关键词: 旋转机械, 相关向量机, 故障诊断, 特征消除

Abstract: To solve the problem of rotating machinery fault diagnosis, a method based on correlation vector machine（RVM） is proposed, which is a Bayesian sparse kernel method for regression and classification. The prominent advantage of RVM is the sparseness of the model and the probability of prediction. In order to improve the robustness of RVM and reduce the influence of outliers on the predicted value, and solve the problem that the redundant feature can not be removed when Lasso method is used for feature selection, the RFE recursive feature elimination method using Lasso as the bottom algorithm is proposed to remove the irrelevant and redundant features of the sample data-set. Finally, the data collected in industrial environment is used as a sample set for testing and compared with the traditional algorithm, the results show that the method can improve the detection efficiency and maintain high detection rate.

Key words: rotating machinery, correlation vector machine, fault diagnosis, feature elimination