Intelligent Fault Diagnosis Based on a Hybrid Multi-Class Support Vector Machines and Case-Based Reasoning Approach
Sangwon Lee, Kwang-Kyu Seo · Journal of Computational and Theoretical Nanoscience · 2013
This paper presents a hybrid multi-class support vector machines (SVMs) and case-based reasoning approach for intelligent fault diagnosis. The multi-class classification method usually has a main shortcoming that the binary classifiers used are obtained by training on different binary classification problems, and thus it is unclear whether their real-valued outputs are on comparable scales. In this paper, we try to use additional information, relative outputs of the machines, for final decision. We propose the hybrid approach base on combining multi-class SVMs and case-based reasoning. Case-based reasoning applies with reject option to use the information in order to verify the effectiveness of the proposed approach. The experimental results with real multi-class fault data of rolling bearings show that the proposed approach is useful to improve the fault diagnosis performance.