A Hybrid Feature Selection Scheme Based on Local Compactness and Global Separability for Improving Roller Bearing Diagnostic Performance

This paper proposes a hybrid feature selection scheme for identifying the most discriminant fault signatures using an improved class separability criteria—the local compactness and global separability (LCGS)—of distribution in feature dimension to diagnose bearing faults. The hybrid model consists of filter based selection and wrapper based selection. In the filter phase, a sequential forward floating selection (SFFS) algorithm is employed to yield a series of suboptimal feature subset candidates using LCGS based feature subset evaluation metric. In the wrapper phase, the most discriminant feature subset is then selected from suboptimal feature subsets based on maximum average classification accuracy estimation of support vector machine (SVM) classifier using them. The effectiveness of the proposed hybrid feature selection method is verified with fault diagnosis application for low speed rolling element bearings under various conditions. Experimental results indicate that the proposed method outperforms the state-of-the-art algorithm when selecting the most discriminate fault feature subset, yielding 1.4% to 17.74% diagnostic performance improvement in average classification accuracy.