基于BP神经网络模型的带式输送机张紧力波动规律分析  

Analysis of the Tension Fluctuation Law of Belt Conveyors Based on the BP Neural Network Model

作　　者：林广旭 LIN Guang-xu(CCTEG Taiyuan Research Institute Co.,Ltd.,Taiyuan Shanxi 030032,China;Shanxi Tiandi Coal Mining Machinery Co.,Ltd.,Taiyuan Shanxi 030032,China;China National Engineering Laboratory for Coal Mining Machinery,Taiyuan Shanxi 030032,China)

机构地区：[1]中国煤炭科工集团太原研究院有限公司,山西太原030032 [2]山西天地煤机装备有限公司,山西太原030032 [3]煤矿采掘机械装备国家工程实验室,山西太原030032

出　　处：《机电产品开发与创新》2025年第2期121-124,共4页Development & Innovation of Machinery & Electrical Products

摘　　要：回顾了带式输送机的结构特点和应用工况,分析了工作过程中负载和张紧力波动之间的关系,阐述了应用BP神经网络算法分析张紧力波动规律方法,建立了带式输送机BP神经网络模型,通过数据采集器采集驱动电机电流数据进行神经网络训练,预测出负载波动电流数据,计算出张紧力波动误差和需求值,通过张紧装置进行自适应控制。实验结果表明,预测波动精度达到91.9%,响应时间为2 s,符合带式输送机负载变化到自动张紧响应时间和精度要求,为设备智能化生产、增加皮带寿命、降低生产能耗打下了基础。The structure characteristics and application conditions of belt conveyors were reviewed,and the relationship between load and tension force fluctuations during operation was analyzed.method for analyzing the tension force fluctuation law using BP neural network algorithm was elucidated.A BP neural network model of the belt conveyor was established,and the motor current data was collected using a data collector for neural network training.The load fluctuation current data was predicted,and the tension force fluctuation error and required were calculated,and adaptive control was performed through the tensioning device.The experimental results show that the prediction accuracy of the fluctuations reaches 91.9%,the response time is 2 s,which meets the requirements of the belt conveyor for load changes to automatic tensioning response time and accuracy,and provides a basis intelligent production of equipment,increasing belt life,and reducing production energy consumption.

关 键 词：带式输送机 BP神经网络 张紧力 自适应控制 波动规律 

分 类 号：TD421[矿业工程—矿山机电]