基于支持向量机的矿山机电设备故障智能化检测方法

Intelligent Fault Detection Method of Mine Electromechanical Equipment Based on Support Vector Machine

常规的矿山机电设备故障智能化检测特征提取通常是定点式检测,检测效率较低、漏报率高,为此,提出基于支持向量机的矿山机电设备故障智能化检测方法。进行智能化辅助检测节点的部署,采用层级的方式提取机电设备的故障层级特征,提升整体的检测效率。构建支持向量机机电设备故障智能化检测模型,采用自适应修正的方式保证检测结果。结果表明,针对选定测试区域上关联的机电设备,结合3组故障测试小组,最终得出的故障检测的漏报率均控制在2.4%以下,说明该方法更灵活、多变,针对性较强,具有实际的应用价值。

The conventional intelligent detection feature extraction for mining electromechanical equipment faults is usually fixed-point detection,which has low detection efficiency and high false alarm rate.Therefore,a support vector machine based intelligent detection method for mining electromechanical equipment faults is proposed.Deploy intelligent auxiliary detection nodes,extract fault level features of electromechanical equipment in a hierarchical manner,and improve overall detection efficiency.Build an intelligent detection model for mechanical and electrical equipment faults using support vector machines,and ensure the detection results through adaptive correction.The results showed that for the associated electromechanical equipment in the selected testing area,combined with three sets of fault testing teams,the final missed alarm rate of fault detection was controlled below 2.4%,indicating that this method is more flexible,versatile,targeted,and has practical application value.