Roller Element Bearing of Mine Ventilating Fan with Fault Diagnosis Based on Mechanics Properties and RBF Neural Network

Abstract

Roller element bearing is an important part of mine ventilating fan. The management and maintenance of the equipment is very important. Therefore, it is necessary to employ fault diagnosis process to the roller element bearing. In this paper, mechanics properties of roller element bearing are analyzed. Then, Radial Basis Function (RBF) neural network is used for the fault diagnosis of the roller element bearing. The structure and inference of RBF network are discussed in detail. The roller element bearing fault diagnosis model is established based on RBF network. A case study is given. The proposed method is applied to the fault diagnosis of roller element bearing. The result shows that the proposed method can improve efficiency of the fault diagnosis.