A Two-Stage Feature Selection for Rolling Bearing Fault Diagnosis Using Relieff and SVM-RFE

Selecting distinguishable features from small sample data of rolling bearings has become an emerging and trending research topic in recent years. In this paper, a two-stage feature selection method based on Euclidean distance and multi-class support vector machine is proposed to remove irrelevant features and redundant features. First, the fault data are formed to a multidomain feature set. Second, the ReliefF algorithm based on Euclidean distance is used to remove irrelevant features. Third, the support vector machine recursive feature elimination (SVM-RFE) is used to remove redundant features. Lastly, the KNN classifier is used to verify the effectiveness of this method. This paper uses the rolling bearing public datasets. After comparison, the results prove that the method proposed in the article is better.