightweight 3D convolutional model combining DenseNet with G...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2322483 |
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| Summary: | Acoustic emission predicts coal sample failure, vital for early warnings and monitoring. The study emphasizes using acoustic emission to predict coal sample failure patterns and establish a discriminative model for monitoring. It employs a lightweight 3D convolutional model combining DenseNet with Group Convolution (GC) and the "squeeze-and-excitation" (SE) module. Coal samples from Xinglongzhuang Coal Mine underwent uniaxial compression, and a subset of experiments with prominent features was selected. The resulting acoustic emission parameters were then transformed into spatiotemporal image sequences for input to the model. Successfully identifying hazardous coal damage stages, all four network structures (DenseNet, DenseNet + GC, DenseNet + SE, DenseNet + GC + SE) achieved over 98.29% predictive accuracy in verification samples. The model exhibited a recall rate of over 98.02% for high-risk samples, effectively capturing spatiotemporal information. DenseNet + GC + SE showed a probability distribution focusing on different risk levels. By integrating group convolution and SE modules, this model significantly reduced both model and time complexity while preserving precision, enhancing efficiency. It effectively discerns diverse acoustic emission features through group convolution and evaluates their significance using the SE module. Compared to the traditional C3D neural network, this model greatly reduces complexity and time requirements while improving efficiency in distinguishing between various damage stages. |
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| ISSN: | 1947-5705 1947-5713 |