摘要
针对矿井提升机故障诊断容易、预警难的问题,提出基于大数据的提升机故障预警平行系统架构。首先分析提升机各子系统结构并建立提升机故障预警平行仿真系统;其次在虚拟机上建立提升机Hadoop生态系统,运用聚类算法结合关联规则算法对海量数据进行数据挖掘;最后提出了提升机安全状态评价规则,并根据故障预警结果做出系统决策。经实验验证能够达到故障预测的目的。
Aiming at the problem that mine hoist fault diagnosis is easy and early warning is difficult,a parallel system architecture of hoist fault early warning based on big data was proposed.Firstly analyzed the structure of each subsystem of the hoist and established a parallel simulation system of early warning.Secondly established the hoist Hadoop ecosystem on the virtual machine and used clustering algorithm combined with association rule algorithm to perform data mining on massive data.Finally the hoist safety status evaluation rules were proposed and system decisions were made based on the fault warning results.It has been verified by experiments to achieve the purpose of fault prediction.
作者
刘宗胜
赵四海
张聪
张钰彦
李丹
Liu Zongsheng;Zhao Sihai;Zhang Cong;Zhang Yuyan;Li Dan(School of Mechatronics and Information Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China)
出处
《煤矿机械》
2023年第2期165-167,共3页
Coal Mine Machinery
