Abstract: In order to deeply understand the deformation and rupture process of coal samples, the early warning discrimination model of coal rock damage monitoring based on acoustic emission precursor information was established to provide an important basis for mine safety production. By constructing a lightweight three-dimensional convolutional coal rock damage identification model integrating acoustic emission temporal and spatial features, the prediction effect of the identification model for different stages of coal rock damage was studied, and the model's generalization ability was verified. The prediction accuracy of the coal rock damage recognition model was 99.39% in the validation samples of identifying the damage hazard stages of coal samples, and the recall rate of the high-risk samples was also more than 99.20%, which indicated that 3D convolution could effectively captured the coupled spatio-temporal information of the acoustic emission waveforms of coal sample damage. Moreover, the ConDenseNet with SE model could be optimized by knowledge distillation to further reduce the degree of model overfitting and obtain a coal damage recognition model with both performance and accuracy, which verified the superiority of the optimized ConDenseNet with SE model in identifying coal damage and damage warning.
高贤成. 基于ConDenseNet架构煤岩破坏识别模型及其优化研究[J]. 隧道与地下工程灾害防治, 2024, 6(4): 90-98.
GAO Xiancheng. Research on coal damage identification model based on ConDenseNet architecture and its optimization. Hazard Control in Tunnelling and Underground Engineering, 2024, 6(4): 90-98.
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