摘要
为了建立液压支架立柱载荷与其动态响应特性之间的映射关系,实现立柱载荷的动态预测,提出一种基于扩展响应特征参量的液压支架立柱载荷预测方法。利用滑模微分器滑模面的高频切换滤除立柱位移和压力信号中的测量噪声,通过导数提取将其动态响应参数扩展成位移、速度、加速度、压力和压力变化率5种响应特征参量;将5种参量输入基于BP神经网络的液压支架立柱载荷预测模型进行立柱载荷预测;搭建了液压支架立柱的AMESim−Simulink联合仿真模型,分析不同程度冲击动载荷作用下立柱的动态响应特性、立柱载荷与其扩展参量间的对应关系及基于扩展多参量的BP神经网络预测模型的性能。仿真结果表明:滑模微分器对立柱的测量位移和压力信号的估计精度分别为98.73%和92.95%;在5次载荷预测测试中,基于扩展多参量的BP神经网络预测模型的平均预测精度为97.2%,平均预测误差为34.39 kN,较基于测量信号的BP神经网络预测模型降低了31.97%。
In order to establish the mapping relationship between the hydraulic support column load and its dynamic response characteristics,and to realize dynamic prediction of the column load,a hydraulic support column load prediction method based on extended response characteristic parameters was proposed.The high-frequency switching of the sliding mode surface of the sliding mode differentiator was used to filter out the measurement noise from the displacement and pressure signals of the column.Through derivative extraction,the dynamic response parameters were extended into five response characteristic parameters:displacement,velocity,acceleration,pressure,and pressure change rate.These five parameters were input into a BP neural network-based hydraulic support column load prediction model to predict the column load.An AMESim–Simulink co-simulation model of the hydraulic support column was built to analyze the dynamic response characteristics of the column under different levels of impact dynamic loads,the correspondence between the column load and its extended characteristic parameters,and the performance of the BP neural network prediction model based on extended multi-parameters.The simulation results showed that the estimation accuracy of the sliding mode differentiator for the measured displacement and pressure signals of the column was 98.73%and 92.95%,respectively.In five load prediction tests,the BP neural network prediction model based on extended multi-parameters had an average prediction accuracy of 97.2%and an average prediction error of 34.39 kN,which was an improvement of 31.97%over the BP neural network prediction model based on measured signals.
作者
王云飞
李冉
赵继云
陈世其
席云熠
WANG Yunfei;LI Ran;ZHAO Jiyun;CHEN Shiqi;XI Yunyi(School of Safety Engineering,China University of Mining and Technology,Xuzhou 221116,China;State Key Laboratory of Coal Mine Disaster Prevention and Control,Xuzhou 221116,China;Xuzhou Construction Machinery Group Co.,Ltd.,Xuzhou 221132,China;School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China)
出处
《工矿自动化》
北大核心
2025年第8期109-116,共8页
Journal of Mine Automation
基金
国家自然科学基金项目(52404268)。
关键词
液压支架立柱
顶板冲击载荷
立柱载荷
滑模微分器
BP神经网络
hydraulic support column
roof impact load
column load
sliding mode differentiator
BP neural network
