Research on coal and rock recognition model based on improved 1DCNN
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摘要: 随着煤矿智能化建设的加速推进，煤岩高效识别已成为煤炭智能化开采亟待解决的技术难题。针对复杂煤矿地质条件下现有煤岩识别方法精度低、通用性差且难以工程应用等问题，提出了一种基于改进一维卷积神经网络（1DCNN）的煤岩识别模型。以1DCNN为基础，使用多个连续卷积层提取一维振动信号特征，通过全局均值池化（GAP）层代替全连接层，以减少模型训练参数，节省计算资源，同时采用带有线性热启动的余弦退火衰减方法优化学习率，以避免模型训练陷入局部极小值区域，提升训练质量。为直观描述改进1DCNN模型对煤岩截割振动数据的特征提取过程和分类能力，采用t−分布随机近邻嵌入（t−SNE）流形学习算法对模型的特征学习过程进行可视化分析，结果表明，改进1DCNN模型通过逐层特征学习，很好地实现了对煤岩截割状态的识别。以陕西某矿MG650/1590−WD型采煤机截割煤岩时的实测振动数据为样本进行模型训练，结果表明，改进1DCNN模型在训练集上的准确率为99.91%，在测试集上的准确率为99.32%，可直接用于采煤机截割煤岩时的原始振动信号分类，并能够有效识别煤岩截割状态。与传统机器学习、集成学习及未改进的1DCNN模型相比，改进1DCNN模型具有明显优势，平均识别准确率达99.56%，同时大大节约了计算成本，提高了模型识别速度。Abstract: With the acceleration of intelligent construction of coal mines, efficient recognition of coal and rock has become a technical problem to be solved urgently in intelligent coal mining. The existing coal and rock recognition methods under complex coal mine geological conditions have problems of low precision, poor universality and are difficult to apply in engineering. In order to solve the above problems, a coal and rock recognition model based on improved 1-dimensional convolutional neural network (1DCNN) is proposed. Based on the 1DCNN, a plurality of continuous convolution layers are used for extracting one-dimensional vibration signal features. The global average pool (GAP) layer is used for replacing the full connection layer. The model training parameters are reduced, and computing resources are saved. At the same time, a cosine annealing attenuation method with a linear hot start is adopted for optimizing the learning rate. Therefore, the model training is prevented from falling into a local minimum region, and the training quality is improved. In order to intuitively describe the feature extraction process and classification capability of the improved 1DCNN model for coal and rock cutting vibration data, the t-distributed stochastic neighbor embedding (t-SNE) manifold learning algorithm is used to visually analyze the feature learning process of the model. The results show that the improved 1DCNN model can realize the recognition of coal and rock cutting states well through feature learning layer by layer. Based on the measured vibration data obtained in the process of coal and rock cutting of the MG 650/1590-WD shearer in a mine in Shaanxi province, the model is trained and the result shows that the accuracy of the improved 1DCNN model is 99.91% on the training set and 99.32% on the test set. The model can be directly used to classify the original vibration signals of the shearer in coal and rock cutting, and can effectively identify the cutting state of coal and rock. Compared with traditional machine learning, ensemble learning and the unmodified 1DCNN model, the improved 1DCNN model has obvious advantages. The average recognition accuracy rate reaches 99.56%. The calculation cost is greatly saved, and the model recognition speed is improved.
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图 1 改进1DCNN模型结构
Figure 1. Structure of improved 1-dimensional convolutional neural network model
图 3 滑窗法构造的煤岩信号样本
Figure 3. Coal and rock signal samples constructed by sliding window method
图 4 训练集在各网络层的特征学习效果
Figure 4. Feature learning effect of training set at each network layer
图 5 改进1DCNN模型在训练集与测试集上的准确率曲线
Figure 5. Accuracy curves of improved 1-dimensional convolutional neural network model on training set and test set
图 6 改进1DCNN模型在测试集上的混淆矩阵
Figure 6. The confusion matrix of improved 1-dimensional convolutional neural network model on test set
表 1 样本数据集信息
Table 1. Sample dataset information
截割状态 样本长度 训练样本数 测试样本数 标签 割煤 400 6 392 660 0 割岩 400 6 392 660 1 
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表 2 改进1DCNN模型结构
Table 2. Structure of improved 1-dimensional convolutional neural network model
网络层 输出形状 参数量/个 Input(Input Layer) (400，1) 0 Conv1(Conv1D) (400， 64) 256 BN2 (Batch Normalization) (400， 64) 256 Map3 (MaxPooling1D) (200， 64) 0 Conv4 (Conv1D) (100， 128) 24 704 Conv5 (Conv1D) (50， 128) 49 280 Conv6 (Conv1D) (25， 256) 98 560 BN7 (Batch Normalization) (25， 256) 1024 Map8 (MaxPooling1D) (12，256) 0 Conv9 (Conv1D) (6，256) 196 864 GAP10 (GlobalAveragePooling1D) (256，1) 0 Dropout (Dropout) (256，1) 0 Output (Dense) (2，1) 514
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表 3 模型对比实验结果
Table 3. Model comparison experiment results
% 模型 准确率 实验1 实验2 实验3 实验4 实验5 平均值 KNN 93.66 94.50 94.00 93.67 94.50 94.07 RF 76.50 75.50 76.16 75.66 75.83 75.93 SVM 92.83 94.00 94.50 93.00 94.83 93.83 XGBoost 92.66 93.00 93.83 89.50 94.50 92.70 R1DCNN 98.82 99.19 99.03 98.85 98.94 98.97 改进1DCNN 99.32 99.69 99.70 99.55 99.54 99.56
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