基于K邻近分类模型的车辆减速器健康状态智能监测系统研究与试验

Development and Test of Retarder Health Status Intelligent Monitoring System Based on K-Nearest Neighbor

目前车辆减速器设备的维护维修工作完全依靠现场工作人员定期巡检和经验,存在工作量大、问题处理不及时、故障定位不准确等问题。本文研究基于K邻近分类模型的车辆减速器健康状态智能监测系统,通过设备端的多传感器采集气压变化数据、电磁换向阀电参数、位置表示状态等关键数据,应用K邻近分类模型对数据进行智能分析,研究故障特征。通过车辆减速器健康状态智能监测系统,实现减速器健康状态的识别和故障的判断,能够为现场人员提供故障报警和维修提示,有效辅助现场人员实现“精准维修”。

At present,the maintenance and repair work of hump yard retarders completely depends on the regular inspection and experience of field workers,which has some problems such as heavy workload,untimely problem handling and inaccurate fault location.In this paper,the hump yard retarder health status intelligent monitoring system based on K-Nearest Neighbor is studied.The key data such as air pressure,electrical parameters of the magnetic exchange valve and position representation state are collected by multisensors on the equipment,and the data are intelligently analyzed by K-Nearest Neighbor model,so that the fault characteristics are studied.Through the hump yard retarder health status intelligent monitoring system,the identification of hump yard retarder health status and fault judgment can be realized,which can provide fault alarm and maintenance prompt for field workers and effectively assist them to attain“precise maintenance”.