内燃机车铁谱分析中大磨粒浓度的定量分析与智能监测

在内燃机车油液监测过程中,对于不同的机车个体,润滑油中大磨粒浓度的正常与否所对应界值是不确定的。针对这种情况,结合一元线性回归的思想,本论文提出了利用Visual Basic语言进行了大磨粒浓度(DL)磨损状态自动判定系统的开发。对于某一个特定的机车个体,用户首先在该系统中输入一组列车正常运行的"走行公里(km)-浓度(ppm)"样本数据,该系统便可自动进行一元线性回归计算,并生成所对应的一元线性回归方程和标准判定方程,在这种前提条件下,用户只要输入待测的样本数据,并观察它和两条直线的位置关系,便可判定该状态点所对应的大磨粒浓度的正常与否,既提高了大磨粒浓度判定的效率和准确性,又提高了监测人员的工作。

Quantitative analysis and intelligent monitoring of big wear particles concentration in ferrography analysis for diesel locomotive

in the course of diesel locomotive lubricant oil monitoring,for different locomotive,the critical value that is used to judge the big wear particles concentration is different.In view of this situation,combined with linear regression thinking,this paper proposed to develop a automatic diagnosis system of big wear particles concentration with Visual Basic language.with regard to a particular diesel locomotive,as long as the user inputs some sample data（kilometer（km）-concentration（ppm）） corresponding to a normally running diesel locomotive,the system will automatically do a linear regression calculation,after that,the system also offers the line graph of linear regression equation and criterion judgment equation,under this precondition,what the user should do is to input other sample data that need to be measured,then observes the location of the point determined by the sample data and analyzes whether the wear particle concentration is normal or abnormal.This will improve the efficiency and accuracy of the big wear particles concentration diagnosis methods,meanwhile,it also enhances the monitoring staff ′work.