Combined flow graphs and normal naive Bayesian classifier for fault diagnosis of gear box

Abstract

In order to improve the intuition, efficiency, and accuracy of fault diagnosis of gear box, a novel fault diagnosis method based on flow graphs and normal naive Bayesian classifier is proposed in this paper. In the proposed method, flow graphs are utilized to represent the relationship between fault symptoms and gear conditions. The algorithm of layer reduction is employed to eliminate the redundant and irrelevant attribute layers to obtain the minimal flow graph for reducing the number of input nodes in normal naive Bayesian classifier. The normal naive Bayesian classifier is constructed according to the minimal flow graph to obtain classification results. To verify the proposed method, an experiment is carried out to apply this method to a gear box rig. The experiment results demonstrate that the proposed method combining the advantages of flow graphs and normal naive Bayesian classifier provides a new way to design high-performance models for fault diagnosis of gear box.