Intelligent prediction for face straightness based on sensor data and human operation information
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摘要:
目前综采工作面直线度调控采用基于工作面推移行程等传感器数据和人工观测调控相结合的方式，但存在传感器数据和人工调控信息得不到有效利用的问题。针对上述问题，提出了融合传感器数据和人工调控信息的工作面直线度智能预测方法。对支架推移油缸行程、支架立柱压力、采煤机位置等进行数据清洗，并按照正常推溜调控距离和调整推溜调控距离进行分类，构建由工作面正常推溜调控距离矩阵和累计推溜调控距离矩阵组成的工作面直线度分析矩阵；通过特征工程对工作面直线度分析矩阵进行特征提取，生成特征矩阵作为样本，将人工调控距离对应的工况类型作为样本标签；采用机器学习分类算法建立工作面直线度预测模型。实验结果表明，采用随机森林算法搭建的工作面直线度预测模型准确率最高，为91.41%。将该模型应用于高河煤矿2312工作面，结果表明，在运行30 d、115次割煤循环的工作面直线度预测过程中，该模型预测准确率达81.4%。
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关键词:
- 工作面直线度 /
- 推移行程 /
- 传感器数据 /
- 人工调控信息 /
- 工作面直线度分析矩阵
Abstract:Currently, the control of face straightness in fully mechanized mining faces combines sensor data such as advancing stroke with manual observations. However, an issue has been identified where sensor data and human operation information are not effectively utilized. To address this problem, an intelligent prediction method for face straightness that integrates sensor data and human operation information was proposed. The support advancing cylinder stroke data, support column pressure data and shearer position data were cleaned, and classified according to the normal advancing stroke control distance and the adjusted advancing stroke control distance. A face straightness analysis matrix was constructed, consisting of the normal advancing stroke control distance matrix and the accumulated advancing stroke control distance matrix. Through feature engineering, feature extraction was carried out on the straightness analysis matrix of the working face, and the feature matrix was generated as a sample, with the working condition type corresponding to the manual control distance to serve as sample labels. The experimental results show that the accuracy of the working face straightness prediction model built by random forest algorithm is the highest, which was 91.41%. A machine learning classification algorithm was employed to establish a prediction model for the face straightness of the current mining cycle. This prediction model was applied to the 2312 working faces at the Gaohe coal mine. The results indicated that during the 30-day period and 115 cutting cycles of the face straightness prediction, achieving an accuracy rate of 81.4%.
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图 1 融合传感器数据和人工调控信息的工作面直线度智能预测流程
Figure 1. Process of intelligent prediction for face straightness based on sensor data and human operation information
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图 4 不同预测模型混淆矩阵对比
Figure 4. Comparison of confusion matrices for different prediction models
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图 5 融合传感器数据和人工调控信息的工作面直线度智能预测部署架构
Figure 5. Deployment architecture of intelligent prediction for face straightness based on sensor data and human operation information
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图 6 工作面直线度预测结果与现场结果对比
Figure 6. Comparison between face straightness prediction results and actual measurement results
表 1 直线度调整操作支架数
Table 1 Number of supports for straightness adjustment operating
操作支架数/台 1 2 3 4 ＞5 操作次数 10 191 101 47 103 
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表 2 样本统计结果
Table 2 Statistics result of samples
样本标签 样本数/个 直线度良好（C≤10 mm） 928 调控100 mm（10 mm＜C＜150 mm） 506 调控200 mm（150 mm≤C＜300 mm） 124 调控300 mm（C≥300 mm） 68
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表 3 模型准确率
Table 3 Model accuracy rate
算法 LR KNN SVM NB DT RF 准确率/% 57.97 89.26 85.89 73.00 80.67 91.41
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