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.
|Title of host publication||Artificial Life and Computational Intelligence|
|Publication status||Published (in print/issue) - 27 Dec 2016|
|Name||Lecture Notes in Computer Science book series|
Bibliographical noteFunding information:
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20162220100050), in part by The Leading Human Resource Training Program of Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2016H1D5A1910564), in part by Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2016 (Grants No. C0395147, Grants S2381631), and in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(2016R1D1A3B03931927).