Real time prediction technology for load bearing effect of hydraulic support after initial support based on data-driven approach
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摘要: 采煤工作面实际生产中，受顶板条件、采动、液压支架姿态影响，液压支架初撑后立柱压力可能发生变化，进而影响支架初撑后承压效果。液压支架在初撑后出现的承压失效可能导致煤壁片帮、架间冒漏、支架前倾、倒架等问题。目前智采工作面液压支架初撑力调控策略大多是直接判断升柱时立柱压力是否达到额定初撑力，缺乏考虑初撑后立柱压力变化引起的承压效果判断。针对上述问题，提出了一种基于立柱压力数据驱动的液压支架初撑后承压效果即时预测方法。将液压支架初撑后3 min内的立柱压力历史数据状况分为6种典型工况，并根据初撑后承压效果的不同将6种典型工况分为有效承压或失效承压；通过相关性分析，确定了影响支架初撑后承压效果的5个特征因素；对立柱压力样本进行有效承压或失效承压人工标注，并进行特征提取，将特征值分别输入决策树、随机森林、支持向量机、K最近邻（KNN） 4种不同算法建立预测模型，经过对比分析，随机森林模型预测准确率最高，达到95.60%，基本满足模型应用的准确率要求；建立了基于随机森林的液压支架初撑后承压效果即时预测模型，在此基础上开发了液压支架初撑后承压效果即时预测系统，并部署到煤矿现场应用，经过连续25 d的运行，该系统采集到液压支架初撑后3 min内的立柱压力后，可在5 s内输出液压支架初撑后的承压效果，预测结果与实际操作记录对比准确率为82.48%，说明该系统具有较高的承压效果预测准确性。Abstract: In the actual production of coal working face, due to the roof conditions, mining influence, hydraulic support attitude and their mutual influence, the column pressure after the initial support of the hydraulic support may change, thus affecting the load bearing effect after the initial support of the support. The pressure failure of hydraulic supports after initial support may lead to problems such as coal wall lining, inter frame leakage, support leaning forward, and overturning. At present, most of the control strategies for the initial support force of hydraulic supports in intelligent mining working faces directly determine whether the column pressure reaches the rated initial support force when lifting the column. It lacks consideration for the load bearing effect caused by the change in column pressure after initial support. In order to solve the above problems, a real-time prediction method for the load bearing effect of hydraulic support after initial support based on column pressure data-driven approach is proposed. The method divides the historical data of column pressure within 3 minutes after initial support of hydraulic supports into 6 typical working conditions. The method classifies the 6 typical working conditions into effective load bearing or failure load bearing according to the different load bearing effects after initial support. Through correlation analysis, five feature factors affecting the load bearing effect of the support after initial support are identified. The method manually annotates the effective or failed load bearing samples of the column, and extracts features. The method inputs the feature values into four different algorithms: decision tree, random forest, support vector machine, and K-nearest neighbor (KNN) to establish classification models. After comparative analysis, the random forest model has the highest precision, reaching 95.60%, which basically meets the accuracy requirements of the model application. A real-time prediction model for the load bearing effect of hydraulic supports after initial support based on random forest is established. On this basis, a real-time prediction system for the load bearing effect of hydraulic supports after initial support is developed and deployed to coal mine sites. After continuous operation for 25 days, the system collects the column pressure within 3 minutes after the initial support of the hydraulic support and could output the load bearing effect of the hydraulic support within 5 seconds. The accuracy of the prediction results compared with the actual operation records is 82.48%, indicating that the system has high accuracy and stability in predicting the load bearing effect.
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图 1 6种典型工况下液压支架初撑后3 min内的立柱压力曲线
Figure 1. Column pressure curves within 3 minutes after initial support of hydraulic support under 6 typical working conditions
图 2 基于数据驱动的液压支架初撑后承载效果即时预测模型建模流程
Figure 2. Modeling process of real time prediction model for bearing effect of hydraulic support after initial support based on data-driven approach
图 3 54号液压支架初撑后某时间段的立柱压力
Figure 3. Column pressure at some time after initial support of No.54 hydraulic support
图 4 液压支架初撑后承压效果即时预测系统
Figure 4. Real time prediction system for load bearing effect after initial support of hydraulic support
表 1 承压效果样本数量统计
Table 1. Statistics on the number of samples of pressure-bearing effect
样本 样本数量/个 占比/% 失效承压 196 18.60 有效承压 858 81.40 
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表 2 各分类模型准确率对比
Table 2. Comparison of accuracy among different classification models
% 模型 训练集准确率 测试集准确率 决策树 100 94.64 随机森林 99.19 95.60 支持向量机 99.00 92.74 KNN 93.69 91.39
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表 3 支架操作策略汇总
Table 3. Support operating strategy summary
决策建议 策略结论 调控操作1 动作类型：降柱；操作时长：1 s 调控操作2 动作类型：升柱；目标压力：24 MPa；操作时长：5 s 人工检查 检查支架姿态及液压系统状态
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表 4 承压效果预测结果统计
Table 4. Load bearing effect prediction results
承压效果 数量/个 占比/% 失效承压 1 861 40.07 有效承压 2 783 59.93
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表 5 决策建议类型统计
Table 5. The types of recommendations for decision-making
操作建议 数量/个 占比/% 无操作 2 781 59.88 升柱 1 827 39.34 降升 2 0.05 人工检查 34 0.73
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