ARMA预测算法在汽车驱动桥齿轮故障诊断上的应用  

The Application of ARMA Prediction Algorithms on the Diagnosis of Drive Axle Gear Fault

作　　者：居浩[1] 欧家福[1] 邵毅敏[1] 

机构地区：[1]中国汽车工程研究院股份有限公司,重庆400039

出　　处：《重庆理工大学学报（自然科学）》2012年第2期17-24,39,共9页Journal of Chongqing University of Technology：Natural Science

基　　金：重庆市自然科学基金重点项目(CSTC2007BA6021)

摘　　要：基于齿轮啮合理论和试验数据,提出了汽车驱动桥早期故障诊断的分析方法。采用ARMA模型对齿轮振动及故障趋势进行了预测分析,设计了基于ARMA预测算法的汽车动力传动系统监测软件,并进行了试验验证。试验和分析结果表明,ARMA模型预测算法对于汽车驱动桥台架试验的早期失效诊断切实可行且应用效果良好。Based on the co-gear meshing theory and experimental method for the diagnosis of the drive axle fault in the early stage data, the paper proposed analyzing Gear vibration and failure trends were forecasted and analyzed by ARMA model. Automobile powertrain monitoring software was de- signed based on ARMA prediction algorithm, and experimental verification was conducted. Test and analysis results show that the ARMA model prediction algorithm has practicable and effective results in the early failure diagnosis of the drive axle bench experiment.

关 键 词：ARMA 驱动桥 故障诊断 残余寿命 

分 类 号：U467[机械工程—车辆工程]