摘要
为了提高刮板输送机故障诊断准确度,降低事故发生率,分析了刮板输送机常见的故障,研究了基于支持向量机的刮板输送机故障分类,介绍了刮板输送机故障数据处理方法以及基于SVM的故障诊断流程,并对支持向量机参数进行了选择,采用网格搜索交叉法得到模型的最佳参数模型,使用该模型对刮板输送机故障数据进行预测。研究表明,采用支持向量机和网格搜索交叉法相结合的方法,可以对刮板输送机故障进行有效诊断。
Abstract:In order to improve the accuracy of the scraper conveyor diagnosis and reduce the accident rate,this paper analyzed the common faults of the scraper conveyor,studied the fault classification of the scraper conveyor based on the support vector machine,and introduces the fault data processing of the scraper conveyor.The method and the fault diagnosis process based on SVM were selected.The parameters of support vector machine were selected.The optimal parameter model of the model was obtained by grid search cross method.The model was used to predict the fault data of the scraper conveyor.The combination of support vector machine and grid search cross method could effectively diagnose the failure of the scraper conveyor.
作者
庞佳
Pang Jia(Zhaogu No.1 Mine,Jiaozuo Coal Industry(Group),Xinxiang 453000,China)
出处
《能源与环保》
2019年第9期138-140,144,共4页
CHINA ENERGY AND ENVIRONMENTAL PROTECTION
关键词
刮板输送机
故障诊断
支持向量机
网格搜索交叉法
scraper conveyor
fault diagnosis
support vector machine
grid search cross method
作者简介
庞佳(1984—),男,河南温县人,工程师,现从事煤矿机电运输管理工作。
