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采煤机滚筒载荷特性研究与预测

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为研究采煤机滚筒载荷特性,并实现载荷预测,以山西高河矿E2312工作面为工程对象,利用离散元-多体动力学(DEM-MBD)双向耦合技术,构建截割耦合模型.数值模拟与工程试验的平均扭矩误差较小,证明通过双向耦合技术研究滚筒载荷的可靠性;利用希尔伯特-黄变换(HHT),分析各载荷的时频特性,研究不同运动参数下总载荷时频特性;基于长短期记忆(LSTM),以数值模拟的载荷为输入样本,实现载荷预测.结果表明:采用DEM-MBD-HHT-LSTM模型,可实现载荷特性研究和预测.通过时频特性分析发现,滚筒不同运动状态下各载荷具有辨识性和关联性;采用LSTM算法预测载荷的准确度由高到低依次为总载荷、截割载荷、牵引载荷、侧向载荷.研究结果可为采煤机自适应调速策略提供参考.
Drum Load Characteristics and Load Prediction of Coal Mining Machine
In order to study the drum load characteristics of coal mining machine and realize the load prediction,the cut-off coupling model was constructed using the two-way coupling technique of discrete element-multibody dynamics(DEM-MBD)with the E2312 working face of Shanxi Gaohe Mine as the engineering object.The average torque error between numerical simulation and engineering experiment is small,which proves that the study of roller load by two-way coupling technique is reliable.Using Hilbert-Huang transform(HHT),the time-frequency characteristics of each load were analyzed,and the time-frequency characteristics of the total load were studied under different motion parameters.Based on the long-short-term memory(LSTM),the load from numerical simulation was used as the input sample to realize the load prediction.The results show that the load characteristics and load prediction can be realized using the DEM-MBD-HHT-LSTM model.Through the time-frequency characterization,it is found that each load under different motion states of the drum is discriminative and correlative.The accuracy of load prediction using the LSTM algorithm from high to low is total load,cutoff load,traction load,and lateral load.The study can provide a reference for the adaptive speed regulation strategy of coal mining machine.

Coal mining machineDrum loadLoad predictionDEM-MBD-HHT-LSTM modelTime-frequency characteristic

王宏伟、郭军军、梁威、耿毅德、陶磊、李进

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太原理工大学山西省煤矿智能装备工程研究中心,山西太原 030024

山西焦煤集团有限责任公司博士后工作站,山西太原 030024

太原理工大学机械与运载工程学院,山西太原 030024

采煤机 滚筒载荷 载荷预测 DEM-MBD-HHT-LSTM模型 时频特性

山西省揭榜招标项目山西省基础研究计划项目

20201101005202203021222082

2024

矿业研究与开发
长沙矿山研究院有限责任公司 中国有色金属学会

矿业研究与开发

CSTPCD北大核心
影响因子:0.763
ISSN:1005-2763
年,卷(期):2024.44(9)