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.
作者
陈瑞
CHEN Rui(Department of Vehicle Engineering,School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei 230009,China)
出处
《机械强度》
CAS
CSCD
北大核心
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~~
