中文摘要：

本文中提出基于支持向量机的汽车自动变速器故障识别方法。首先利用设计的虚拟测试系统采集自动变速器3种状态下的运行数据,再对数据进行整理和筛选,提取合适的数据作为训练样本,然后设计基于支持向量机的多值分类器进行故障识别,最后与基于BP人工神经网络的诊断方法进行对比。结果表明,基于支持向量机的故障识别方法具有更快的收敛速度和更强的分类能力,适用于汽车自动变速器实时故障识别和诊断。

英文摘要：

A fault identification technique for vehicle automatic transmission based on support vector machine（SVM） is presented in this paper.Firstly,a virtual testing system designed is used to collect,process and screen the operation data of an automatic transmission under three statuses with appropriate data extracted as training samples.Then,a SVM-based multi-class classifier is designed to identify faults.Finally,the outcomes are compared with that using BP neural-network diagnostics.The results show that the fault identification based on SVM converges faster with stronger classification ability,suitable for real-time fault identification of vehicle automatic transmission.