基于改进KFDA独立特征选择的故障诊断  

FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION

作　　者：陈瑞 CHEN Rui(Department of Vehicle Engineering,School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学汽车与交通工程学院车辆工程系,合肥230009

出　　处：《机械强度》2019年第3期527-531,共5页Journal of Mechanical Strength

基　　金：国家重点研发计划新能源汽车专项(SQ2017ZY020013);安徽省科技重大专项(16030901030)资助~~

摘　　要：为了有效利用故障特征集中对故障敏感的特征进行故障诊断,对核Fishier判别分析(KFDA)进行改进,提出基于改进KFDA独立特征选择的故障诊断方法。该方法首先从多个角度提取故障振动信号的故障特征,构建原始高维多域混合故障特征集。然后,采用改进的核Fisher特征选择方法为每两类故障状态独立选择敏感特征集。最后,采用"一对一"的方法训练多个二分类相关向量机(RVM),将得到的敏感特征集输入多分类故障诊断模型进行识别。齿轮故障诊断实例表明,所提方法具备较高的诊断准确率。In order to diagnose fault effectively by using sensitive features contained in the feature set,KFDA was improved in this paper and a fault diagnosis method based on improved KFDA individual feature selection was proposed.Firstly,the mixed feature of the fault vibration signal was extracted from different angels,and the original high-dimensional and multi-domain feature set was constructed.Then,an improved kernel Fisher feature selection method was proposed and used to select individual sensitive feature subset for each pair of class.Finally,a one-against-one approach was applied to train several relevance vector machine(RVM) binary classifiers,and sensitive feature was input into the multi-class fault diagnosis model for recognizing the fault types.The experimental results of gear indicate that the proposed method is of high diagnostic accuracy.

关 键 词：KFDA 独立特征选择 故障诊断 齿轮 

分 类 号：TH165.3[机械工程—机械制造及自动化]