Autoregressive Modeling preprocessing with SVM Optimization based on BFA for Bearing Fault Diagnosis

Abstract

As an effective tool in pattern recognition and machine learning, support vector machine (SVM) has been adopted abroad. In developing a successful SVM classifier, extracting feature is very important. This paper proposes the application of Autoregressive Modeling to SVM for feature extraction. According to the fact that parameter selection of support vector machine(SVM) for fault diagnosis is difficult, a new method based on bacterial foraging algorithm(BFA) for support vector machine parameter optimization was proposed , then the faster optimization of the parameters ”c” and kernel parameter ”σ” was performed. The results have shown feasibility and effectiveness of the proposed approach.