Hao Wenchao, Jiang Haiyan, Song Qinghui, Song Qingjun, Sun Shirong
College of Intelligent Equipment, Shandong University of Science and Technology, Taian, Shandong, 271000, China.
Sci Rep. 2024 Dec 5;14(1):30396. doi: 10.1038/s41598-024-80811-6.
Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whale optimization algorithm (IBWO), convolutional neural network, and long short-term memory network (CNN-LSTM) multi-modal fusion model. First, the mutation and memory library mechanisms are introduced into the beluga whale optimization to explore the solution space fully, prevent falling into local optimum, and accelerate the convergence process. Subsequently, the image mapping of the audio signal and vibration signal is performed to extract Mel-Frequency Cepstral Coefficients (MFCC) features, generating rich sample data for CNN-LSTM. Then the multi-head attention mechanism is introduced into CNN-LSTM to speed up the training speed and improve the classification accuracy. Finally, the IBWO-CNN-LSTM coal-gangue recognition model is constructed by the optimal hyperparameter combination obtained by IBWO to realize the automatic recognition of coal-gangue. The benchmark function proves that IBWO is superior to other optimization algorithms. By building an experimental platform for the impact of coal and gangue falling on the tail beam of hydraulic support, multiple experimental data collection is carried out. The experimental results show that the proposed coal-gangue recognition model has better performance than other recognition models, and the accuracy rate reaches 95.238%. The multi-modal fusion strategy helps to improve the accuracy and robustness of coal-gangue recognition.
准确识别煤与矸石是综放工作面高效安全开采的关键保障。本文提出一种基于改进的白鲸优化算法（IBWO）、卷积神经网络和长短期记忆网络（CNN-LSTM）的多模态融合煤矸石识别方法。首先，将变异和记忆库机制引入白鲸优化算法，以充分探索解空间，防止陷入局部最优，并加速收敛过程。随后，对音频信号和振动信号进行图像映射，提取梅尔频率倒谱系数（MFCC）特征，为CNN-LSTM生成丰富的样本数据。然后将多头注意力机制引入CNN-LSTM，以加快训练速度并提高分类准确率。最后，利用IBWO获得的最优超参数组合构建IBWO-CNN-LSTM煤矸石识别模型，实现煤矸石的自动识别。基准函数证明IBWO优于其他优化算法。通过搭建煤矸石撞击液压支架尾梁影响的实验平台，进行多次实验数据采集。实验结果表明，所提出的煤矸石识别模型比其他识别模型具有更好的性能，准确率达到95.238%。多模态融合策略有助于提高煤矸石识别的准确性和鲁棒性。
