摘要
设计了基于可编程逻辑控制器(PLC)控制系统的皮带运输机故障报警系统,旨在提高煤炭生产过程中皮带运输机的工作效率,并能及时预警故障。该系统采用了改进的EEMD算法,以提高齿轮箱故障特征提取效率,实现更高的输出信号信噪比和较低的信号失真度。同时,该系统引入了机器学习驱动的随机数生成模块,增强了算法的方向性,加速了改进的EEMD算法的收敛循环。通过采用改进的EEMD算法深度挖掘系统数据,并建立皮带运输机故障诊断预警体系,能有效提升皮带运输机故障报警效率,在煤炭生产领域中有着重要的工程应用价值。
A fault alarm system of belt conveyer based on PLC control system is designed to improve the efficiency of belt conveyer in coal production and to warn the fault in time.The improved EEMD algorithm is adopted in this system to improve the efficiency of fault feature extraction,achieve higher signal to noise ratio and lower signal distortion.At the same time,the random number generation module driven by machine learning is introduced to enhance the direction of the algorithm and accelerate the convergence cycle of the improved EEMD algorithm.By using the improved EEMD algorithm to dig the system data deeply and establish the belt conveyor fault diagnosis and early warning system,the fault alarm efficiency of belt conveyor can be effectively improved,which has important engineering application value in the field of coal production.
作者
汤明国
Tang Mingguo(CHN Energy Xinjiang Company Hongshaquan Energy Co.,Ltd.,Xinjiang Qitai,831814,China)
出处
《机械设计与制造工程》
2024年第5期27-30,共4页
Machine Design and Manufacturing Engineering
