关键词: 故障诊断, 相关向量机, 决策树

Abstract: In view of the problems in fault diagnosis, such as small samples, nonlinear, multiple faults processing, and the defects of traditional intelligent methods, an intelligent fault diagnosis method based on Decision Tree（DT） and Relevance Vector Machine（RVM） is proposed. The DT is constructed, and the multi-class classification problem is divided into many binary classification problems. RVM is used to make binary classification at every node, and then the multi-class classification of RVM is achieved. The theoretical analysis and results of application show that the proposed method has better performance in sparsity and diagnosis efficiency while keeping high accuracy compared with the traditional SVM methods, which makes it more practical; and that the proposed method has a better training efficiency compared with OAR-RVM and OAO-RVM.

Key words: fault diagnosis, Relevance Vector Machine（RVM）, Decision Tree（DT）